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How We Use Abstract Thinking

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

MoMo Productions / Getty Images

• How It Develops

Abstract thinking, also known as abstract reasoning, involves the ability to understand and think about complex concepts that, while real, are not tied to concrete experiences, objects, people, or situations.

Abstract thinking is considered a type of higher-order thinking, usually about ideas and principles that are often symbolic or hypothetical. This type of thinking is more complex than the type of thinking that is centered on memorizing and recalling information and facts.

Examples of Abstract Thinking

Examples of abstract concepts include ideas such as:

• Imagination

While these things are real, they aren't concrete, physical things that people can experience directly via their traditional senses.

You likely encounter examples of abstract thinking every day. Stand-up comedians use abstract thinking when they observe absurd or illogical behavior in our world and come up with theories as to why people act the way they do.

You use abstract thinking when you're in a philosophy class or when you're contemplating what would be the most ethical way to conduct your business. If you write a poem or an essay, you're also using abstract thinking.

With all of these examples, concepts that are theoretical and intangible are being translated into a joke, a decision, or a piece of art. (You'll notice that creativity and abstract thinking go hand in hand.)

Abstract Thinking vs. Concrete Thinking

One way of understanding abstract thinking is to compare it with concrete thinking. Concrete thinking, also called concrete reasoning, is tied to specific experiences or objects that can be observed directly.

Research suggests that concrete thinkers tend to focus more on the procedures involved in how a task should be performed, while abstract thinkers are more focused on the reasons why a task should be performed.

It is important to remember that you need both concrete and abstract thinking skills to solve problems in day-to-day life. In many cases, you utilize aspects of both types of thinking to come up with solutions.

Other Types of Thinking

Depending on the type of problem we face, we draw from a number of different styles of thinking, such as:

• Creative thinking : This involves coming up with new ideas, or using existing ideas or objects to come up with a solution or create something new.
• Convergent thinking : Often called linear thinking, this is when a person follows a logical set of steps to select the best solution from already-formulated ideas.
• Critical thinking : This is a type of thinking in which a person tests solutions and analyzes any potential drawbacks.
• Divergent thinking : Often called lateral thinking, this style involves using new thoughts or ideas that are outside of the norm in order to solve problems.

How Abstract Thinking Develops

While abstract thinking is an essential skill, it isn’t something that people are born with. Instead, this cognitive ability develops throughout the course of childhood as children gain new abilities, knowledge, and experiences.

The psychologist Jean Piaget described a theory of cognitive development that outlined this process from birth through adolescence and early adulthood. According to his theory, children go through four distinct stages of intellectual development:

• Sensorimotor stage : During this early period, children's knowledge is derived primarily from their senses.
• Preoperational stage : At this point, children develop the ability to think symbolically.
• Concrete operational stage : At this stage, kids become more logical but their understanding of the world tends to be very concrete.
• Formal operational stage : The ability to reason about concrete information continues to grow during this period, but abstract thinking skills also emerge.

This period of cognitive development when abstract thinking becomes more apparent typically begins around age 12. It is at this age that children become more skilled at thinking about things from the perspective of another person. They are also better able to mentally manipulate abstract ideas as well as notice patterns and relationships between these concepts.

Uses of Abstract Thinking

Abstract thinking is a skill that is essential for the ability to think critically and solve problems. This type of thinking is also related to what is known as fluid intelligence , or the ability to reason and solve problems in unique ways.

Fluid intelligence involves thinking abstractly about problems without relying solely on existing knowledge.

Abstract thinking is used in a number of ways in different aspects of your daily life. Some examples of times you might use this type of thinking:

• When you describe something with a metaphor
• When you talk about something figuratively
• When you come up with creative solutions to a problem
• When you analyze a situation
• When you notice relationships or patterns
• When you form a theory about why something happens
• When you think about a problem from another point of view

Research also suggests that abstract thinking plays a role in the actions people take. Abstract thinkers have been found to be more likely to engage in risky behaviors, where concrete thinkers are more likely to avoid risks.

Impact of Abstract Thinking

People who have strong abstract thinking skills tend to score well on intelligence tests. Because this type of thinking is associated with creativity, abstract thinkers also tend to excel in areas that require creativity such as art, writing, and other areas that benefit from divergent thinking abilities.

Abstract thinking can have both positive and negative effects. It can be used as a tool to promote innovative problem-solving, but it can also lead to problems in some cases:

• Bias : Research also suggests that it can sometimes promote different types of bias . As people seek to understand events, abstract thinking can sometimes cause people to seek out patterns, themes, and relationships that may not exist.
• Catastrophic thinking : Sometimes these inferences, imagined scenarios, and predictions about the future can lead to feelings of fear and anxiety. Instead of making realistic predictions, people may catastrophize and imagine the worst possible potential outcomes.
• Anxiety and depression : Research has also found that abstract thinking styles are sometimes associated with worry and rumination . This thinking style is also associated with a range of conditions including depression , anxiety, and post-traumatic stress disorder (PTSD) .

Conditions That Impact Abstract Thinking

The presence of learning disabilities and mental health conditions can affect abstract thinking abilities. Conditions that are linked to impaired abstract thinking skills include:

• Learning disabilities
• Schizophrenia
• Traumatic brain injury (TBI)

The natural aging process can also have an impact on abstract thinking skills. Research suggests that the thinking skills associated with fluid intelligence peak around the ages of 30 or 40 and begin to decline with age.

Tips for Reasoning Abstractly

While some psychologists believe that abstract thinking skills are a natural product of normal development, others suggest that these abilities are influenced by genetics, culture, and experiences. Some people may come by these skills naturally, but you can also strengthen these abilities with practice.

Some strategies that you might use to help improve your abstract thinking skills:

• Think about why and not just how : Abstract thinkers tend to focus on the meaning of events or on hypothetical outcomes. Instead of concentrating only on the steps needed to achieve a goal, consider some of the reasons why that goal might be valuable or what might happen if you reach that goal.
• Reframe your thinking : When you are approaching a problem, it can be helpful to purposefully try to think about the problem in a different way. How might someone else approach it? Is there an easier way to accomplish the same thing? Are there any elements you haven't considered?
• Consider the big picture : Rather than focusing on the specifics of a situation, try taking a step back in order to view the big picture. Where concrete thinkers are more likely to concentrate on the details, abstract thinkers focus on how something relates to other things or how it fits into the grand scheme of things.

Abstract thinking allows people to think about complex relationships, recognize patterns, solve problems, and utilize creativity. While some people tend to be naturally better at this type of reasoning, it is a skill that you can learn to utilize and strengthen with practice. 

It is important to remember that both concrete and abstract thinking are skills that you need to solve problems and function successfully. 

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American Psychological Association. Creative thinking .

American Psychological Association. Convergent thinking .

American Psychological Association. Critical thinking .

American Psychological Association. Divergent thinking .

Lermer E, Streicher B, Sachs R, Raue M, Frey D. The effect of abstract and concrete thinking on risk-taking behavior in women and men . SAGE Open . 2016;6(3):215824401666612. doi:10.1177/2158244016666127

Namkoong J-E, Henderson MD. Responding to causal uncertainty through abstract thinking . Curr Dir Psychol Sci . 2019;28(6):547-551. doi:10.1177/0963721419859346

White R, Wild J. "Why" or "How": the effect of concrete versus abstract processing on intrusive memories following analogue trauma . Behav Ther . 2016;47(3):404-415. doi:10.1016/j.beth.2016.02.004

Williams DL, Mazefsky CA, Walker JD, Minshew NJ, Goldstein G. Associations between conceptual reasoning, problem solving, and adaptive ability in high-functioning autism . J Autism Dev Disord . 2014 Nov;44(11):2908-20. doi: 10.1007/s10803-014-2190-y

Oh J, Chun JW, Joon Jo H, Kim E, Park HJ, Lee B, Kim JJ. The neural basis of a deficit in abstract thinking in patients with schizophrenia . Psychiatry Res . 2015;234(1):66-73. doi: 10.1016/j.pscychresns.2015.08.007

Hartshorne JK, Germine LT. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span . Psychol Sci. 2015;26(4):433-43. doi:10.1177/0956797614567339

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Thinking Outside The Box: The Difference Between Concrete Vs. Abstract Thinking

Abstract thought is a defining feature of human cognition . Scholars from diverse fields — including psychologists, linguists, anthropologists, neuroscientists, and even philosophers — have contributed to the scientific discussion of how abstract ideas are acquired and used by the brain. Concrete thought is somewhat better understood, as it represents a more grounded form of thinking than what is typically found in abstract thought. Concrete thinkers focus on physical objects and the physical world, making their thinking process more immediately obvious and tied to the literal form. Both modes of thinking are useful for human cognition.

Distinguishing between concrete and abstract thoughts

Understanding the differences between these two types of thinking may help illustrate their unique contributions to human thought.

Concrete thinking

Concrete thinking is grounded in facts and operates in a literal domain , focusing on objective facets such as physical attributes (e.g., color and shape) and verifiable occurrences (e.g., chronological sequences). Concrete thinkers often rely on concrete objects and specific examples to solve problems and classify objects. It avoids extrapolations, categorizing information superficially and within rigid boundaries. Concrete thinking is chiefly concerned with detail gathering, excluding analyses of trends and exploration of potentialities.

Rumination , a cognitive process characterized by excessive or repetitive thoughts, including intrusive memories, that interfere with daily life, might use concrete thinking to contemplate complex issues. These thoughts might include questions like "What happened in this situation?" and "What steps can I take to address the problem?" Although these questions address more than basic attributes, they are anchored in objectively definable detail.

Abstract thinking

It synthesizes and integrates information into broader contexts, forming the bedrock of creativity, critical analysis, and problem-solving. This thinking style is a vital skill for those who exercise creativity in fields like theoretical math or philosophical concepts. This allows individuals to transcend surface-level understanding. Abstract thinking is indispensable for grappling with intangible concepts, including emotions, and often involves contemplating hypothetical scenarios.

Rumination, explored above, also has an abstract component . Abstract ruminative thoughts may include questions like "Why do I always feel so unhappy?" or "Why didn’t I handle this better?" These queries pivot away from objective facts and explore concepts that may be interpreted in multiple ways.

When is each type of thinking most useful?

Several factors determine whether concrete or abstract thinking is most appropriate, but in practice, most deliberate thought processes benefit from the interplay between the two modes. Abstract thinking skills, including abstract reasoning skills, are crucial in understanding complex concepts and integrating existing knowledge. For instance, effective problem-solving necessitates the initial definition of its core features (concrete thinking) and subsequent high-level analysis (abstract thinking).

Psychologists and sociologists have scrutinized the relationship between abstract and concrete thought, often using  construal learning theory (CLT) as a framework. CLT identifies how psychological distance influences a person’s choice between abstract and concrete thinking. “Psychological distance” can be measured in various ways:

• Temporal distance: The amount of time between a person and their subject of contemplation.
• Spatial distance: The physical separation between a person and their subject of contemplation.
• Social distance: The emotional distance between individuals.
• Hypothetical distance: An individual’s assessment of the likelihood of their subject of contemplation occurring.

CLT suggests that individuals tend toward abstract thinking when they perceive substantial psychological distance and favor concrete thinking when that distance diminishes. This indicates that more abstract thinkers are likely to engage in abstract reasoning when dealing with subjects that are not immediately present or concrete. For example, a person planning to attend a family reunion next year (significant temporal distance) is more likely to think of big-picture, abstract elements of their plan — perhaps their excitement about attending the event. But as the event approaches, their thoughts shift toward concrete details, such as what they’ll wear to the party.

CLT can be used to assess a person's propensity for risk-taking behavior. Evidence suggests that individuals with a high construal level (greater psychological distance) employ more abstract thought processes and are more likely to engage in risky behaviors. Conversely, individuals with a low construal level (lesser psychological distance) display greater risk aversion as they are more aware of objective risk factors.

How do concrete and abstract thinking develop?

It’s worth noting that babies are not born with the ability to think abstractly. Jean Piaget’s stages of cognitive development illustrate how a child’s cognition develops over time. This cognitive development is crucial in the transition from a concrete thinker to an abstract thinker.

• Sensorimotor stage (birth to age two): Babies engage primarily with their sensory world, absorbing concrete information like a sponge without making abstract connections. This stage is fundamental in developing motor skills and concrete thinking skills.
• Preoperational stage (ages two to seven): Young children begin to develop abstract thinking, engaging in imaginary play, comprehending the rudiments of symbolism, and understanding someone else’s point. They start to understand figurative language and can interpret facial expressions, moving towards more abstract thinking abilities.
• Concrete operational stage (ages seven to 11): Children can understand that other people may experience the world differently than they do. They can recognize abstract concepts but remain tethered to empirical experiences. This stage involves processing theoretical concepts and developing concrete thinking skills to solve problems.
• Formal operational stage (age 11 to adulthood): Abstract thought matures as individuals use concrete information to derive abstract conclusions. Individuals expand their ability to empathize and discern patterns among abstract concepts. This stage is where strong abstract thinking skills are developed, allowing individuals to grapple with more complex concepts and engage in theoretical math and philosophical concepts, and solve abstract riddles such as brain teasers. This stage equips individuals with the capacity to analyze hypothetical scenarios and address "what-if" questions.

Key insights from Piaget's theory underscore the development of abstract thinking, where concrete thinking lays the foundation. This progression from being a concrete thinker to an abstract thinker is a vital aspect of cognitive development. That is, concrete thought is a prerequisite for abstract thought because objective facts must be defined before they can be analyzed. Proficiency in abstract thought unfolds gradually over many years.

Assessing the merits of abstract and concrete thinking

Abstract thinking allows humans to create art, reach conclusions through debate, and predict what the future may hold. It involves a thinking process that is less immediately obvious than concrete thinking, often requiring the individual to consider other meanings and exercise creativity. Because abstract thought empowers higher cognitive functions, it may seem that it is a preferable mode of cognition over concrete thought.

However, abstract thinking is not without its limitations. An unbalanced reliance on abstract rumination can lead to mental health concerns , such as depression. In individuals with mental health conditions like autism spectrum disorder or who have had a traumatic brain injury, the balance between abstract and concrete thinking can be particularly crucial, and reading body language and understanding figurative expressions may be difficult for some individuals. Conversely, a conscious preference for concrete thinking can potentially  mitigate negative mental health . Both concrete and abstract thinking are necessary for human cognition. For instance, abstract thinkers may engage in the active practice of new ideas, while concrete thinkers might focus on classifying objects and dealing with the literal form of information. While abstract thought may be associated with higher-order cognitive processes, those processes are built upon the foundation of concrete thinking.

Can therapy help manage cognitive and abstract thinking?

If you’re interested in recognizing and adapting your cognitive tendencies, a therapist can help. Therapists are trained in a variety of evidence-based techniques, including cognitive behavioral therapy , to analyze your mental processes and guide you toward meaningful conclusions about your thought patterns. This therapy can be particularly helpful for those struggling with difficulty relating to others due to their thinking style, whether they are more comfortable with abstract thinking vs concrete thinking.

You may wish to consider online therapy, which is available for individuals to avail the care of a skilled mental health professional. Working with a therapist online removes some common barriers to therapy, like having to commute to an office. Removing geographical constraints allows you to choose a therapist outside of your local area, which may be especially helpful to those who live in regions with limited mental health professionals. Online therapists have the same training and credentials as traditional therapists, and evidence indicates that therapy delivered remotely is just as effective as in-person therapy.

What is an example of concrete thinking?

Concrete thinking is literal. It focuses on physical attributes and things that can be verified with facts. Concrete thinking is more rigid and is chiefly concerned with gathering details or information. Someone who is a concrete thinker might take things very literally. For example, if you ask them to run to the store, they may think you want them to actually run to the store.

What is an example of abstract thinking?

An abstract thinking style involves processing theoretical concepts. It is more flexible and links causality, figurative language, themes, and intangible concepts and is the basis of things like problem-solving, creativity, and critical analysis. It often involves contemplating hypothetical scenarios, intangible concepts, and emotions. An excellent example of abstract thinking is making predictions. Any time someone assesses available information and processes it to determine what might happen next, they use abstract thinking.

Can you be both a concrete and abstract thinker?

Yes, people can be both concrete and abstract thinkers. According to construal level theory (CLT), psychological distance can affect whether a person uses concrete or abstract thinking . This theory measures psychological distance in four ways: temporal distance, or the amount of time between the person and the subject they’re thinking about; spatial distance, or the physical distance between the person and what they’re thinking about; spatial distance, or the physical separation between the person and what they’re thinking; and hypothetical distance, of the person’s assessment of the likelihood of what they’re thinking about occurring. 

CLT suggests that people tend to think more abstractly when they perceive a larger psychological distance and more concretely when they perceive less psychological distance. For example, someone who has a big vacation planned next year may think about how excited they are or a simple list of the things they hope to see, but as the trip approaches, they will likely focus on more concrete details, like making a list of what they need to pack, making sure they have their travel documents in order, and double-checking their itineraries.

Am I an abstract or concrete thinker?

Gaining abstract thinking is part of cognitive development; young children have concrete thinking first and develop abstract thinking as they mature. Some people may be prone to thinking more abstractly or concretely, but most are capable of both. People with good abstract reasoning skills may be better at imagining things that are not physically present, understanding complex concepts, and deciphering body language, and they may be more talented at creative endeavors or theoretical math or science concepts. On the other hand, concrete thinkers may be more likely to stick to rigid routines. They may think in more black-and-white terms and have difficulty considering gray areas or expanding their existing knowledge.

What are abstract thinkers good at?

People with strong abstract thinking skills can excel in many areas, including graphic design, landscape architecture, engineering, psychology, and psychology. They can also make excellent detectives, criminal investigators, and scientists.

An example of concrete thinking might be someone who sits down and lists items they need to accomplish in a day. In contrast, an abstract thinker might make the same kind of list, but they may rank it according to the order of importance or organize it according to the most efficient way to get all the tasks done.

What is a concrete thinking example for a student?

Specific examples of when students may use concrete thinking skills are when they organize their schedules or make a list of assignments they need to complete.

What is an example of a concrete task?

Many tasks might be considered concrete. For example, doing the dishes is a concrete task; they’re either clean or not. Other examples might be making the bed, folding laundry, washing the car, or vacuuming the carpet.

Is concrete thinking good or bad?

Concrete thinking isn’t necessarily good or bad; everyone needs to be able to think concretely at times. It can become a problem when people cannot switch out of concrete thinking in the physical world. Having abstract thinking abilities can help with problem-solving, creativity, and analysis, all of which can influence how someone interacts with the world. 

What is an example of concrete thinking in mental health?

Concrete thinking can be considered a feature of schizophrenia . People with this condition can be said to have an abstraction deficit or the inability to distinguish between symbolic, abstract ideas and the concrete. People with schizophrenia may not be able to deal with their experiences conceptually and cannot perceive objects as belonging to a class or category. Another example is autism spectrum disorder; people with this condition may have a very concrete way of thinking.

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Psychologily

What is Abstract Thinking? Understanding the Power of Creative Thought

When we think about thinking, we usually imagine it as a straightforward process of weighing options and making decisions. However, there is a more complex and abstract thinking type. Abstract thinking involves understanding and thinking about complex concepts not tied to concrete experiences, objects, people, or situations.

Abstract thinking is a type of higher-order thinking that usually deals with ideas and principles that are often symbolic or hypothetical. It is the ability to think about things that are not physically present and to look at the broader significance of ideas and information rather than the concrete details. Abstract thinkers are interested in the deeper meaning of things and the bigger picture. They can see patterns and connections between seemingly unrelated concepts and ideas. For example, when we listen to a piece of music, we may feel a range of emotions that are not directly related to the lyrics or melody. Abstract thinkers can understand and appreciate the complex interplay of elements that create this emotional response.

Understanding Abstract Thinking

Humans can think about concepts and ideas that are not physically present. This is known as abstract thinking. It is a type of higher-order thinking that involves processing often symbolic or hypothetical information.

Defining Abstract Thinking

Abstract thinking is a cognitive skill that allows us to understand complex ideas, make connections between seemingly unrelated concepts, and solve problems creatively. It is a way of thinking not tied to specific examples or situations. Instead, it involves thinking about the broader significance of ideas and information.

Abstract thinking differs from concrete thinking, which focuses on memorizing and recalling information and facts. Concrete thinking is vital for understanding the world, but abstract thinking is essential for problem-solving, creativity, and critical thinking.

Origins of Abstract Thinking

The origins of abstract thinking are partially clear, but it is believed to be a uniquely human ability. Some researchers believe that abstract thinking results from language and symbolic thought development. Others believe that it results from our ability to imagine and visualize concepts and ideas.

Abstract thinking is an essential skill that can be developed and strengthened with practice regardless of its origins. By learning to think abstractly, we can expand our understanding of the world and develop new solutions to complex problems.

Abstract thinking is a higher-order cognitive skill that allows us to think about concepts and ideas that are not physically present. We can improve our problem-solving, creativity, and critical thinking skills by developing our abstract thinking ability.

Importance of Abstract Thinking

Abstract thinking is a crucial skill that significantly impacts our daily lives. It allows us to understand complex concepts and think beyond what we see or touch. This section will discuss the benefits of abstract thinking in our daily lives and its role in problem-solving.

Benefits in Daily Life

Abstract thinking is essential for our personal growth and development. It enables us to think critically and creatively, which is necessary for making informed decisions. When we think abstractly, we can understand complex ideas and concepts, which helps us communicate more effectively with others.

Abstract thinking also helps us to be more adaptable and flexible in different situations. We can see things from different perspectives and find innovative solutions to problems. This skill is beneficial in today’s fast-paced world, where change is constant, and we need to adapt quickly.

Role in Problem Solving

Abstract thinking plays a crucial role in problem-solving. It allows us to approach problems from different angles and find creative solutions. When we can think abstractly, we can see the bigger picture and understand the underlying causes of a problem.

By using abstract thinking, we can also identify patterns and connections that may not be immediately apparent. This helps us to find solutions that are not only effective but also efficient. For example, a business owner who can think abstractly can identify the root cause of a problem and develop a solution that addresses it rather than just treating the symptoms.

Abstract thinking is a valuable skill with many benefits in our daily lives. It allows us to think critically and creatively, be more adaptable and flexible, and find innovative solutions to problems. By developing our abstract thinking skills, we can improve our personal and professional lives and positively impact the world around us.

Abstract Thinking Vs. Concrete Thinking

When it comes to thinking, we all have different approaches. Some of us tend to think more abstractly, while others tend to think more concretely. Abstract thinking and concrete thinking are two different styles of thought that can influence how we perceive and interact with the world around us.

Key Differences

The key difference between abstract and concrete thinking is the level of specificity involved in each style. Concrete thinking focuses on a situation’s immediate and tangible aspects, whereas abstract thinking is more concerned with the big picture and underlying concepts.

Concrete thinking is often associated with literal interpretations of information, while abstract thinking relates to symbolic and metaphorical interpretations. For example, if we describe a tree, someone who thinks concretely might describe its physical appearance and characteristics. In contrast, someone who thinks abstractly might explain its symbolic significance in nature.

The transition from Concrete to Abstract

While some people may naturally lean towards one style of thinking over the other, it is possible to transition from concrete to abstract thinking. This can be particularly useful in problem-solving and critical-thinking situations, where a more abstract approach may be needed to find a solution.

One way to make this transition is to focus on a situation’s underlying concepts and principles rather than just the immediate details. This can involve asking questions that explore the broader implications of a situation or looking for patterns and connections between seemingly unrelated pieces of information.

Abstract and concrete thinking are two different styles of thought that can influence how we perceive and interact with the world around us. While both styles have their strengths and weaknesses, transitioning between them can be valuable in many areas of life.

Development of Abstract Thinking

As we grow and learn, our ability to think abstractly develops. Age and education are two major factors that influence the development of abstract thinking.

Influence of Age

As we age, our ability to think abstractly improves. This is due to the development of our brain and cognitive abilities. According to Piaget’s theory of cognitive development , children progress through four stages of cognitive development, with the final stage being the formal operational stage. This stage is characterized by the ability to think abstractly and logically about hypothetical situations and concepts.

Role of Education

Education also plays a significant role in the development of abstract thinking. Through education, we are exposed to new ideas, concepts, and theories that challenge our existing knowledge and encourage us to think abstractly. Education also gives us the tools and skills to analyze and evaluate complex information and ideas.

In addition to traditional education, engaging in activities promoting abstract thinking can be beneficial. For example, participating in debates, solving puzzles, and playing strategy games can all help improve our abstract thinking skills.

The development of abstract thinking is a complex process influenced by age and education. By continually challenging ourselves to think abstractly and engaging in activities that promote abstract thinking, we can continue to improve our cognitive abilities and expand our knowledge and understanding of the world around us.

Challenges in Abstract Thinking

Abstract thinking can be a challenging cognitive process, especially for those not used to it. Here are some common misunderstandings and difficulties people may encounter when thinking abstractly.

Common Misunderstandings

One common misunderstanding about abstract thinking is that it is the same as creative thinking. While creativity can certainly involve abstract thinking, the two are not interchangeable. Abstract thinking consists of understanding and thinking about complex concepts not tied to concrete experiences, objects, people, or situations. Creative thinking, on the other hand, involves coming up with new and innovative ideas.

Another common misunderstanding is that abstract thinking is only helpful for people in certain fields, such as science or philosophy. Abstract thinking can benefit many different areas of life, from problem-solving at work to understanding complex social issues.

Overcoming Difficulties

One difficulty people may encounter when thinking abstractly is a lack of concrete examples or experiences to draw from. To overcome this, finding real-world examples of the concepts you are trying to understand can be helpful. For example, if you are trying to understand the concept of justice, you might look for examples of situations where justice was served or not served.

Another challenge people may encounter is focusing too much on details and needing more on the bigger picture. To overcome this, try to step back and look at the broader significance of the ideas and information you are working with. This can involve asking yourself questions like “What is the main point here?” or “How does this fit into the larger context?”

Abstract thinking can be a challenging but valuable cognitive process. By understanding common misunderstandings and overcoming difficulties, we can develop our ability to think abstractly and apply it in various aspects of our lives.

Frequently Asked Questions

How does abstract thinking differ from concrete thinking.

Abstract thinking is a type of thinking that involves the ability to think about concepts, ideas, and principles that are not necessarily tied to physical objects or experiences. Concrete thinking, on the other hand, is focused on the here and now, and is more concerned with the physical world and immediate experiences.

What are some examples of abstract thinking?

Examples of abstract thinking include the ability to understand complex ideas, to think creatively, to solve problems, to think critically, and to engage in philosophical discussions.

What is the significance of abstract thinking in psychiatry?

Abstract thinking is an important component of mental health and well-being. It allows individuals to think beyond the present moment and to consider different possibilities and outcomes. In psychiatry, the ability to engage in abstract thinking is often used as an indicator of cognitive functioning and overall mental health.

At what age does abstract thinking typically develop?

Abstract thinking typically develops during adolescence, around the age of 12 or 13. However, the ability to engage in abstract thinking can continue to develop throughout adulthood, with continued practice and exposure to new ideas and experiences.

What are the stages of abstract thought according to Piaget?

According to Piaget, there are four stages of abstract thought: the sensorimotor stage (birth to 2 years), the preoperational stage (2 to 7 years), the concrete operational stage (7 to 12 years), and the formal operational stage (12 years and up). During the formal operational stage, individuals are able to engage in abstract thinking and to think about hypothetical situations and possibilities.

What are some exercises to improve abstract thinking skills?

Some exercises that can help improve abstract thinking skills include engaging in philosophical discussions, solving puzzles and brain teasers, playing strategy games, and engaging in creative activities such as writing or painting. Additionally, exposing oneself to new ideas and experiences can help broaden one’s perspective and improve abstract thinking abilities.

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Abstract Thinking

Abstract thinking is a fundamental cognitive process that allows us to explore and understand concepts beyond the realm of concrete reality. It involves the ability to think conceptually, creatively, and symbolically, enabling us to grasp complex ideas, solve problems, and engage in higher-order thinking.

Abstract thinking can be defined as the mental ability to conceptualize and understand concepts that are not directly tied to physical objects or concrete events. Unlike concrete thinking that focuses on specific, tangible things, abstract thinking allows us to derive meaning, interpret symbols, make inferences, recognize patterns, and engage in metaphorical and symbolic reasoning. It is a process that goes beyond the surface-level understanding and helps us navigate the complexities of the world.

• Interpreting a poem’s underlying meaning rather than focusing solely on its literal words.
• Understanding the concept of justice and evaluating its application in various scenarios.
• Recognizing and appreciating symbolism in art, literature, and music.
• Arriving at a logical conclusion by examining multiple perspectives and possibilities.
• Using analogies to explain complex ideas or relationships.
• Developing and testing hypotheses in scientific experiments.

The Importance of Abstract Thinking

Abstract thinking plays a crucial role in various aspects of our lives. It is not only an essential cognitive skill but also a tool for problem-solving, decision-making, and creativity. Here are a few key areas where abstract thinking is particularly valuable:

• Education: Abstract thinking helps students engage in critical thinking, analyze information, and delve deeper into subjects beyond surface-level knowledge. It promotes a deeper understanding of complex ideas and encourages independent thinking.
• Problem Solving: When faced with challenges, abstract thinking allows us to generate innovative solutions by exploring unconventional possibilities and finding connections between seemingly unrelated concepts. It helps us think “outside the box” and discover new perspectives.
• Creativity: Abstract thinking fuels creativity by allowing us to envision and create something new. Artists, musicians, writers, and inventors rely heavily on abstract thinking to generate original ideas, visualize concepts, and communicate abstract emotions or experiences.
• Communication: Abstract thinking enhances effective communication by enabling us to convey complex ideas using metaphors, analogies, and symbolic language. It helps us express ourselves more vividly and engage listeners or readers on a deeper, emotional level.
• Decision-Making: Abstract thinking aids in decision-making, as it helps us consider the potential consequences, evaluate different options, and anticipate long-term effects. By thinking abstractly, we can make informed choices and weigh the pros and cons of each alternative.

Tips for Enhancing Abstract Thinking

While abstract thinking comes naturally to some individuals, it can also be developed and strengthened through practice. Here are a few tips to enhance your abstract thinking abilities:

• Embrace Curiosity: Cultivate a curious mindset and ask questions that encourage deeper thinking.
• Engage in Creative Activities: Explore art, music, writing, or any activity that encourages abstract thinking and self-expression.
• Read Widely: Engage with diverse literature and expose yourself to different perspectives, ideologies, and worldviews.
• Practice Symbolic Reasoning: Analyze symbols, metaphors, and allegories in various forms of media to develop your ability to interpret abstract representations.
• Investigate Opposing Views: Challenge your own beliefs by seeking out and critically evaluating opposing viewpoints.
• Play Brain-stimulating Games: Engage in puzzles, riddles, and strategy games that require abstract thinking and problem-solving.

Abstract thinking is a remarkable cognitive ability that allows us to explore the world beyond its concrete boundaries . By unlocking the power of imagination, symbolism, and conceptualization, abstract thinking enriches our lives and enables us to navigate the complexities of our existence. Enhancing our abstract thinking skills not only empowers us intellectually but also enhances our creativity, problem-solving abilities, and decision-making skills.

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Table of Contents

Thinking is cognitive activity when we consciously use our brains to make sense of the world around us and choose how to respond to it. We develop our ability to think at an early age, so we often take it for granted, but it’s a very complex process that makes us what we are.

Young children tend to think of their world in concrete ways, learning facts about objects they have encountered and their own experiences. But as they grow and mature , children develop abstract thinking patterns that allow them to see a bigger picture, think beyond just the “here and now,” and reflect on events and ideas.

Fully grown-up adults can adopt their styles of thinking according to the demands of the environment, depending on the situations and contexts, although some people may have difficulty with abstract thinking. So here, we’ll talk about the difference between abstract vs. concrete thinking and provide you with some tips on how to develop abstract reasoning skills.

What is abstract thinking ?

Abstract thinking can be defined as the ability to think about complex concepts and ideas without being tied to specific examples, experiences, situations, objects, and people. It is considered a type of high-order thinking because it’s more complicated than other styles of thinking that are centered around real-life facts and information based on data.

Abstract thinking allows us to absorb information from our senses, process it, and connect it to a wider world . As a result, abstract thinkers can reflect on events and ideas as well as attributes and relationships separate from the real-life objects that share those relationships or have those attributes.

Abstract thinking also allows us to exercise creativity, think critically, and solve problems in unique ways even if there isn’t enough existing knowledge. Examples of abstract thinking include using humor in conversations, being hopeful in tough situations , recognizing that the value of things is defined by what we place on them, etc.

We use abstract thinking in different aspects of our daily lives, for example, when we:

• Describe something using metaphor or analogy
• Analyze situations and come up with creative solutions
• Notice patterns and relationships between objects, events, and processes
• Continue to explore options in a situation after we have found a resolution
• Consider someone else’s point of view
• Predict something based on available information and our thoughts

Abstract thinking vs. concrete thinking

Abstract thinking and concrete thinking are opposite approaches. Concrete thinking is closely connected to objects and experiences that we can observe directly . Concrete thinkers perceive things that are present physically around them through their senses (smell, sight, sound, taste, and touch) and interpret them as they are. Whatever can be seen, heard, smelt, or touched is analyzed at a superficial level, but concrete thinkers don’t generalize information to other meanings and situations and don’t establish further connections.

Abstract thinking goes deeper and allows us to make generalizations and classify objects and experiences . It’s a form of abstract reasoning when we don’t rely on concrete facts but instead use our imagination to think about something that isn’t immediately obvious or real. While concrete thinking focuses on the physical world and emphasizes facts, abstract thinking involves thinking about concepts in general terms rather than concrete details.

How do we develop abstract thinking?

Abstract thinking isn’t something that we’re born with – abstract thinking skills are an important part of cognitive development in childhood . The Swiss developmental psychologist Jean Piaget explained how children’s thinking abilities change as they get older and outlined this process from birth to early adulthood. He described four distinct stages of cognitive development:

• Sensorimotor stage is an early period when children are gaining information from their senses (touching, grasping, watching, and listening) and exploring the world using their motor skills. This is often why you see babies putting everything they can grab in their mouth! They aren’t able to think further than what is in front of them, though they do start to understand object permanence. Those things are still there even when they can no longer see them.
• Preoperational stage (from ages 2 to 7) is the time when children develop the ability to think symbolically. They learn that such symbols as pictures, sounds, and letters can represent the objects in the real world. This is often where you see engagement in pretend play, imitation of others, mental imagery (such as creating an imaginary friend), and drawing pictures that represent past and future events. This is the beginning of when the capability for abstract thinking starts to develop though is likely not expressed. Kids at this stage believe that other people see things the same way as they do.
• Concrete operational stage (from age 7 until around 11), children become more logical, but their thinking still remains tied to what they directly observe. Kids also begin to realize that not everyone sees the world the same as them, and are open to new ideas and problem-solving. This is the stage where they realize that water can turn to ice and then back to water as well as begin sorting and organizing objects by color or type.
• Formal operational stage begins at age 11 and continues into adulthood. Children improve their ability to reason about concrete information and begin to think abstractly. They become more skilled at thinking about something from the perspective of another person and grow their ability to empathize . Kids also develop skills to mentally manipulate abstract ideas and notice certain patterns and relationships between abstract concepts. This is where they can start to think of different theories or entertain thoughts of “what if.”

Conditions that impact abstract thinking

Abstract thinking is a vital skill, but it can be challenging for some. So why can’t some people think abstractly? There are some disorders and mental health conditions that may limit abstract reasoning , including:

• Autism spectrum disorder
• Learning disabilities
• Schizophrenia
• Traumatic brain injury (TBI)

The natural aging process can also impact abstract thinking abilities, especially those associated with fluid intelligence, which can be defined as the ability to solve problems in unique ways. Research suggests that skills associated with fluid intelligence reach their peak around the ages of 30 or 40 and tend to decline as people reach later adulthood.

People who think too concretely may find it difficult to understand how other people feel because they can’t accurately interpret such social signs as body language, facial expressions, words, tones of voice, and behaviors in a social context. Sometimes, concrete thinkers stick to literal interpretations of phrases and figurative expressions and use rigid behaviors, and which may cause conflicts with other people. Concrete thinkers may also have difficulties with problem-solving, imagination, and creating things.

How to communicate with a concrete thinker

If some people in your life have a condition that makes them prone to concrete thinking, you can use these tips to communicate with them more effectively:

• Always be as specific as possible and look for opportunities to present facts
• Avoid using metaphors, analogies, and idioms
• Use illustrations and photos when you explain something
• Limit sarcasm and jokes because they often rely on abstract ideas and plays on words
• Anticipate that a concrete thinker might compare, contract, and categorize things in concrete ways

Can abstract thinking not be helpful?

People with strong abstract thinking skills tend to score well on IQ tests and excel in areas that require creativity, such as art, writing , and other related areas. But you should remember that in some cases, the ability to make connections, predict, and imagine can lead to problems.

For example, a cognitive distortion known as catastrophizing, when people habitually imagine the worst possible potential outcomes, can cause feelings of fear and anxiety or worsen depression symptoms.

Research has also shown that abstract thinking is sometimes associated with rumination . This thinking style can occasionally become problematic for people with such mental health conditions as depression, anxiety, and post-traumatic stress disorder (PTSD).

The good news is that researchers have found it can be helpful to practice concrete thinking skills and use them to improve symptoms of depression because it can stop people from overgeneralizing. Training people to think concretely about traumatic experiences has been shown to help trauma survivors build resilience and decrease the number of intrusive memories.

On the flip side, those that are good at abstract thinking can learn how to use it to combat these symptoms in conjunction with the concrete thinking skills mentioned above. By focusing on building mindfulness as to when they are ruminating, this allows for an opportunity to catch these thoughts when they are happening and then, with the guidance of a text therapist , use abstract skills to shift and engage in using their imagination to envision coping effectively with challenges in their life.

How to improve abstract thinking skills

Abstract thinking is a skill that you can learn and improve through active practice. This can be done in a number of ways:

• Solving puzzles, optical illusions, crosswords, and other brain teasers will help you learn to view information from different perspectives and angles and improve problem-solving and critical thinking skills.
• You can also play with figurative language and write metaphors, similes, analogies, and pieces of personification.
• Try to expose yourself to completely new experiences and ideas regularly. For example, learning about new cultures will allow you to get rid of biases and prejudice, think more freely, and minimize stereotypes.
• Seek a few efficient solutions to a single problem. Think out of the box, and don’t be afraid to come out of your comfort zone and experiment with your ideas.

Bottom line

Abstract thinking and concrete thinking are two types of thought processes. Concrete thinking focuses on things that are real and tangible, while abstract thinking is a higher-level mode of thinking that involves processing theoretical concepts and allows us to make connections and see patterns. It’s important to remember that you need both concrete and abstract thinking skills to solve problems and maintain good mental health . At Calmerry , we understand the significance of this balance and offer support to help you nurture these essential cognitive skills.

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What is abstract thinking? 10 activities to improve your abstract thinking skills

Have you ever been in a meeting and proposed a unique solution to a problem? Or have you ever been faced with a difficult decision and thought about the potential consequences before making your choice?

These are examples of abstract thinking in action. Everyone uses abstract thinking in day-to-day life, but you may be wondering — what is abstract thinking?

Abstract thinking is the ability to comprehend ideas that aren't tangible or concrete. It's a crucial skill for problem-solving, creativity, and critical thinking — and the best part is that it can be developed and strengthened with practice.

In this article, we'll explore the concept of abstract thinking and offer some simple ways to become a stronger abstract thinker in everyday life. With some practice, you can become an expert problem-solver and use conceptual thinking to your advantage.

What is abstract thinking?

Abstract thinking is a cognitive process that allows us to think beyond observable information and deal with concepts, ideas, theories, and principles. By thinking outside of our existing knowledge, we can come up with solutions that aren't immediately obvious. This type of thinking is essential for problem-solving, decision-making, and critical thinking .

Abstract thinking enables us to generate new ideas, connect unrelated concepts, and look at the bigger picture. It also involves contemplating sentiments such as love, freedom, and compassion. These concepts aren’t concrete and can have different interpretations. By using abstract thinking, we can gain a deeper understanding of these concepts and their different meanings.

Abstract thinking is also crucial to creativity, innovation, and advanced problem-solving. It allows us to think beyond the surface level of a problem and come up with unique solutions. This can be especially important in fields such as science and technology, where new breakthroughs often require fresh perspectives and innovative thinking.

In addition, abstract thinking is a vital skill for personal development, enabling us to think beyond our immediate environment and beliefs and consider different perspectives. This allows individuals to make better decisions, be more receptive and open to change, and be more creative.

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Abstract vs. concrete thinking

We can best understand abstract thinking by knowing what it's not — concrete thinking. Concrete thinking is understanding and processing observable and directly experienced information. It's often associated with basic sensory and perceptual processes, such as recognizing a familiar face or identifying a physical object by its shape.

On the other hand, abstract thinking is the ability to understand and process information that isn’t directly observable or experienced. Abstract thinking is often associated with higher-level cognitive processes, such as decision making and critical thinking.

For example, if you’re asked what a chair looks like, concrete thinking would involve picturing it and what it's typically used for. By contrast, abstract thinking would involve considering what a chair could symbolize or how it could be used differently than what is traditionally accepted.

The two types of thinking aren’t mutually exclusive — instead, they complement each other in the cognitive process. We need concrete and abstract thinking skills to effectively process information and make informed decisions.

How is abstract thinking developed?

Abstract thinking is a cognitive process that develops over time, beginning in childhood and continuing into adulthood. The psychologist Jean Piaget , known for his theory of cognitive development, proposed that children go through different stages of mental growth. This begins with the sensorimotor stage, in which infants and young children learn through their senses and motor skills and develop concrete thinking skills. In their later years, they develop more advanced cognitive abilities, including abstract thinking.

During childhood, abstract thinking develops as children use the cognitive approach to learning to grasp new concepts and skills. They start to understand and manipulate abstract concepts such as numbers, time, and cause and effect. As they observe the world around them, they use what they know to make sense of what is happening and explore other possibilities.

A learning disability, mental health condition, or brain injury can, however, affect abstract thinking. Among these are psychological illnesses like schizophrenia , developmental disorders like autism, ADHD, and dyslexia, and physical illnesses like stroke, dementia, and traumatic brain injury. These individuals may have difficulty understanding and manipulating abstract concepts and require additional support to develop their abstract thinking skills.

As adults, we continue to refine our abstract thinking skills through practice. We can become adept at problem-solving and critical thinking by regularly engaging in activities that require abstract thought. These activities include brainstorming, reading, writing, playing board games, and exploring creative projects. Factors such as experience, education, and environment all play a role in the development of abstract thinking, and it's essential to continue challenging and exercising our cognitive learning skills to maintain and improve abstract thinking.

Why is it important to learn to think abstractly?

Thinking abstractly is a crucial skill that allows us to go beyond surface-level understanding and interpret the deeper meaning of concepts, ideas, and information. It enables us to see the big picture and make connections between seemingly unrelated ideas, which is a crucial thinking tool for problem solving and critical thinking. Additionally, learning to think abstractly can bring numerous benefits in our daily lives and in various fields such as science, technology, engineering, and mathematics (STEM).

For instance, abstract thinking enables us to process information quickly and efficiently on a daily basis. It helps us understand and interpret what people are saying and what is happening around us, which can lead to better decision-making. Abstract thinking is vital in STEM fields for innovation and progress, as it encourages creative thinking and the exploration of new ideas and perspectives.

Furthermore, abstract thinking helps us understand abstract concepts such as justice, freedom, and patriotism. By using analogies and other tools, we can consider what these words stand for, their implications in our world, and how they can be applied effectively in day-to-day life. In this way, abstract thinking helps us make sense of complex ideas and concepts and enables us to navigate the world with greater insight and understanding.

10 tips to improve your abstract thinking skills

Abstract thinking is crucial for problem-solving, creativity, and critical thinking. Fortunately, there are many ways to improve these skills in your everyday life.

1. Incorporate puzzles into your life

Solving puzzles is a great way to practice abstract reasoning and exercise your brain. Whether you enjoy crosswords, Sudoku, or jigsaw puzzles, solving these types of problems improves your ability to think abstractly by requiring you to think critically and strategically to find solutions to issues that aren’t immediately obvious.

2. Learn something new

Your mind engages in the information processing cycle when learning new things. Learning something new allows you to explore different perspectives and understand how the world works. You'll gain new knowledge and practice your abstract thinking skills as you process, store, and recall what you’ve learned.

3. Explore your creativity

Creative expression is another excellent way to exercise your abstract thinking skills. Creativity engages the right side of the brain , which is responsible for abstract thinking and creative problem-solving. Through drawing, painting, writing, or photography, exploring the creative process encourages you to think outside the box and develop new ideas.

4. Practice mindfulness

Mindfulness is the practice of purposely observing the present moment without judgment or bias. Practicing mindfulness can help you improve your abstract thinking by teaching you how to observe your thoughts, feelings, and emotions objectively and without judgment. As you think more deeply and analytically about what's happening in the present moment, you will further develop your abstract thinking skills.

5. Make a habit of reading

Books and articles on various topics can help you build your understanding of complex concepts and ideas. Reading enables you to develop your ability to connect different ideas and think critically about the material. You also have to use your imagination to visualize what you're reading, which helps to improve your creative thinking abilities. Annotating your reading can step this up a notch.

6. Travel somewhere new

Traveling to new places exposes you to new cultures and ways of thinking, which can help to expand your mind and improve your abstract thinking skills. Plus, when you're in a new place, you're forced to think on your feet as you figure out how to navigate the unfamiliar landscape. This helps to build up your problem-solving skills, which are essential for developing abstract thinking abilities.

7. Get more exercise

Exercise is not only beneficial for your physical health, but it can also be beneficial for your mental health . Exercise helps to increase oxygen flow to the brain, which can improve cognitive functioning and help you think more clearly. Exercise also increases the production of endorphins, which can improve your mood and make it easier to focus on what you're doing.

8. Practice critical thinking

Critical thinking involves using your reasoning skills to evaluate information objectively. By practicing critical thinking, you can develop your abstract thinking ability by learning to analyze information, identify patterns and connections, and draw logical conclusions. Additionally, critical thinking will help you become more aware of your own biases so that you can make unbiased decisions.

9. Embrace risk-taking

Taking risks and engaging in activities that make you uncomfortable can help you practice abstract thinking. Stepping outside of your comfort zone forces you to think differently and create solutions to complex problems. It also requires you to push yourself beyond what is familiar and take a leap of faith as you learn new things .

10. Take up a new hobby

Hobbies like painting, sculpting, and photography can help you practice abstract thinking by allowing you to explore new ideas and ways of looking at the world. These activities also require you to use your imagination and creativity to devise solutions that aren’t immediately obvious. It also makes you feel accomplished when you're done, which can boost your confidence and make you more open to taking risks in other aspects of life.

Enhance your abstract thinking skills

If you've wondered, "What is abstract thinking?" now you have a better understanding. Abstract thinking skills can benefit us in many areas. From problem solving to meaningful learning to critical thinking, it's a powerful tool that can enhance our ability to navigate daily challenges.

By incorporating activities that promote the abstract thinking process into our daily routine, we can improve our ability to grasp abstract ideas, improve our decision-making skills, and see the bigger picture. With practice and dedication, we can master the art of abstract thinking and unlock its full potential.

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This chapter introduces the reader to abstract thinking and abstract intelligence. Abstract thinking is the ability to understand concepts that are real but not tied to concrete physical objects and experiences. In other words, abstract thinking is the ability to consider concepts beyond what we observe physically. Abstract intelligence is a form of driving force that transfers information into behaviors or actions. It is the ability to respond to words, numbers, letters, etc. It is the ability to carry on abstract thinking. It is a measure of one’s ability to reason and understand complex concepts and assimilate new information beyond previous experience.

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Sadiku, M.N.O., Musa, S.M. (2021). Abstract Intelligence. In: A Primer on Multiple Intelligences. Springer, Cham. https://doi.org/10.1007/978-3-030-77584-1_16

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Development of abstract thinking during childhood and adolescence: The role of rostrolateral prefrontal cortex

Iroise dumontheil.

a Department of Psychological Sciences, Birkbeck, University of London, UK

b Institute of Cognitive Neuroscience, University College London, UK

• • Rostral prefrontal cortex (RPFC) supports self-generated, abstract thought processing.
• • Flexibly attending towards and processing abstract thoughts develop in adolescence.
• • RPFC activation becomes more specific to relational integration during development.
• • Prospective memory development remains to be further studied using neuroimaging.
• • Training of abstract thinking, e.g. reasoning, may have implication for education.

Rostral prefrontal cortex (RPFC) has increased in size and changed in terms of its cellular organisation during primate evolution. In parallel emerged the ability to detach oneself from the immediate environment to process abstract thoughts and solve problems and to understand other individuals’ thoughts and intentions. Rostrolateral prefrontal cortex (RLPFC) is thought to play an important role in supporting the integration of abstract, often self-generated, thoughts. Thoughts can be temporally abstract and relate to long term goals, or past or future events, or relationally abstract and focus on the relationships between representations rather than simple stimulus features. Behavioural studies have provided evidence of a prolonged development of the cognitive functions associated with RLPFC, in particular logical and relational reasoning, but also episodic memory retrieval and prospective memory. Functional and structural neuroimaging studies provide further support for a prolonged development of RLPFC during adolescence, with some evidence of increased specialisation of RLPFC activation for relational integration and aspects of episodic memory retrieval. Topics for future research will be discussed, such as the role of medial RPFC in processing abstract thoughts in the social domain, the possibility of training abstract thinking in the domain of reasoning, and links to education.

1. Introduction

Abstract thoughts can be broadly defined as thoughts that are self-generated and stimuli-independent, in contrast to stimulus-oriented, perceptually-derived, information. Beyond this definition, two particular forms of abstraction can be considered (see Nee et al., 2014 ). Abstraction can be defined temporally: abstract thoughts are those that relate to long term goals, or past or future events. Alternately, abstraction can be defined relationally: abstract thoughts are those that focus on the relationships between representations rather simple stimulus features. A subset of cognitive processes has particularly high requirements of abstract thoughts manipulation, either within a single temporal or relational domain, or across both. These include the retrieval of past thoughts and memories (e.g. episodic or source memory retrieval), the manipulation of current task-related or task-unrelated self-generated information (e.g. relational reasoning and problem solving or mindwandering respectively) and the processing of thoughts linked to the future (e.g. planning, multitasking, prospective memory). Interestingly, the most anterior part of the lateral prefrontal cortex, the rostrolateral prefrontal cortex (RLPFC), has been found to show increased activations in paradigms testing this whole range of cognitive functions (e.g. see Badre, 2008 , Burgess et al., 2007a , Ramnani and Owen, 2004 for review). The rostral prefrontal cortex (RPFC), as other parts of the frontal cortex and the temporal cortices, shows prolonged structural development during adolescence (e.g. see Dumontheil et al., 2008 for review). The relationship between abstract thoughts and RPFC, in particular the RLPFC, during late childhood and adolescence will be the topic of this review.

Adolescence starts at the onset of puberty and can be broadly defined as between the ages of 10 and 19 ( Sawyer et al., 2012 ). Although brain and behavioural changes during this period are less pronounced than during infancy and childhood, adolescence is nevertheless an important period of development in terms of the acquisition of higher cognitive skills, as well as the onset of mental disorders (see Dumontheil et al. (2008) for a discussion of RPFC and developmental disorders). Adolescence emerges as a critical phase of reorganisation of regulatory systems, and may also be a period of extended brain plasticity and thus a relevant target for interventions ( Steinberg, 2005 ).

The first section of this paper will focus on the association between lateral RPFC and the ability to attend to and manipulate abstract thoughts. I will then discuss the development of this ability during late childhood and adolescence and how structural and functional development of RPFC may underlie the behavioural changes observed during adolescence. I will then briefly relate these findings to studies of the development of medial RPFC function in social cognition tasks. Finally, I will discuss future avenues of research in this field as well as potential implications of these findings for education policy and practice. This review will focus on aspects of both relationally and temporally abstract thoughts ( Nee et al., 2014 ), as identified from the research on RLPFC function in adults. Although an effort was made to gather relevant evidence, this review is unlikely to be exhaustive and is biased towards those fields where more developmental neuroimaging research has currently been published.

Recently Ferrer et al. (2009) summarised the development of fluid reasoning, which can be considered as a type of abstract thinking. Here the goal is to perform a more extensive review of the development of abstract thinking more generally, including recent studies on the topic. Although some aspects of metacognition are relevant to the domain of abstract thought and reasoning, there has been until now little cognitive neuroscience research done with a developmental focus (see Fleming and Dolan, 2012 , Fleming et al., 2010 ) and thus metacognition will not be reviewed here (see Schneider, 2008 for a review of the development of meta-cognitive knowledge).

2. Rostral prefrontal cortex function

2.1. rostral prefrontal cortex: cytoarchitecture and subdivisions.

RPFC, which corresponds approximately to Brodmann area 10 (BA10), is a large brain region in humans and is thought to be subdivided into separate subregions distinct in terms of cellular organisation and function ( Christoff and Gabrieli, 2000 , Gilbert et al., 2006a , Gilbert et al., 2006b ). Two quite different types of cognitive ability have been associated with the RPFC. The lateral parts of RPFC (RLPFC) appear to support the ability to detach oneself from the environment and to elaborate, evaluate and maintain abstract rules and information, as it is involved in reasoning, problem solving, and more generally abstract thinking ( Amati and Shallice, 2007 , Christoff and Gabrieli, 2000 , Christoff et al., 2009b , Gilbert et al., 2006b , Koechlin et al., 2003 , Ramnani and Owen, 2004 ) (see below for further details). The medial aspect of RPFC, or medial prefrontal cortex (MPFC), is implicated in social cognition, that is, the understanding of other people's minds ( Amodio and Frith, 2006 , Blakemore, 2008 , Van Overwalle, 2009 ).

In the last decade, large scale magnetic resonance (MRI) studies have shown that the RPFC is one of the last brain regions to reach maturity in humans (see Dumontheil et al., 2008 for review). This region is also particularly interesting in terms of its cellular organisation and connection with other regions. RPFC is the only prefrontal region that is predominantly interconnected with supramodal cortex in the PFC ( Andersen et al., 1985 , Petrides and Pandya, 1999 ), anterior temporal cortex ( Amaral and Price, 1984 , Moran et al., 1987 ) and cingulate cortex ( Andersen et al., 1985 , Arikuni et al., 1994 , Bachevalier et al., 1997 , Morecraft and Van Hoesen, 1993 ). In addition, its projections to these other regions are broadly reciprocal ( Passingham, 2002 ; see Ramnani and Owen, 2004 for review). RPFC has a low cell density, which may indicate that this region in humans has more space available for connections both within this region and with other brain regions ( Semendeferi et al., 2011 , Semendeferi et al., 2001 ). RPFC also has a particularly high number of dendritic spines per cell, an indicator of the number of synaptic connections, which suggests that the computational properties of RPFC are more likely to involve the integration of inputs than those of comparable areas ( Ramnani and Owen, 2004 ).

In line with these findings, Amati and Shallice (2007) proposed that RPFC may support a novel type of cognitive computational process required for “abstract projectuality”, that may be behind the cognitive capacities specific to modern humans. They propose that this brain operation permits a fluent sequence of non-routine computational operations to occur over a prolonged timecourse. This qualitatively different type of brain operation may have emerged from increasing prefrontal cortical connectivity in the RPFC, induced by gradual (quantitative) genetic changes affecting RPFC structure and organisation over evolution ( Amati and Shallice, 2007 ). This model fits well with current theories of RLPFC function which will be detailed in the next section.

2.2. RLPFC and abstract thinking

A number of theories of the functional organisation of the frontal lobes have been proposed in the last decade based on neuroimaging and lesion data. The broad consensus is that the frontal cortex may possess a rostro-caudal organisation whereby more rostral regions support cognitive control involving progressively more abstract representations ( Azuar et al., 2014 , Badre and D’Esposito, 2007 , Badre and D’Esposito, 2009 , Badre, 2008 , Botvinick, 2008 , Christoff et al., 2009b , Koechlin and Jubault, 2006 , Koechlin and Summerfield, 2007 , Koechlin et al., 2003 , Petrides, 2005 ). In this organisation, posterior PFC supports the control and manipulation of temporally proximate, concrete action representations, while anterior PFC supports the control of temporally extended, abstract representations ( Badre, 2008 ). Fig. 1 , adapted from Badre (2008) , shows a representation of this organisation. Of interest here is the position of the RLPFC, at the top of this frontal lobe hierarchy, and the suggestion that this brain region is recruited when temporally extended, abstract representations are attended to or manipulated.

Sub-divisions of the frontal lobes. (a) Schematic representation of the major anatomical sub-divisions of the frontal lobes. Following a caudal to rostral direction, labelled areas include motor cortex, dorsal and ventral premotor cortices, dorsal and ventral aspects of anterior premotor cortex, ventrolateral prefrontal cortex (VLPFC), dorsolateral prefrontal cortex (DLPFC), and lateral frontopolar cortex, also termed rostrolateral prefrontal cortex (RLPFC). Boundaries and Brodmann areas (BA) are approximate. (b) Schematic representation of the rostro-caudal gradiant of the organisation of the prefrontal cortex. The consensus among diverse theoretical accounts of the organisation of the PFC is that progressively more anterior PFC regions support cognitive control of progressively more abstract and temporally extended representations (adapted from Badre, 2008 ).

RLPFC indeed shows increased blood oxygen level dependent (BOLD) signal in a number of tasks that require such aspects of cognition, including the retrieval of episodic or source memory (e.g. Dobbins et al., 2004 , Turner et al., 2008 ; see Gilbert et al., 2006b for review and Spaniol et al., 2009 for meta-analysis); prospective memory ( Barban et al., 2013 , Benoit et al., 2011 , Burgess et al., 2007b ); the manipulation of highly abstract information ( Christoff et al., 2009b ); the selection and maintenance of task rules ( Bengtsson et al., 2009 , Braver et al., 2003 , Dumontheil et al., 2011 , Sakai and Passingham, 2003 , Sakai and Passingham, 2006 ); sub-goal processing or branching ( Badre and D’Esposito, 2007 , Braver and Bongiolatti, 2002 , Koechlin et al., 2003 ); integration of information ( Badre and Wagner, 2004 , Wolfensteller and von Cramon, 2011 ); analogical and relational reasoning ( Bunge et al., 2009 , Geake and Hansen, 2005 , Hampshire et al., 2011 , Smith et al., 2007 , Volle et al., 2010 , Wendelken et al., 2008 , Wendelken et al., 2012 , Wright et al., 2008 ) – although note that medial dorsal RPFC has also been implicated in analogical reasoning ( Green et al., 2006 , Krawczyk, 2012 , Volle et al., 2010 ); reality monitoring ( Simons et al., 2008 ); and mind-wandering ( Christoff et al., 2004 , Christoff et al., 2009a , Dumontheil et al., 2010a , Schooler et al., 2011 ).

Lesion studies also provide supporting evidence for a role of RPFC in the control of temporally extended abstract representations, although, by their nature, these studies rarely distinguish between lateral and medial aspects of RPFC, and therefore between the social cognition and cognitive control aspects of RPFC function ( Burgess, 2000 , Burgess et al., 2009 , Gläscher et al., 2010 , Roca et al., 2010 , Shallice and Burgess, 1991 , Volle et al., 2011 ).

3. Behavioural studies of the development of abstract thinking

Abstract thinking encompasses a number of different cognitive processes, but one definition adopted here is that abstract thinking can be considered as the manipulation of self-generated thoughts, or thoughts that are not directly connected to the environment. A distinction is made between relationally and temporally abstract thoughts. As described above, neuroimaging and lesion studies in adults suggest that RLPFC is thought to be specifically involved in the elaboration, evaluation and maintenance of abstract rules ( Amati and Shallice, 2007 , Christoff and Gabrieli, 2000 , Christoff et al., 2009b , Koechlin et al., 2003 , Ramnani and Owen, 2004 ), as well as in the ability to flexibly control whether one selectively attends towards self-generated thoughts or the environment ( Burgess et al., 2007a ), whether this self-generated information is task-relevant, or task-irrelevant, i.e. when the mind wanders ( Christoff et al., 2004 , Christoff et al., 2009a , Dumontheil et al., 2010a ). A number of theorists have suggested that adolescents can operate at a new and more abstract level of thought because they can integrate the results of two different sorts of lower-order processing ( Case, 1985 , Fischer, 1980 , Halford, 1982 ). This new intellectual potential emerging in adolescence builds on the idea that children can progressively handle first one new abstract element, then two, and then multiple abstract elements simultaneously (see Marini and Case, 1994 , for review). Below are described behavioural studies investigating the development of the ability to flexibly attend towards self-generated thoughts, the development of the ability to reason logically and integrate relations or representations, and finally the development of the processing of self-generated thoughts that can be considered temporally abstract, and are related to past experiences (episodic memory) or future events (prospective memory). Although multitasking, or branching, has been a particular focus of neuroimaging and lesion research on RLPFC function in adults ( Badre and D’Esposito, 2007 , Braver and Bongiolatti, 2002 , Burgess, 2000 , Koechlin et al., 2003 ), this topic has not been specifically investigated in developmental psychology research.

3.1. Development of the flexible selection of self-generated thoughts

An important aspect of the manipulation of abstract thought resides in the ability to modulate the balance between cognition that is provoked by perceptual experience (stimulus-oriented, SO) and that which occurs in the absence of sensory input (self-generated, or stimulus-independent, SI) ( Burgess et al., 2007a ). In children, manipulation of SI thoughts has been studied in the context of fluid intelligence and relational reasoning ( Crone, 2009 , Wright et al., 2008 ; see below) and working memory (WM) tasks ( Crone et al., 2006 ), while the ability to resist distracting SO information has been studied in perceptual ( Booth et al., 2003 , Bunge et al., 2002 ) and WM tasks ( Olesen et al., 2007 ). In this latter study 13 year-old participants showed poorer accuracy than adults in visuospatial WM trials that included distraction relative to trials that did not.

In a recent study ( Dumontheil et al., 2010b ), we tested 179 female participants aged 7–27-year old on a single task (Alphabet task) that could be performed on the basis of either SO or SI information, without high working memory requirements ( Gilbert et al., 2005 , Gilbert et al., 2007 , Gilbert et al., 2008 ). Participants were asked to classify letters of the alphabet according to whether the upper case letter contained a curve or not. In SO blocks consecutive letters of the alphabet were presented on the screen, while in SI blocks either no letter (No-distractor condition) or distracting non-consecutive letters (Distractor condition) were presented on the screen. In SI blocks participants were asked to continue going through the alphabet sequence in their head and continue responding (see Fig. 2a ). Different patterns of development were observed for the different aspects of this task. Resistance to visual distractors exhibited small improvements with age, both in accuracy and speed of responding, while the manipulation of SI thoughts and switching between SI and SO thoughts showed steeper response speed improvements extending into late adolescence (see Fig. 2b ). This development in the speed of manipulating self-generated thoughts and in the speed of switching between perceptually-derived and self-generated thoughts may underlie improvements during adolescence in planning, reasoning and abstract thinking, abilities that rely on the manipulation of thoughts that are not directly derived from the environment ( Anderson et al., 2001 , De Luca et al., 2003 , Huizinga et al., 2006 , Rosso et al., 2004 ). Below is described in more detail the particular case of the development of reasoning.

Development of the flexible switching between selecting thoughts derived from the environment and abstract thoughts. (a) Alphabet task. Participants classify letters of the alphabet according to their shape (line or curve). When the letter is red, participants judge the letter presented on the screen (stimulus-oriented (SO) blocks). When the letter is blue (or when there is no letter) participants continue reciting the alphabet in their head and judge the shape of the letter in their head (stimulus-independent (SI) blocks), while ignoring the distracting letter presented on the screen (Distractor condition), or in the absence of a letter on the screen (No-distractor condition). Performance in the two types of blocks (SI vs. SO) and the two conditions (Distractor vs. No-distractor), and performance in switch trials (first trial of a SO or SI block) and subsequent trials (stay trials) were compared. (b) Behavioural results. The speed of responding in SI vs. SO, and in switch vs. stay trials continued to increase during adolescence. The speed of responding in the presence of Distractors also improved but followed a flatter linear developmental function (adapted from Dumontheil et al., 2010b ). (c) Functional MRI results. The main effect of switching between SO and SI conditions vs. a simple change of colour of the stimuli over the whole age range is presented (family-wise error corrected p < .05), highlighting the right superior RLPFC activation (top). RLPFC activity in this contrast is plotted against age (bottom). There was a significant decrease in activity during adolescence, which was not purely a consequence of differences in performance and brain structure between the participants and could reflect the maturation of neurocognitive strategies (see Dumontheil et al., 2010b ). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.2. Development of logical reasoning

Problem solving by analogy requires the transfer of previously acquired solutions or strategies from one context or situation to another. Preschoolers (e.g. Holyoak et al., 1984 ) and even infants (e.g. Chen et al., 1997 ) exhibit an ability to draw analogies and use a solution learned from a one problem to solve another problem. However older children are better able to detect the underlying similarities between the original problem and the novel problem situation (e.g. Chen and Daehler, 1992 , Daehler and Chen, 1993 , Holyoak et al., 1984 ; see Chen et al., 1997 for review). Experimental paradigms have tended to be action-based, requiring children to perform a particular action to achieve a goal. However, analogical reasoning is also assessed using verbal or pictorial stimuli in propositional analogy tasks ( Ferrer et al., 2009 ), for example asking children to match the sequence “bread: slice of bread:: orange:?” with one of the following options: slice of orange, slice of cake, squeezed oranges, orange balloon, orange basketball. The relational shift hypothesis proposes that young children interpret analogy and metaphor first in terms of object similarity, and then in terms of relational similarity. Support for this hypothesis is given for example by the observation that when relational similarity competes with object similarity, young children make object-similarity responses, while with increasing age/experience responses become in line with relational similarity ( Rattermann and Gentner, 1998 ). This relational shift is thought to be not simply age-determined, but knowledge-related, which means it can occur at different ages in different domains. However, adults continue to use both object commonalities and relational commonalities in processing comparisons (see Rattermann and Gentner (1998) for discussion). In a recent computational study, Morrison et al. (2011) propose that the development of analogical reasoning during childhood is best explained by a combination of improved information processing, in particular working memory (which supports the maintenance of a greater number of relations) and inhibitory control (which supports the resistance to distraction by object commonalities), in combination with knowledge accretion.

Subsequent developmental changes have been observed during adolescence. Marini and Case (1994) show that a capacity for abstract reasoning begins to emerge in both social and non-social domains about the age of 11 or 12 and that further development of this ability is constrained by the number of abstract elements that can be coordinated at one time, independent of the particular content of these abstract elements. The task used required participants to predict the movement of a beam where both the weight and distance from the centre were relevant factors to be combined, or to predict a character's behaviour based on personality traits abstracted from a scenario. Similarly, Hatcher et al. (1990) observed development of abstract thinking between ages 10, 13 and 17-year old, using the balance beam task and a verbal analogical reasoning task. Using conditional reasoning (if… then… statement) tasks, De Neys and Everaerts (2008) showed that improvements in conditional reasoning observed during adolescence were not only related to the start of the formal reasoning stage around age 12, but also depended on the ability to retrieve alternatives from memory and to inhibit these alternatives when necessary. The authors note that according to other studies (see De Neys and Everaerts, 2008 , for review) not all adolescents will show this ability to inhibit alternatives when they are irrelevant, leading to individual differences in conditional reasoning in adulthood.

These studies therefore suggest that logical reasoning depends on the interplay of the ability to maintain and manipulate information in working memory, the inhibition of irrelevant or incorrect alternatives, and domain-specific knowledge, in addition to the requirements of integrating multiple abstract representations.

3.3. Behavioural measures of relational reasoning development during adolescence

Although, as discussed above, relational processing can be recruited for analogical reasoning, a number of studies have focused more specifically on relational reasoning per se. The relational reasoning demands of a problem can be defined in terms of the number of dimensions, or sources of variation, that need to be considered simultaneously to reach a correct solution. Children under 5 years can solve 0- and 1-relational problems, but fail to solve 2-relational problems ( Halford et al., 1998 ). Early improvements in relational reasoning may reflect a shift from a focus on object similarity to relational similarity ( Rattermann and Gentner, 1998 ). Further improvements during childhood and adolescence may relate to increased relational knowledge or increased working memory capacity ( Crone et al., 2009 , Sternberg and Rifkin, 1979 ; see Richland et al., 2006 , for discussion). Indeed, Carpenter et al. (1990) argued that the processes leading to individual differences on relational reasoning tasks such as the Raven's matrices ( Raven, 1998 ) are primarily the ability to extract abstract relations and to dynamically manage a large set of problem-solving goals in working memory. Thus, for relational reasoning as for logical reasoning, working memory is thought to play an important role in supporting the maintenance of multiple abstract thoughts to allow their comparison and integration.

Prolonged developmental changes in relational reasoning into adolescence have been observed in a few behavioural studies (see also the next section on neuroimaging studies). For example, although their age groups were small, Rosso et al. (2004) showed that accuracy in the matrix reasoning section of the WAIS-III increased with age in the range 9–19-year old. We recently employed a relational reasoning task initially developed by Christoff et al. (2003) , to investigate relational reasoning development during adolescence in a large sample of healthy participants ( Dumontheil et al., 2010c , Experiment 1). The Shapes task required participants to assess whether two pairs of items, which could vary in shape and/or texture, differed or changed along the same dimension. The pairs of items could both show texture differences or both show shape differences, in which case participants were asked to response yes, i.e. the pairs change along the same dimension (match). Alternatively, one pair of items differed in texture while the other pair differed in shape, in which case participants were asked to respond no, i.e. the pairs change along different dimensions (no-match). One hundred and seventy nine female participants aged 7–27-year old participated in the study (same participant as Dumontheil et al. (2010b) ). When comparing the relational integration (or 2-relational) condition of the task to a condition requiring the processing of only 1-relation (either shape, or texture), the results showed a non-linear pattern of improvement in accuracy across age. After an early improvement in accuracy, with 9–11-year olds performing at adult levels, performance dipped in the 11–14-year olds and gradually improved again to adult levels throughout late adolescence. Further analysis of these data using a combined measure of reaction time over accuracy to take into account a potential speed-accuracy trade-off suggests that in fact 2-relational vs. 1-relational performance in this task improved progressively during late childhood and mid-adolescence, with a significant improvement between the 7–9 and 14–17 years old age groups on this combined measure.

3.4. Development of episodic memory

Episodic memory refers to memories for specific episodes previously experienced. Memories for such events are often accompanied by the phenomenal experience of recollective experience ( Tulving, 1983 ). Sander and colleagues have proposed that episodic memory relies on the combination of an associative and a strategic processing component ( Sander et al., 2012 ). Raj and Bell (2010) have reviewed the development of episodic memory formation in childhood extensively and similarly contrast binding and source memory to source monitoring. It is generally believed that by the age of 4 years, children have an episodic memory system in place ( Raj and Bell, 2010 ). The associative component, which relies primarily on mediotemporal and posterior brain regions (e.g. Simons and Spiers, 2003 ; see Raj and Bell, 2010 for review) is relatively mature by middle childhood ( Gathercole, 1998 , Rhodes et al., 2011 ). However, some studies still show continuing improvements in episodic memory performance between late childhood and adulthood ( DeMaster and Ghetti, 2013 , Lorsbach and Reimer, 2005 ), in particular in tasks requiring memory for combined features (e.g. objects and locations) ( Lorsbach and Reimer, 2005 ).

In contrast, the strategic component, which refers to top-down control processes involved in the organisation and monitoring of memory representations mainly relies on prefrontal brain regions ( Miller and Cohen, 2001 ), particularly for tasks requiring binding of feature information and source memory retrieval. This component shows more prolonged development in childhood, adolescence and until young adulthood. For example, in a longitudinal study following children between 4 and 10 years of age, different developmental timecourses were observed for the memory for individual items vs. a combination of source and facts ( Riggins, 2014 ). Overall, younger children perform worse than adolescents on source discrimination tasks, and adolescents perform themselves worse than adults ( De Chastelaine et al., 2007 , DeMaster and Ghetti, 2013 , Ghetti et al., 2010 ). Adults also perform better than children and adolescents on tasks requiring a recollection judgement, i.e. requiring the specific contextual details of a memory episode, but not in tasks requiring a recognition judgement, i.e. knowing that an item has been previously encountered ( Billingsley et al., 2002 , Ofen et al., 2007 ). Sander et al. (2012) showed that, similarly to adults, children and adolescents could benefit from mnemonic instruction and training in an episodic memory task, highlighting the role of strategy implementation in episodic memory performance.

Executive function (EF) abilities have been suggested to play a role in episodic memory performance. Indeed, higher EF scores are associated with better performance on source memory tests, and lower rates of source memory errors, particularly lower false alarm rates. Frontal lobe function may support the integration of item and source information, content and context, during encoding, and may also support contextual memory retrieval by guiding the search and monitoring processes and inhibition of feelings of familiarity (see Raj and Bell, 2010 for review). The specific role of RLPFC in episodic memory may be in supporting the coordination of search and monitoring processes during episodic memory retrieval ( Spaniol et al., 2009 ), with BOLD signal increases in RLPFC possibly specific to intentional rather than incidental retrieval ( Fletcher and Henson, 2001 , Simons and Spiers, 2003 ).

Little research has been done to investigate the role played by EF during episodic memory development. In young children (4 and 6 years old), Rajan et al. (2014) found that language ability, and a composite measure of EF (combining inhibitory control, working memory and set shifting) uniquely predicted fact and source memory retrieval, however when the EF measures were considered individually, the only significant association was that inhibitory control predicted source recall. Rhodes et al. (2011) found that 10 and 11-year old children, but not 8 and 9-year olds, showed a relationship between episodic memory and verbal working memory, which differed from the observed relationship between episodic memory and spatial working memory in adults, and thus suggested that the relationship between episodic memory and executive (frontal) components of episodic memory retrieval changed over the period of adolescence. Picard et al. (2012) also found that EF contributed to changes in temporal and spatial context aspects of episodic memory during adolescence. Ruffman et al. (2001) found that in children aged 6, 8 and 10 years old, working memory was related to accuracy in source monitoring judgements, while inhibitory control uniquely predicted false alarm rates.

3.5. Development of prospective memory

Prospective memory (PM) is the ability to “remember to remember”, and is particularly difficult when an individual is simultaneously engaged in other activities. Research suggests that active strategical monitoring is more likely to be required when the PM cues are non-focal, non-distinctive, when the task is non-demanding and non-absorbing, when high importance is given to the PM task and the interval retentions are short ( McDaniel and Einstein, 2007 ). Although a number of studies have now investigated the development of PM in childhood, fewer studies have investigated later development during adolescence ( McDaniel and Einstein, 2007 ).

Event-based PM can be observed in preschool aged children (e.g. Guajardo and Best, 2000 ), however performance tends to be poor when the ongoing task needs to be interrupted (e.g. Kliegel et al., 2008 ) or when the cue is non-focal, suggesting that children aged 5 or younger have not developed strategic monitoring processes or do not have the attentional resources to deploy them during ongoing task performance (see also McDaniel and Einstein, 2007 for review). Event-based PM continues to develop as children become more able to use external reminders to cue prospective remembering and to interrupt ongoing task performance when necessary ( Kliegel et al., 2008 ). Time-based PM requires greater strategic monitoring than event-based PM. Although time-based PM has also been observed in young children (5–7-year olds, Aberle and Kliegel, 2010 ), it tends overall to be associated with poorer performance than event-based PM (e.g. in 7–12-year-olds Yang et al., 2011 ). Time-based PM has been shown to continue to develop in late childhood and early adolescence ( Yang et al., 2011 ) as children become increasingly proficient at using time-checking strategies ( Kerns, 2000 , Mackinlay et al., 2009 , Voigt et al., 2011 ).

Developmental changes in PM performance are also observed further into adolescence, with more correct event-related PM responses made by adults than adolescents (aged 12 in Zöllig et al. (2007) ; aged 14 in Wang et al. (2006) ; but no difference observed with 13–14-year olds in Zimmermann and Meier (2006) ). In a large online study, Maylor and Logie (2010) found (using a single event-based PM trial) that performance peaked in late adolescence (16–19-year old) and that females outperform males in early adolescence. Ward et al. (2005) showed that adolescents detected more PM cues than children, with similar performance to adults, however they relied more than adults on a remembering strategy described as “Thought about all the time/looked out for the cues”, while adults used more frequently a strategy described as “Remembered only when saw the cues”. This indicates that to achieve a similar performance, adolescents needed to use a more active monitoring strategy than the adults. In a realistic time-based PM task requiring participants to remember to take baking cakes out of an oven while playing a video game, 14-year-olds were better than 10-year-old s , even though both age groups were able to deploy strategic clock-monitoring strategies ( Ceci and Bronfenbrenner, 1985 ). Consistent with the greater need for strategic monitoring, the development of PM abilities is mainly observed during adolescence when non-focal cues are used ( Wang et al., 2011 ).

The realisation of delayed intentions is thought to rely on a prospective component, the detection or recognition of prospective cues, but also a retrospective component, the retrieval of an intention from memory following the recognition of a prospective cue ( Simons et al., 2006 ). The retrospective component is likely to share many of the processes that support episodic memory, in particular the retrieval of contextual information from long-term memory. Zöllig et al. (2007) found that adolescents made more confusion errors than young adults, which the authors argue indicates that the retrospective component of PM is less efficient in adolescents. Similarly, Yang et al. (2011) report that 7–8-year-olds missed PM cues more often than 11–12-year olds, while 9–10-year olds showed a higher frequency of confusion (false-alarm and wrong responses) than 11–12-year olds suggesting differential developmental patterns of the PM and retrospective memory components. Maylor and Logie (2010) similarly observed earlier development of PM performance compared to retrospective memory performance in a lifespan study.

Successful PM is thought to rely on a range of other executive skills, however evidence is mixed regarding which aspects of EF are most relevant to PM development. A few studies have investigated this with time-based PM tasks. Aberle and Kliegel (2010) found that PM performance in 5–7-year olds was associated with processing speed and working memory. In older, 7–12-year old children, Mackinlay et al. (2009) found that the majority of the developmental changes in PM performance could be explained by planning and task switching performance measures, while Mäntylä et al. (2007) found children aged 8–12-year old achieved similar accuracy to adults in a time-based PM task by checking the clock more often, and that while in children inhibition and updating (within a single “supervision” factor), but not shifting, predicted clock monitoring frequency, in adults they predicted timing error.

To summarise, similarly to the investigations of logical and relational reasoning, these studies highlight the role of working memory in supporting temporally abstract thinking. In addition, good performance on prospective and episodic memory tasks may depend on the use of appropriate strategies, themselves dependent on the ability to extract and evaluate abstract information regarding task rules, goals and performance monitoring. It is this higher level of abstraction, either in the relational or temporal domain, which is thought to be specific to RLPFC ( Badre, 2008 ).

4. Functional neuroimaging studies of abstract thinking development

This section reviews the functional MRI findings on the development of abstract thinking during adolescence. The focus will first be on research on relationally abstract thinking, reviewing studies which have investigated the orientation of attention towards self-generated thoughts and the manipulation and integration of relations. Second, I will discuss findings related to the processing of temporally abstract thoughts, reviewing studies of episodic memory retrieval and prospective memory, although the evidence is more limited for the latter.

4.1. Neuroimaging study of the development of the flexible selection of self-generated thoughts

On the basis of studies in adults, Burgess et al. (2007a) have suggested that RPFC supports the flexible orientation of attention towards perceptually-derived information or self-generated thoughts. In a recent study, the Alphabet task described above, which contrasts SI and SO phases with very similar task requirements, was tested in a smaller group of participants aged 11–30 years old using functional MRI (fMRI). Two comparisons were performed using this task ( Dumontheil et al., 2010b ): SI vs. SO thought manipulation and switches between SO and SI phases versus switches of the colour of the letter stimuli. In this sample of 37 participants, the difference in performance between SI and SO trials did not change with age, however participants did become faster in the SO/SI switch trials with age. The comparison of SI vs. SO thought manipulation led to increased BOLD signal in a large fronto-parietal network of regions that extended into RLPFC bilaterally. Among this network, only the left anterior insula showed developmental changes, with a decrease in activation with age, which was independent of individual differences in performance. The comparison of SO/SI switches versus Colour switches led to a much smaller network of brain regions including the right superior RLPFC, precuneus and superior temporal gyrus ( Fig. 2c ). In this comparison only the RLPFC cluster showed a trend for a decrease in activation with age, similarly not accounted for by individual differences in performance ( Fig. 2c ).

4.2. Neuroimaging studies of visuospatial relational reasoning development

Neuroimaging studies in adults have shown that a fronto-parietal network of brain regions is recruited during relational integration, i.e. when solving 2-relational problems, with activation in RLPFC, and in particular left RLPFC, specific to relational integrational demands ( Bunge et al., 2009 , Christoff et al., 2003 , Smith et al., 2007 , Wendelken et al., 2012 ). Four recent studies have investigated the development of relational reasoning between late childhood and adolescence or adulthood using fMRI ( Crone et al., 2009 , Dumontheil et al., 2010c , Eslinger et al., 2009 , Wendelken et al., 2011 ). These four studies used paradigms of relational processing in the visuospatial domain. Dumontheil et al. (2010c) and Wendelken et al. (2011) used very similar tasks and compared 2-relational (i.e. relational integration), 1-relational, and fixation conditions. Crone et al. (2009) used problems derived from the Ravens Progressive Matrices ( Raven, 1998 ) and included an additional 0-relational condition and a simple orientation of arrows task as baseline. Eslinger et al. (2009) used coloured geometrical shape sequences as stimuli and compared 2-relational and 1-relational conditions.

In terms of behaviour, Crone et al. (2009) found that 8–12-year old made more errors, but were not slower, than 18–25-year olds in 2-relational than 1-relational trials; Dumontheil et al. (2010c, Experiment 2) found that 11–14-year olds responded faster than 14–18-year olds in 2-relational than 1-relational trials, but neither group differed from the adult group, and there was no age group difference in accuracy; Wendelken et al. (2011) did not observe age differences in 2-relational vs. 1-relational performance over the age range of 7–18-year old using age as a continuous variable; Eslinger et al. (2009) do not report analyses of performance changes in the 8–19-year age range they studied. Thus the performance findings are mixed in these studies and performance was typically included as a covariate in the analyses.

Neuroimaging results of the first three studies, with a particular focus on the RLPFC findings, are described in Fig. 3 . Crone et al. (2009) found increased specificity for 2-relational vs. 1-relational problems between childhood and adulthood in the left RLPFC ( Fig. 3a ) in the later part of the trial period, and increased specificity for 2-relational vs. 1-relational problems with age within the child group, aged 8–12-year old. Performance was not included as a covariate in these analyses, however the authors suggested that the fact that the left RLPFC in children showed increased BOLD signal in 2-relational trials compared to 1-relational in the initial part of the trial may be associated with the poorer performance observed in children in 2-relational trials. Dumontheil et al. (2010c) observed a trend for an increase in activation in the left RLPFC in 2-relational vs. 1-relational trials between early - and mid-adolescence, and a subsequent decreased activation in this region between mid-adolescence and adulthood ( Fig. 3b ). The early- to mid-adolescence increase did not remain when performance was included as covariates, while the mid-adolescence to adulthood increase was only partially accounted for by accuracy differences. Wendelken et al. (2011) found decrease activation with age in 1-relational trials in the left RLPFC, which led to increased activation in 2-relational vs. 1-relational trials between the ages of 6 and 18 years old ( Fig. 3c ). This developmental effect remained significant when performance was covaried. Finally, Eslinger et al. (2009) report increases with age between late childhood and adolescence in the parietal cortex bilaterally and decreases in age across large parts of the frontal cortex, but no specific findings in RLPFC. The development of the relational integration of semantic stimuli will be described below, before a possible general pattern of developmental change observed in these studies is discussed.

Increased specificity of left RLPFC activation for relational integration (2nd order vs. 1st order relational processing) during development. Although the three studies summarised here used slightly different tasks, methods and age groups, the overall pattern shows an increased specificity of left RLPFC activation, in particular between late childhood and mid-adolescence. (a) RLPFC activation observed in adult ( N = 17, age 18–25) and children ( N = 15, age 8–12) performing problems following the general form of the Raven Progressive Matrices test ( Raven, 1998 ), with a varying number of dimensions to be integrated. On the left are shown activations related to 1st order relational processing (REL-1 > REL-0) and relational integration (REL-2 > REL-1) in adults ( p < .001 uncorrected) and children ( p < .005 uncorrected) in the 8–16 s interval of a timecourse analysis. On the right are plotted the timecourses of activation from left RLPFC regions of interset in adults and children. In the later part of the timecourses, there was a significant interaction between age group and condition (grey highlight), with activations greater in REL-2 than REL-1 in adults, and greater in REL-1 than REL-0 in children (adapted from Crone et al., 2009 ). (b) Left RLPFC activation observed in three groups of children and adolescents (total N = 85) performing a task requiring 1st or 2nd order visuospatial relational processing. Analyses using age as a continuous variable show a significant decrease in left RLPFC associated with 1st-order relational processing only, resulting in a significant age × condition interaction (adapted from Wendelken et al., 2011 ). (c) Left hemisphere activation observed in a group of adult ( N = 13, age 22–30) and adolescent ( N = 24, age 11–18) participants performing a similar task to (b). In the left RLPFC, Relational > Control activation, i.e. that specific to 2nd vs. 1st order relational processing, increased marginally between early and mid-adolescence (#), while it decreased between mid-adolescence and adulthood (*) (adapted from Dumontheil et al., 2010a , Dumontheil et al., 2010b , Dumontheil et al., 2010c ).

4.3. Development of relational integration of semantic stimuli

Another study also investigated the development of relational integration, however the paradigm was an analogical reasoning task requiring the integration of semantic information ( Wright et al., 2008 ). Stimuli were pictures of objects. In the analogical condition participants were , for example , presented with a bee and a bee's nest, and a spider, and had to pick the correct matching object (a spider's web) among other items. In the control semantic condition the participant had to pick the most closely related object to a presented target object (e.g. a baseball for a baseball bat). A group of 6–13-year old children and a group of 19–26-year old adults participated in this study. The children/young adolescents were overall slower and made more errors than the adults, and also made disproportionally more errors in the analogical problems. In addition, children's RT was affected to a greater extent than adults by lure which were semantically vs. perceptually related to one of the stimulus items. Overall the comparison of analogical and semantic problems did not show increased BOLD signal in RLPFC. However, further analyses showed (1) increasing RLPFC activation with age in children both for semantic and analogical problems, and (2) in adulthood, greater RLPFC activation in the right RLPFC associated with greater accuracy in analogical problems. The authors argue this suggests that RLPFC is first increasingly involved in the processing of 1-relational (semantic) and 2-relational (analogical) problems, while in adulthood, its activation becomes more specific to relational integration, i.e. the analogical problems. In addition, Wright et al. (2008) similarly to Crone et al. (2009) observed timecourse differences in RLPFC activity between the children and the adults, with respectively later and more prolonged activation observed in children.

The use of a paradigm recruiting the manipulation of semantic relations raises the question of the role of verbal abilities in relational reasoning, including visuospatial reasoning. As discussed below, a recent study investigated the domain specificity of relational integration ( Wendelken et al., 2012 ), comparing visuo-spatial and semantic variants of the Shapes task described above. The results indicated that both tasks recruited left RLPFC specifically for the relational integration condition vs. the processing of two relations without integration. This left hemisphere-specificity of relational integration activity may be related to a verbal recoding during relational reasoning. In terms of development, it has been shown that after age 7 children tend to recode visuospatial or pictorial information in a verbal format in working memory tasks ( Conrad, 1971 , Flavell et al., 1966 ), and that these processes are related to their use of self-regulatory private speech ( Al-Namlah et al., 2006 ). This shift to phonological recoding has been suggested to be part of a general transition towards verbal mediation of cognitive processes ( Ford and Silber, 1994 , Hitch et al., 1991 ). Articulatory suppression has been shown to affect performance of executive functions tasks more broadly (e.g. in task switching ( Baddeley et al., 2001 ), or Tower of London tasks ( Wallace et al., 2009 )) and a diminished use of inner speech among individuals with autism spectrum disorders is thought to contribute to the executive dysfunction associated with these disorders ( Wallace et al., 2009 , Whitehouse et al., 2006 ). In addition, a large-scale lesion study in adults showed that performance deficits on the Raven's Colored Progressive Matrices, which is considered to be a non-verbal test of reasoning, were associated with lesions in temporal regions essential for language processing, as well as in the left inferior parietal lobule ( Baldo et al., 2010 ).

Therefore, current results suggest that relational reasoning in adults relies on verbal recoding of the relations and specific activations in the left RLPFC, however whether verbal recoding becomes more prevalent with age during relational reasoning, as in certain EF tasks, has not yet been investigated, and more research will be necessary to further explore these issues.

4.4. Increasing specificity of RLPFC activation for relational integration during development

A common overall pattern of the studies described above was of an increased activation in 2-relational problems vs. 1-relational problems between childhood and adolescence, which may be specific to the left RLPFC. However, this pattern of increased specialisation may be similar in a broader network of brain regions. Indeed, Crone et al. (2009) found that left dorsolateral prefrontal cortex (DLPFC) and left parietal cortex showed similar increased specialisation of activation for 2-relational trials vs. 1-relational trials when comparing children and adults. Wendelken et al. (2011) also found increased specialisation, although weaker, in bilateral intraparietal lobules, but not in the DLPFC. When comparing adolescents to adults Dumontheil et al. (2010c) did not find age effects in either DLPFC or parietal cortex. It is possible that only more sensitive analyses looking at BOLD signal timecourse or including a large number of children and adolescent participants may be able to pick up specialisation of brain activation in these regions.

It is as yet unclear how much this increased specialisation may relate to changes in accuracy and reaction times in 2-relational trials. However, the pattern suggests specialisation of left RLPFC, and potentially DLPFC and parietal cortex for relational integration compared to relational processing during adolescence. Only one of these studies compared later adolescence to adulthood and the findings showed decreased activation in the 2-relational vs. 1-relational comparison ( Dumontheil et al., 2010c ), which was partly related to accuracy differences between these age groups.

The pattern of increasing specialisation of brain activation for relational integration was driven in some studies by decreasing activation for relational processing, which highlights the complexity of investigating fMRI data developmentally. In particular, it is unclear whether increased activation (e.g. in WM task, Klingberg et al., 2002 ) or decreased activation (e.g. in response inhibition tasks, Tamm et al., 2002 ) reflect “more efficient” neural processing. One interpretation is that increased activation reflects greater specialisation of the brain region for a particular cognitive process, while decreased activation may reflect the fact that with more efficient neural processing in other brain regions or increased connectivity between regions, a particular brain region is no longer necessary for a particular cognitive process (e.g. RLPFC for the processing of single relations). In this context, as is true in general for fMRI studies, the specific contrast investigated is particularly relevant, for example whether one is contrasting relational integration (2-Rel) to relational processing (1-Rel) or to a fixation control condition. Although RLPFC did not show an increased BOLD signal during a Raven reasoning task at the corrected threshold used, a recent study in adults by Perfetti et al. (2009) speaks to the fact that lower performance or abilities overall may be associated with less specific brain activations in fronto-parietal regions. Comparing high and low fluid intelligence (gf) participants, Perfetti et al. (2009) found that while the high gf group showed increased fronto-parietal activation in the analytical (more complex) problems compared to the figural problems, the low gf group showed greater activations in the figural condition than the high gf group, and a tendency for the activations in the analytical condition to be lower than in the figural condition. In the visual analogy task described above, Wright et al. (2008) found that in adults the specificity of RLPFC activations for relational integration was positively correlated with accuracy on the task. In another study, it was shown that high gf participants showed greater parietal activations than low gf participants in a relational integration task ( Lee et al., 2006 ). This later result highlights the importance of processing in brain regions other than RLPFC for the performance of relational integration. The parietal cortex has been suggested to support the identification of the visuo-spatial relations that are the basis of relational integration ( Ferrer et al., 2009 ).

In summary, fMRI studies have demonstrated changes in RLPFC activation during adolescence during the manipulation and integration of self-generated thoughts and their relations. The overall pattern suggests increasing specialisation of activations in the left RLPFC in particular, but also in the DLPFC and parietal cortex, which are thought to support the processing of single relations. More work will be needed to assess how these observed functional changes relate to developmental changes in performance. One factor that has been proposed to play a role is brain structure, which will be discussed in Section 4.7.

4.5. RLPFC and episodic memory retrieval during development

RPFC has been suggested to play a role in the control, and possibly processing, of temporally extended representation ( Badre, 2008 , Fig. 1 ), as suggested by its increased activation during branching or multitasking ( Badre and D’Esposito, 2007 , Braver and Bongiolatti, 2002 , Koechlin et al., 2003 ), prospective memory ( Benoit et al., 2011 , Burgess et al., 2007b ), episodic memory, in particular episodic memory retrieval ( Dobbins et al., 2004 , Spaniol et al., 2009 , Turner et al., 2008 ) and mindwandering ( Christoff et al., 2009a , Christoff et al., 2004 , Dumontheil et al., 2010a , Schooler et al., 2011 ). Studies investigating the development of the neural correlates for episodic memory have tended to focus on the encoding phase of episodic memory, rather than episodic memory retrieval ( Chiu et al., 2006 , Ghetti et al., 2010 , Ofen et al., 2007 ). However a few very recent studies investigated episodic memory retrieval using fMRI and event-related potentials (ERPs).

Findings regarding the development of the neural correlates of episodic memory in the hippocampus have been mixed. In contrast, more consistent findings have been observed in the frontal and parietal cortices thought to support memory retrieval (see DeMaster et al., 2013 for review). Paz-Alonso et al. (2008) focused on the development of true and false recognition and tested children age 8 and 12-year old, and 19–23-year old adults. The results showed region-specific developmental changes in the MTL, bilateral DLPFC, posterior parietal cortex, and right RLPFC. Adults, but not children, exhibited strongest right RLPFC activation for hits and those trials where a semantically-related lure was correctly rejected, i.e., according to the authors, those conditions in which monitoring was both required (due to the presentation of semantically relevant stimuli), and successful (leading to a correct response) ( Fig. 4a ).

Developmental changes in RLPFC activation during episodic memory tasks. (a) Neural correlates of episodic memory retrieval. Top left: increased activation with age associated with hit trials compared to trials with correctly rejected semantically unrelated lures; top right: increased activation with age associated with trials where a semantically related (critical) lure vs. an unrelated lure is correctly identified; bottom: region of interest analysis suggesting that in adults right RLPFC is involved in the monitoring of performance during episodic memory retrieval, with greater activation associated to correctly recognised semantically relevant items (hits or critical lures). CR: correct rejections; FA: false alarms; aPFC: anterior prefrontal cortex (adapted from Paz-Alonso et al., 2008 ). (b) Region of interest analysis of left RLPFC activation during source memory retrieval. The condition × age group interaction was significant, revealing increased RLPFC activation for increasingly amount of recollected information (correct border = both drawing and colour were remembered (source memory); incorrect border = the drawing but not its border colour was remembered (item memory); Miss = error trial; correct rejection = drawing correctly identified as not presented before) in the adults, but not the children, who showed similar RLPFC recruitment across trial types (adapted from DeMaster and Ghetti, 2013 ). (c) Region of interest analysis of left RLPFC activation during source memory retrieval. The condition × age group interaction was significant, revealing increased RLPFC activation for increasingly amount of recollected information (correct spatial recall = both drawing and its location were remembered (source memory); incorrect spatial recall = the drawing but not its location was remembered (item memory); Miss = error trial; correct rejection = drawing correctly identified as not presented before) in the adults, with a difference between source and item memory in the 10–11-year olds, but activation for item memory only for the 8–9-year olds (adapted from DeMaster et al., 2013 ).

DeMaster and Ghetti (2013) scanned children aged 8–11-year old and adults aged 18–25-year old who were asked whether a drawing shown on the screen had been presented before or not (item memory) and what colour was the border of the drawing during its first presentation (context or source memory). Activations associated with successful retrieval across age groups were observed in the right MTL, left posterior parietal cortex, left RLPFC and precuneus. In the RLPFC activation was observed across conditions and was unspecific to successful retrieval in children, while in adults the activation was greater for trials where the colour-drawing pair was successfully remembered than when the drawing was recognised but the colour not remembered, and in turn these trials show greater activation than for drawings correctly recognised as new ( Fig. 4b ).

In a second study, DeMaster et al. (2013) used a spatial context (drawing presented on the left or right of the screen) rather than a colour border and scanned children aged 8–9 or 10–12 years old and adults. Similarly to their previous study, DeMaster et al. (2013) observed an age × condition interaction in the left RLPFC (with a similar but weaker pattern in the right RLPFC). Adults showed greater activation for correct than incorrect source memory retrieval, and more activation for incorrect source memory retrieval (but correct old item recognition) than for correctly rejected items (new items) ( Fig. 4c ). In 10–11-year-olds, only the comparison correct vs. incorrect source memory retrieval was significant, while in 8–9-year olds activation was greater for correctly recognised items than for items correctly identified as new ( Fig. 4c ). A similar pattern of developmental changes was observed in the left parietal cortex and precuneus, but differed in the insula and DLPFC. The similar pattern observed between the parietal cortex and RLPFC further reinforces the idea that these two regions interact strongly during abstract thinking, as suggested in the relational abstract thoughts studies described above and in Section 5 below. Although DeMaster et al. (2013) point out that these two regions have been associated with different cognitive processes in the past, they suggest that further work needs to be done to disentangle their role during episodic memory retrieval development.

Contrary to the three studies described above ( Fig. 4 ), Güler and Thomas (2013) did not observe developmental changes in RLPFC during episodic memory retrieval. However this study compared 9–10 and 12–13-year olds children and did not include an adult group, which may have limited the size of the developmental effect. In addition, the paradigm used was a paired-associate picture memory task rather than a source memory paradigm. Developmental differences in activation associated with successful recall were instead observed in a more posterior part of the left middle frontal gyrus (area 46/47), right middle temporal gyrus and cerebellum, left inferior parietal lobule and anterior cingulate gyrus ( Güler and Thomas, 2013 ).

To summarise, recent studies investigating episodic memory development using neuroimaging methods show prolonged development of the neural correlates of item and source memory retrieval between late childhood and adulthood, with evidence of increased sensitivity of RLPFC activation to specific components of episodic memory (e.g. source vs. item memory, old vs. new item) in adults compared to children.

4.6. Neuroimaging studies of episodic memory and prospective memory during development

Only two studies have investigated the neural correlates of PM development. Both studies used event-related PM paradigms and collected ERP data. Mattli et al. (2011) tested children (mean age 10.3 years) and younger adults (mean age 31.4 years) (as well as an older adult group not discussed here). The N300 component reflects greater negativity for PM hits than PM misses and ongoing activity trials over the occipito–parietal region of the scalp. It is therefore thought to be associated with the detection of an event-based PM cue in the environment. Mattli et al. (2011) observed no difference in N300 amplitude for PM hits versus ongoing trials between the age group, however while adults showed greater N300 amplitude for PM hits than PM misses, children did not. According to the authors, this suggests that in children cue detection was not necessarily associated with realisation of the intention, possibly reflecting failure of executive processes associated with switching or disengaging from the ongoing activity. Reversely, a parietal positivity discriminated between PM hits and misses in children, but not in adults. No difference between age group was found between a frontal positivity which also discriminated between PM hits and PM misses. In a study including adolescent participants, Zöllig et al. (2007) observed larger N300 amplitudes in adolescents than in adults when a PM intention had to be inhibited, and a larger parietal positivity between 600 and 800 ms when a PM intention had to be executed, as compared to ongoing trials. The latter effect is similar to that observed by Mattli et al. (2011) . Source analyses suggested differences in current density between adolescents and adults for PM execution in mostly posterior brain regions, while ongoing trials were associated with greater right middle frontal gyrus activations in adolescents, which may be associated with some sort of anticipatory processing ( Simons et al., 2006 ). However, adolescents also showed poorer performance in ongoing trials, limiting the inferences that can be made from these results. To summarise, very little neuroimaging research has been done to investigate the development of PM during late childhood and adolescence. Further work, including fMRI studies, will be necessary to inform our understanding of the role played by RLPFC during PM development.

5. Association between structural changes during development and abstract thinking

RLPFC undergoes substantial structural changes during adolescence (see Dumontheil et al., 2008 for review). Research on developmental changes in brain structure have tended to consist of whole-brain analyses and do not typically report analyses in anatomical subdivisons of the frontal cortex. Overall the results show increases in white matter volumes and decreases in grey matter volumes with age in the frontal cortex during adolescence ( Barnea-Goraly et al., 2005 , Giedd et al., 1999 , Shaw et al., 2008 , Sowell et al., 1999 , Sowell et al., 2004 , Tamnes et al., 2010 , Westlye et al., 2010 ). Behavioural and functional changes during development, and in particular late childhood and adolescence, are often interpreted as being a consequence of the structural changes that occur during this period ( Crone and Dahl, 2012 , Luna et al., 2010 , Spear, 2000 ). Decreases in functional activations are considered to reflect developmental reductions in grey matter volume, presumably related to synaptic pruning. Increases are thought to relate to improved and more localised task-specific processing, potentially facilitated by faster long-range connections due to increased axonal myelination and size ( Luna et al., 2010 ). Understanding the link between structural and functional changes is critical in understanding the mechanisms of neurocognitive development, yet very few studies have directly compared structural and functional data within the same individuals (e.g. Lu et al., 2009 , Olesen et al., 2003 , Van den Bos et al., 2012 ). The association between structural changes during development and relationally abstract thinking will be described below, presenting data from recent studies which attempt to integrate brain and behavioural measures. No studies to date have investigated associations between brain structure and temporally abstract thinking during development.

Cortical thickness of RLPFC, in particular in females (e.g. Narr et al., 2007 ), and during adolescence (e.g. Shaw et al., 2006 ), has been shown to be positively correlated with standardised intelligence quotient (IQ). IQ is typically measured using tests such as the Wechsler intelligence scales ( Wechsler, 1997 ), which include a variety of subtests testing verbal and performance intelligence. Some of these tests will require the manipulation of self-generated and abstract thoughts; however, it is as yet unclear whether this accounts for the observed link between RLPFC structure and IQ ( Narr et al., 2007 , Shaw et al., 2006 ). The finding by Shaw et al. (2006) that the developmental timecourse of cortical thickness changes was associated with IQ, rather than cortical thickness in early childhood or in adulthood, stresses the importance of studying developmental trajectories. However, very few research groups have the means to do so using large longitudinal samples and most of the data discussed below are cross-sectional.

Using the datasets described above, collected while participants performed the Alphabet and Shapes tasks ( Dumontheil et al., 2010b , Dumontheil et al., 2010c ), we aimed to test the hypothesis that decreases in functional BOLD signal during adolescence may reflect the concomitant local decreases in grey matter volume. To do so we extracted local grey and white matter volumes in the brain regions showing functional developmental changes and entered these data into multiple regression analyses. The results revealed that the decrease in superior RLPFC during switching between self-generated and perceptually-derived information was not accounted for by local structural changes ( Dumontheil et al., 2010b ). Analyses of the relational integration data from the Shapes task ( Dumontheil et al., 2010c ) provided a different picture, showing that the decreased BOLD signal between mid-adolescents and adults did not remain significant when local structural measures (and performance) were covaried. Further tests were performed to relate structural changes to the connectivity changes observed using dynamic causal modelling (DCM) ( Bazargani et al., 2014 ). Grey matter volume in RLPFC and fixed connectivity (i.e. connectivity in 1-relational trials) between frontal and insular regions were both found to decrease with age. RLPFC grey matter volume was further found to predict short-range fixed connectivity. However, no significant mediation of the effect of age on short-range fixed connectivity by RLPFC grey matter volume was observed ( Bazargani et al., 2014 ). RLPFC grey matter volume in addition predicted 2-relational vs. 1-relational accuracy ( Bazargani et al., 2014 ). In the other study of relational integration development in children and adolescent participants described above, increased functional selectivity in the left RLPFC was partly accounted for by cortical thinning in the left inferior parietal lobule ( Wendelken et al., 2011 ), with a positive correlation between inferior parietal lobule thickness and activation in the left RLPFC in 1-relational trials.

The first two sets of results, within the same participants, provide evidence for the complex relationships between developmental changes in task-related brain activity, performance and local changes in brain structure. Overall the results discussed above suggest that individual differences in grey matter, in RLPFC or the inferior parietal lobule, can play a role in the development of functional networks supporting relational integration. There is less evidence suggesting specific roles of individual differences or developmental changes in white matter in the development of relational reasoning. Indeed, a recent study has shown that developmental changes in whole-brain measures of white matter volume or fractional anisotropy predicted developmental improvements in visuospatial reasoning ability. However, this effect was mediated via processing speed and was not found to be specific to fronto-parietal white matter tracts ( Ferrer et al., 2013 ). This suggests that, contrary to grey matter volume, the influence of structural developmental changes in white matter on reasoning ability may not be region-specific.

6. Questions for future research

6.1. influence of puberty vs. chronological age.

The role of puberty in the developing adolescent brain ( Blakemore et al., 2010 , Crone and Dahl, 2012 ) and whether changes observed during adolescence are a consequence of chronological age or puberty levels has been the topic of a few recent studies investigating structural changes ( Goddings et al., 2014 ) and functional changes during a social cognition task ( Goddings et al., 2012 ). Although in this latter study the functional changes observed in the MPFC were related to age rather than puberty level (in contrast to the functional changes observed in the temporal cortex), very little is known about the effect of puberty stage on the development of abstract thinking and the lateral parts of the prefrontal cortex during adolescence. More generally, there is currently little evidence of gender differences in this age range in functional imaging data (e.g. Hatcher et al., 1990 , Wendelken et al., 2011 ), however the available data is limited as some studies only included participants of one gender (e.g. Dumontheil et al., 2010b , Dumontheil et al., 2010c ), and others did not test for potential gender differences (e.g. DeMaster and Ghetti, 2013 , Crone et al., 2009 ), likely because of sample size limitations. However, structural neuroimaging studies have shown that the RPFC is the region with the greatest difference in rates of cortical thinning between males and females between the ages of 9 and 22 years ( Raznahan et al., 2010 ), and that there are sex differences in the relationship between cortical thickness maturation in the RPFC and in the superior frontal cortex in the same age range ( Raznahan et al., 2011 ). These structural studies suggest investigating the possible consequences of these structural differences over chronological and pubertal development for RLPFC function maturation is warranted.

6.2. Investigation of the role of RLPFC in the development of temporally abstract thinking

As mentioned above, RLPFC has been implicated in prospective memory, episodic memory retrieval and mindwandering, i.e. cognitive processes associated with the manipulation of temporally extended abstract information. Although recent neuroimaging work has started to investigate the neural correlates of episodic memory retrieval, only a couple of ERP studies have investigated PM, and no research has been done on mindwandering development. Future research on these topics will broaden our understanding of the development of adolescents’ ability to retrieve past experience and think about the future, and how these abilities relate to the control of attention towards perceptually-derived vs. self-generated thoughts.

6.3. Abstract thinking in the social domain: the role of medial RPFC

Anatomical studies investigating the cytoarchitectonic properties of RPFC (e.g. Öngür et al., 2003 ) and meta-analyses of fMRI data ( Gilbert et al., 2006b , Van Overwalle, 2009 ) suggest a distinction between the medial and lateral aspects of RPFC. Activations along the medial wall have mainly been observed in social cognition tasks, in particular those involving theory of mind, or mentalising, i.e. our ability to understand our own and other people's mental states (except in the most polar part of Brodmann area 10, see Gilbert et al., 2006b , Van Overwalle, 2009 ). In some situations another person's intention may be quite apparent on the basis of their overt behaviour, and our own mental states or feelings may be salient via e.g. increased heart beat frequency, sweat or stomach-ache in response to stress. In such cases , mentalising would rely on perceptually-derived information. In other situations, one may need to retrieve from episodic memory past behaviour of a friend, or to retrieve social scripts and semantic information in order to judge how they should respond to a friend's comment or behave in a novel social situation. In such cases, one would need to manipulate and integrate self-generated information. Along these lines, Van Overwalle (2009) in his review describes MPFC “as a module that integrates social information across time and allows reflection and representation of traits and norms, and presumably also of intentionality, at a more abstract cognitive level”.

Of particular interest for further research would therefore be the functional relationship between RLPFC and MPFC during abstract thinking, and whether there is anything special about the reasoning and manipulation of social vs. non-social information. A couple of recent studies speak to this. In one study, the storage and manipulation of social information in working memory was associated with activations in both the typical lateral fronto-parietal network associated with working memory and regions of the social brain, including the MPFC and temporo-parietal junction ( Meyer et al., 2012 ). In contrast, the other study, using a relational reasoning task on social information (how pleasant or unpleasant the participant or a participant's friend finds a particular concept), did not observe greater medial PFC activation during relational integration compared to the manipulation of single relations, but did observe left RLPFC activation, consistent with the relational integration studies reported above ( Raposo et al., 2011 ). Note however that neither study included a non-social comparison condition, which would be needed to assess activation patterns that are specific to the manipulation of self-generated information of a social nature.

In terms of development, adolescents typically show increased MPFC activation during social cognition tasks ( Blakemore, 2008 , Crone and Dahl, 2012 ), although we recently showed that a pattern of increasing specialisation for perspective taking compared to the processing of social stimuli could be observed between adolescence and adulthood ( Dumontheil et al., 2012 ). Touching on the relationship between abstract thinking about social vs. non-social information, an older study reported complex links in participants aged 10, 13 and 17-year old between abstract reasoning and self- or other- mentalising measures, which were found to differ according to sex ( Hatcher et al., 1990 ). Finally, results of a recent qualitative study suggest that older teenagers coordinate an increasing number of psychological components while telling stories about their family and themselves, and in so doing, create increasingly abstract and coherent psychological profiles of themselves and others ( Mckeough and Malcolm, 2010 ). A better understanding of the link between abstract thinking and social cognition during development may thus inform our understanding of the development of the self-concept during adolescence.

7. Training studies and implications for education

Fluid intelligence can be defined as the use of deliberate mental operations to solve novel problems. These mental operations include drawing inferences, concept formation, classification, generating and testing hypothesis, identifying relations, comprehending implications, problem solving, extrapolating, and transforming information. Thus, fluid intelligence is tightly linked to abstract thinking and relational integration ( Ferrer et al., 2009 ). Fluid intelligence is thought to be an essential component of cognitive development ( Goswami, 1992 ) and the basis for acquisition of abilities in various domains during childhood and adolescence ( Blair, 2006 ; see Ferrer et al., 2009 for review). Fluid intelligence in childhood predicts achievements at school (e.g. in maths during early adolescence ( Primi et al., 2010 )), university and in cognitively demanding occupations ( Gottfredson, 1997 ). Fluid intelligence is therefore a predictor of learning , especially in novel and complex situations. Consequently, a better understanding of the development of abstract thinking and reasoning during late childhood and adolescence , both in terms of behaviour and neuroscience, may have implications for education.

Of particular relevance are recent studies assessing the training of abstract thinking or reasoning skills. A few studies have investigating fluid reasoning training during childhood. For example, computerised non-verbal reasoning training was shown to improve fluid intelligence in a large sample of 4-year olds ( Bergman Nutley et al., 2011 ), and fluid reasoning training emphasising planning and relational integration led to substantial improvement on performance IQ, but not speed of reasoning , in children aged 7–9-year old from low socioeconomic backgrounds ( Mackey et al., 2011 ). A couple of studies in young adults further report that students taking a US Law School Admissions Test (LSAT) course offering 70 h of reasoning training showed a strengthening in fronto-parietal and parietal-striatal resting state connectivity compared to matched control participants ( Mackey et al., 2013 ), as well as changes in white matter structure in the frontal and parietal lobes ( Mackey et al., 2012 ). Very little work has been done investigating training of reasoning in adolescents, although Chapman and Gamino (2008) have developed the Strategic Memory and Reasoning Training (SMART) programme, designed to improve top down reasoning skills. The aim of this programme is to teach children how to learn rather than what to learn, by supporting higher-order abstraction of meaning from incoming details and world knowledge, and there is promising evidence that this training programme leads to improved gist-reasoning and fact-learning ability ( Gamino et al., 2010 ).

Whether children and adolescents may benefit more from training than adults will be an important area of research. Relatively little is currently known about developmental differences in brain plasticity in response to training interventions, however research in this domain has greater potential for tailoring appropriate training interventions to different age groups (see Jolles and Crone, 2012 for discussion). Both childhood and adolescence may be “sensitive periods” for teaching, as significant brain reorganisation is taking place during these periods. Perhaps the aims of adolescents’ education might usefully include a focus on abilities that are controlled by the parts of the brain that undergo most change during adolescence, including those described in this review: abstract thinking and reasoning, and the ability to focus on one's own thoughts in spite of environmental distraction. However, training intervention may be limited by the current level of structural brain development and cognitive capacity (as pointed out in Jolles and Crone, 2012 ), in particular for those training interventions based on strategy rather than repeated performance.

8. Conclusion

Rostrolateral prefrontal cortex supports a wide range of cognitive processes, which may have in common their requirement of retrieval, maintenance, manipulation and/or integration of self-generated, or stimulus-independent thoughts, considered broadly here as abstract thoughts, either relationally abstract, or temporally abstract. This review focused on summarising the evidence from behavioural and neuroimaging studies of the development of RLPFC and its associated functions. Behavioural studies have shown prolonged changes in the speed and accuracy of attending towards and processing self-generated information, in particular in reasoning tasks. These developmental changes appear to build on working memory and inhibitory control functions, as well as the acquisition of domain-specific knowledge. This dependence on the maturation of other aspects of cognition, including working memory and inhibitory control, which are dependent on more posterior regions of the frontal cortex, reinforces the idea that the maturation of RLPFC function will be relatively more protracted. Certain aspects of episodic memory and prospective memory, namely those that rely on implementation of strategies for recollecting source memory, and for time-checking in prospective memory tasks also continue to develop during adolescence. Neuroimaging evidence suggests a possible developmental pattern of increasing specialisation of RLPFC for the integration of relational information, with complex relationships between developmental changes in structure, performance and brain activation, and increasing specialisation for the retrieval of source memory, and item memory information, compared to the processing of new items. A strong relationship between RLPFC and the parietal cortex was apparent across tasks, and further work, in particular using connectivity analyses, may inform our understanding of how the interplay between these brain regions permits the increasingly successful integration of relationally and temporally abstract thoughts over development. Future research could inform our understanding of development of reasoning and abstract thinking in the social domain, and whether functions associated with the RPFC could be trained, with potential benefits in the domain of education.

Acknowledgements

I thank Prof. Uta Frith for inviting this review and Prof. Sarah-Jayne Blakemore for her continuing support.

Available online 12 August 2014

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Abstract Thinking: What It Is, Examples And How To Develop It

The ability to formulate hypotheses and be able to test them is not a skill that has accompanied us throughout our lives. Our way of thinking changes with development, also accompanied and supported by the development of our own nervous system.

A child may know that if he presses a certain button the television will turn on, but what if it doesn’t turn on? Surely, go to the adult, who will be able to come up with various explanations for what may be happening. He will check if the remote has batteries, if they have run out, if the television has the light indicating that it is plugged into the power, etc.

Abstract thinking, often regarded as a cornerstone of human intelligence, plays a pivotal role in problem-solving and innovation. In today’s rapidly evolving world, where challenges are becoming increasingly complex, the ability to think abstractly is more crucial than ever before.

The adult, through development, has acquired the ability to think abstractly or formally. Do you want to know more about it? Continue reading this PsychologyFor article in which we talk to you about abstract thinking: what it is, examples and how to develop it.

Table of Contents

What is abstract thinking

He Abstract thinking either formal thinking consists of the ability to think independently of the reality that is shown to us in a concrete way< It allows the human being to think about different scenarios and possibilities among which, of course, is concrete reality.

In the simplified example that we have presented in the introduction, the child is not able to think beyond the reality in front of him, which is that the television does not turn on. The adult, however, may think further, establish hypotheses, test them and thus solve the problem.

Abstract thinking, contextualized in Piaget’s theory, appears in the last stage of development: the stage of formal operations. For Vygotsky, it is precisely this acquisition that marks the difference between the thinking of the child and the thinking of the adolescent.

Understanding Abstract Thinking

Abstract thinking refers to the mental process of contemplating ideas, concepts, and principles that are detached from specific instances or contexts. Unlike concrete thinking, which deals with tangible objects and observable phenomena, abstract thinking involves conceptualization, analysis, and synthesis of information at a higher level of abstraction. It enables individuals to grasp the underlying patterns, relationships, and implications inherent in diverse situations, thereby facilitating creative problem-solving and critical decision-making.

Significance of Abstract Thinking

The significance of abstract thinking lies in its ability to transcend the constraints of immediate reality and conventional wisdom. By fostering a deeper understanding of abstract concepts and principles, individuals can navigate complex scenarios with agility and insight. Abstract thinking empowers individuals to:

1. Foster Creativity and Innovation

Abstract thinking encourages divergent thought processes, allowing individuals to generate novel ideas, perspectives, and solutions. By breaking free from conventional constraints and exploring unconventional possibilities, abstract thinkers drive innovation and creativity across various fields, from technology and science to art and literature.

2. Enhance Problem-Solving Skills

Abstract thinking equips individuals with the analytical tools and mental flexibility needed to tackle multifaceted problems effectively. By discerning underlying patterns, identifying root causes, and envisioning alternative approaches, abstract thinkers can devise innovative solutions to complex challenges, driving progress and advancement.

3. Promote Strategic Planning

Abstract thinking enables individuals to envision long-term goals, anticipate future trends, and develop strategic plans to achieve desired outcomes. By synthesizing disparate information and discerning emerging patterns, abstract thinkers can formulate robust strategies that adapt to changing circumstances and seize opportunities for growth and success.

Practical Applications of Abstract Thinking

Abstract thinking finds applications across a wide range of domains, from scientific research and engineering to business management and artistic expression. Some practical applications include:

1. Scientific Discovery

In scientific research, abstract thinking plays a fundamental role in hypothesis formulation, experimental design, and theoretical modeling. Scientists leverage abstract concepts and mathematical frameworks to elucidate complex phenomena, advance knowledge, and drive technological innovation.

2. Business Strategy

In the business world, abstract thinking informs strategic decision-making, market analysis, and competitive positioning. Business leaders rely on abstract reasoning to identify emerging trends, assess competitive threats, and devise innovative strategies that drive sustainable growth and profitability.

3. Artistic Creation

In the realm of art and creativity, abstract thinking fuels artistic expression, aesthetic exploration, and conceptual innovation. Artists use abstract concepts, symbolism, and metaphorical imagery to evoke emotions, provoke thought, and challenge perceptions, fostering cultural enrichment and artistic diversity.

Cultivating Abstract Thinking Skills

While abstract thinking is often regarded as an innate ability, it can be cultivated and enhanced through deliberate practice and cognitive stimulation. Some strategies for cultivating abstract thinking skills include:

1. Engage in Divergent Thinking

Divergent thinking involves generating multiple solutions to a problem by exploring various perspectives, ideas, and possibilities. Engaging in activities such as brainstorming, mind mapping, and lateral thinking exercises can stimulate divergent thinking skills and foster creativity.

2. Explore Interdisciplinary Connections

Interdisciplinary learning exposes individuals to diverse fields of knowledge, fostering cross-disciplinary connections and insights. By exploring intersections between different disciplines, individuals can gain new perspectives, expand their intellectual horizons, and cultivate abstract thinking skills.

3. Practice Reflective Thinking

Reflective thinking involves introspection, analysis, and synthesis of information to derive deeper insights and understanding. By reflecting on past experiences, analyzing complex issues, and synthesizing disparate information, individuals can refine their abstract thinking skills and enhance their problem-solving abilities.

Phases of development and abstract thinking

As we have indicated, formal thinking is what characterizes the Piaget’s last stage of cognitive development Piagetian theory postulates that cognitive development occurs throughout several phases or stages, more or less lasting depending on each person but necessarily successive.

The acquisition of abstract thinking begins around the age of 11 (incipient formal stage) and is consolidated from the age of 14 or 15 (advanced formal stage). Although it is true that Piaget modifies his initial theories and indicates that it is at the age of 20 when this evolutionary acquisition is consolidated (Aguilar Villagrán, M., Navarro Guzmán, JI, López Pavón, JM and Alcalde Cuevas, C., 2002 ) (1)</sup.

Until this acquisition occurs in adolescence, the child has gone through several stages of development in which his or her way of thinking has been qualitatively different.

1. Sensory-motor stage

It covers from birth to two years of age and is linked to sensory and motor development. The baby’s thinking would be circumscribed “here and now”

2. Preoperational stage

This stage ranges from approximately 2 to 7 years old. At this stage arises the symbolic thinking , so that the child can think about events or objects that are not present at that moment. He may think about the ball you showed him a few days ago or the toy his schoolmate has and he liked it so much.

3. Stage of concrete operations

Although from 7 to 11 years old children are capable of doing complex mental operations (conservation tasks, classification, serialization, etc.) their way of thinking has a limitation, and that is that the child has to manipulate things or see them to be able to think about them. If you ask him to imagine them he will not give a correct answer. In the preoperational stage, therefore, they begin to use logic and mental operations but only for facts and objects in their environment, their concrete reality.

4. Formal operations stage

For Piaget, the most important characteristic of this new way of thinking would be the fact that being able to think in terms of possibilities and not just realities< Adolescents go beyond immediate reality and begin to discover that reality can be much broader than what is in front of them, which will significantly influence their behavior.

Following Sierra, P. and Brioso, A. (2006) (2) the adolescent differentiates between what is real and what is possible, necessarily using hypothetico-deductive reasoning and reasoning about verbal statements instead of reasoning about concrete objects.

This would be the last stage of Piagetian theory, however the existence of post-formal thought, subsequent to formal thought, has been proposed. This postformal thinking would go beyond formal reasoning that yields right or wrong results and would propose solutions relative to problems.

Examples of abstract thinking

In the introduction of this article we have presented a simplified example of abstract thinking in which the person is capable of thinking about hypotheses and possibilities beyond what concrete reality shows them.

• Deductive reasoning It is a clear example of abstract thinking. Trying to exemplify this type of reasoning, we can think “All people breathe. “My cousin is a person, therefore my cousin breathes.”
• Make hypotheses In a more ecological and less theoretical example, imagine that you have met a friend who is late. You write him a message and he doesn’t answer. Our abstract thinking will allow us to establish hypotheses about what could have happened: he forgot something and turned around, the bus was delayed, there is a traffic jam, he doesn’t want to answer us, a problem has arisen, etc.
• Create a work of art It is an example of obstructed thinking, whether it is the colors in a painting or the notes in a piece of music.
• Imagine the future : the future is something that we cannot touch or know, so it is part of abstract thinking. For example: making future plans or simply thinking about the future are examples of abstract thinking.
• Analyze the past : Leaving the present means using this type of thinking, so reflecting on the past is another example of abstract thinking.

Activities to develop abstract thinking

In general, any task that requires deductive reasoning or requires the person to think about various possibilities will trigger formal thinking mechanisms. For example:

• Solving mathematical problems : in these we must apply mathematical rules and formulas and, on many occasions, we need to think about the problem from different perspectives to find the solution, therefore, it is a good exercise in abstract reasoning.
• Solving riddles and riddles:  This abstract reasoning activity helps develop this type of thinking since to solve them we will have to go beyond their literal message.
• Resolution of syllogisms : we can offer two premises and request the conclusion.

Abstract thinking is a vital cognitive skill that empowers individuals to navigate complexity, foster innovation, and drive progress in an ever-changing world. By cultivating abstract thinking skills and embracing creative thinking, individuals can unlock new possibilities, overcome challenges, and shape a brighter future for themselves and society as a whole.

This article is merely informative, at PsychologyFor we do not have the power to make a diagnosis or recommend a treatment. We invite you to go to a psychologist to treat your particular case.

If you want to read more articles similar to Abstract thinking: what it is, examples and how to develop it we recommend that you enter our Cognitive Psychology category.

• Aguilar Villagrán, M., Navarro Guzmán, JI, López Pavón, JM and Alcalde Cuevas, c. (2002). Formal thinking and mathematical problem solving. Psychothema, 14 (2), 382-386.
• Sierra, P. and Brioso, A. (2006). Biological and Cognitive Changes During Adolescence. In Sierra, L. and Brioso, A. (2006). Developmental Psychology</i. Madrid: Sanz and Torres

Bibliography

• Moya Santoyo, J. and Georgieva Kostova, E. (2014). Psychology of Thought</i. Madrid: Editorial Síntesis.
• Saldarriaga-Zambrano, PJ, Bravo-Cedeño, GR and Loor-Rivadeneira, M. (2016). Jean Piaget’s constructivist theory and its significance for contemporary pedagogy. Scientific Magazine Domain of Sciences, 2 (Extra 3), 127-137.

The art and science of abstract thinking

Anne-Laure Le Cunff

What is something we only become capable of doing after age eleven, that helps us solve complex problems and write poetry, but needs to be yielded carefully? That’s abstract thinking, a powerful tool for creativity and innovation which anyone can learn how to use better.

The difference between concrete and abstract thinking

Concrete thinking is closely related to experiences that can be directly observed. It involves everyday, tangible facts and physical objects. On the other hand, abstract thinking is a higher-order reasoning skill. It deals with conceptual ideas, patterns, and theories.

For instance, thinking about the Statue of Liberty is a concrete thought, but thinking about what it represents — the idea of liberty — is an abstract thought. Listing the names of everyone on the team who are working on a specific project is concrete thinking, but questioning whether this is the best team for the project is abstract thinking.

Another way to put it is that concrete thinking asks how whereas abstract thinking asks why . In the words of researchers from Tel-Aviv University: “Focusing on the means required to achieve a specific goal ultimately entails transforming an abstract idea into a concrete action and thus primes a concretizing mindset; likewise, focusing on the purpose of an action primes an abstracting mindset.” 

According to famous psychologist Jean Piaget, it is not until around eleven years old that children become able to think abstractly and to use metacognition . Before that age, we are only able to think logically about objects we can physically manipulate. Our ability to think abstractly keeps on expanding as we grow up, but most people take this ability for granted, and very few proactively practice their abstract reasoning skills.

Three concrete ways to practice abstract thinking

It is possible to improve your abstract reasoning skills.

• Reframe the question. Go from “how?” to “why?” in order to take a step-back and tap into your abstract reasoning skills. For example, if you feel stuck trying to write a blog post, ask yourself: why am I writing this, who is this for, what exactly am I trying to achieve? This higher-order approach may help you discover a fresh angle to tackle your project.
• Look for patterns. Instead of looking at each concrete element in isolation, practice networked thinking to uncover abstract patterns and underlying dynamics in the relationship between those elements. Don’t be afraid to use your imagination. Sometimes patterns can be hard to detect, but the simple process of looking for them will help you improve your abstract reasoning skills.
• Take inspiration from abstract thinkers. Philosophers, artists, and scientists are great abstract thinkers. Like a philosopher, examine the nature of ideas such as success, reality, or community. Like a poet, go from concrete thinking to abstract thinking by using metaphors, simile, analogies, and symbolism. Like a scientist, formulate a theory by going from the particular to the general. Is the concrete event you are currently observing an occurrence of a wider phenomenon? Could you test your hypothesis?

Abstract thinking is essential in order to solve complex problems, come up with innovative ideas, and collaborate with other people. It allows us to analyse situations, understand new concepts, formulate theories, and to put things in perspective.

Without abstract thinking, we would not be able to grasp concepts such as friendship, hope, democracy, imagination, success, wisdom, happiness, or even love. However, while it’s a powerful tool to add to your thinking toolbox, it should not be the only tool, and it should be used wisely.

A balancing game

As with any powerful tool, abstract thinking can be a double-edged sword. First, abstract thinking without concrete thinking amounts to imagination without execution. Creativity requires an ambidextrous mindset which balances exploration and exploitation. Once you have figured out why an idea needs to see the light of day, you need to think about how you will make it happen. In other words, you need to go from abstract thinking to concrete thinking.

It can also be dangerous for your mental health to always default to abstract thinking, especially when thinking about past events. Psychology researchers explain that “abstract rumination is characteristic of depressed individuals, as is the tendency to experience post-decisional regret.” It is particularly true of thinking about traumatic events, where concrete thinking has been found to be much more helpful than abstract thinking.

Despite these caveats, abstract thinking skills are particularly helpful in situations that require thinking outside the box, uncovering hidden patterns, and generating innovative ideas. Just make sure you are balancing it with concrete thinking and monitoring your thought patterns so abstract thinking doesn’t turn into abstract rumination.

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The Peak Performance Center

The pursuit of performance excellence, types of thinking.

Thinking is the cognitive activities you use to process information, solve problems, make decisions, and create new ideas. You use your thinking skills when you try to make sense of experiences, organize information, make connections, ask questions, make plans, or decide what to do.

There are several different types of thinking or ways to think.

Creative thinking  – refers to the ability to conceive new and innovative ideas by breaking from established thoughts, theories, rules, and procedures. It involves putting things together in new and imaginative ways. Creative thinking is often referred to as “thinking outside the box.”

Analytical thinking – refers to the ability to separate a whole into its basic parts in order to examine the parts and their relationships. It involves thinking in a logical, step-by-step manner to break down a larger system of information into its parts.

Critical thinking – refers to the ability to exercise careful evaluation or judgment in order to determine the authenticity, accuracy, worth, validity, or value of something. In addition to precise, objective analysis, critical thinking involves synthesis, evaluation, reflection, and reconstruction.   And rather than strictly breaking down the information, critical thinking explores other elements that could have an influence on conclusions.

Concrete thinking – refers to the ability to comprehend and apply factual knowledge. It is about thinking of objects or ideas as specific items, rather than as a theoretical representation of a more general concept. It involves thinking only on the surface, always literal, and to-the-point.

Abstract thinking – refers to the ability to use concepts to make and understand generalizations then relating or connecting them to others items, events, or experiences. It involves paying attention to the hidden meanings thus allowing you to observe and understand theories and possibilities.

Divergent Thinking – refers to the ability to generate creative ideas by exploring many possible solutions in an effort to find one that works. It involves bringing facts and data together from various sources and then applying logic and knowledge to solve problems or make decisions. It starts from a common point and moves outward in diverging directions to involve a variety of aspects or perspectives.

Convergent thinking – refers to the ability to put a number of different pieces or perspectives of a topic together in some organized, logical manner to find a single answer. It involves focusing on a finite number of solutions rather than proposing multiple solutions.

Sequential (linear) thinking – refers to the ability to process information in orderly prescribed manner. It involves a step-by-step progression where a response to a step must be obtained before another step is taken.

Holistic (nonlinear) thinking – refers to the ability to see the big picture and recognize the interconnectedness of various components that form the larger system.  It involves expanding your thought process in multiple directions, rather than in just one direction, and understanding a system by sensing its patterns.

Opposing Categories of Types of Thinking

Types of thinking can be divided into several opposing categories;

Concrete Thinking vs. Abstract Thinking

• Convergent Thinking vs. Divergent Thinking
• Creative Thinking vs. Analytical Thinking
• Sequential (linear) Thinking vs. Holistic Thinking

Concrete thinking refers to the thinking on the surface whereas abstract thinking requires much more analysis and goes deeper. Concrete thinking will only consider the literal meaning while abstract thinking goes deeper than the facts to consider multiple or hidden meanings.

Concrete thinking refers to the process of comprehending and applying factual knowledge. It involves only those things which are visible and obvious allowing any individual to observe and understand. Abstract thinking goes beyond all the visible and present things to find hidden meanings and underlying purpose.

A concrete thinker will look at the flag and only sees specific colors, marking, or symbols that appear on the cloth. An abstract thinker would see the flag as a symbol of a country or organization. They may also see it as a symbol of liberty and freedom.

Convergent thinking vs. Divergent thinking

Convergent thinking involves bringing facts and data together from various sources and then applying logic and knowledge to solve problems or to make informed decisions. Convergent thinking involves putting a number of different pieces or perspectives of a topic back together in some organized, logical manner to find a single answer.

The deductive reasoning that the Sherlock Holmes used in solving mysteries is a good example of convergent thinking. By gathering various bits of information, he was able to put the pieces of a puzzle together and come up with a logical answer to the question of “Who done it?”

Divergent thinking, on the other hand, involves breaking a topic apart to explore its various component parts and then generating new ideas and solutions. Divergent thinking is thinking outwards instead of inward. It is a creative process of developing original and unique ideas and then coming up with a new idea or a solution to a problem.

Analytical Thinking vs. Creative Thinking

Analytical thinking is about breaking information down into its parts and examining those parts their relationship. It involves thinking in a logical, step-by-step manner in order to analyze data, solve problems, make decisions, and/or use information. Creative thinking, on the other hand, refers to conceiving new and innovative ideas by breaking from established thoughts, theories, rules, and procedures. It is not about breaking things down or taking them apart, but rather putting things together in new and imaginative ways.

An analytical thinker may look at a bicycle to determine how it works or what is wrong with it. A creative thinker may look at the same bicycle and think or an new way to make it faster or a new way to use it.

Sequential Thinking vs. Holistic Thinking

Sequential thinking is processing information in orderly prescribed manner. It involves a step-by-step progression where the first step needs to be completed before then second step occurs.

If a = b, and b = c, then a = c

Holistic thinking, on the other hand, is about seeing the big picture and recognize the interconnectedness of various components that form larger systems.  It involves expanding your thought process in multiple directions, rather than in one direction, in order to understand how everything connects. Holistic thinkers want to understand the patterns and how thing connect to each other.

When assembling a table, a sequential thinker would follow the step-by-step directions. A holistic thinker would want to see or mentally visualize how the table would look when it is completed.

Author:  James Kelly, July 2015

Related Links

Types of thinking

Critical thinking

Brainstorming

Blooms taxonomy

Blooms taxonomy revised

Mind mapping

Mind mapping tips

How to mind map

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• Review Article
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• Published: 11 May 2016

Relational thinking and relational reasoning: harnessing the power of patterning

• Patricia A Alexander 1  

npj Science of Learning volume  1 , Article number:  16004 ( 2016 ) Cite this article

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This article offers an overview of the nature and role of relational thinking and relational reasoning in human learning and performance, both of which pertain to the discernment of meaningful patterns within any informational stream. Distinctions between thinking and reasoning relationally are summarized, along with specific forms of patterning that might be discerned. Next, the article summarizes what is presently known about relational reasoning, and then moves to explore future directions in educational research and in instructional practice that warrant attention based on the empirical literature.

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Introduction

A baby reaches for his mother among a gathering of women; a student becomes enrapt in a story, seeing herself in the main character; an attending physician realizes that her patient is displaying abnormal symptoms indicative of acute myocardial infarction; and a physicist sets out to disprove an espoused cosmological theory. At the core of all human learning and performance, as with the diverse episodes just described, is the foundational ability to perceive patterns that thread through all of nature, including human nature. Those patterns can be as intimate as a hand gesture; 1 as academically core as literacy comprehension; 2 as critical as effective medical diagnosis; 3 or as sweeping as the laws of the physical universe. 4

Without the ability to discern meaningful patterns in the stream of data that continually flood the senses, humans would remain prisoners within a world of isolated sights, smells, and sounds, unable to comprehend or to build on experiences across time and space. Thankfully, humans enter the world with the capacity to perceive patterns within the sensory information that surrounds them and then draw on that capacity in intentional, effort, and strategic ways to promote higher-order cognitive processing. 5 – 7 Granted that initial capacity, which we labeled as relational thinking, 8 can be quite primitive and can vary greatly from person to person or from situation to situation, but it is nonetheless the neurobiological functioning that guides the development of human perception and cognition across the lifespan.

This contention that the ability to discern patterns within any informational stream is rudimentary, pervasive, and essential is by no means new. From the philosophical writings of Heraclitus, Aristotle, and Immanuel Kant to William James and John Dewey, from the Gestalt school of psychology to contemporary research in cognitive science 9 , 10 and neuroscience, 11 , 12 the foundational nature and potency of relational thinking appears undeniable. Yet, there is still much to be learned about pattern perception and its purposeful utilization. Toward that end, what new insights are offered herein pertain to the emerging body of psychological, cognitive neuroscience, and psychometric research on the character of the relations that might be spontaneously perceived (i.e., relational thinking) and, more particularly, on the intentional harnessing of pattern recognition to drive higher levels of human learning and performance (i.e., relational reasoning).

Thinking and reasoning relationally

To move forward in the discussion of harnessing the power of patterning, it is important to first disentangle the two associated processes I have referenced, relational thinking, and relational reasoning—processes that operate in concert to allow for the coupling of percepts and concepts. Further, it is essential to consider how those notions compare with associated concepts that populate the cognitive science and neuroscience literatures. 13 – 15

Relational thinking and reasoning in comparison

According to Peirce, 16 percepts, which are mental impressions formed in the moment from the sensory systems data, 17 – 19 are “the starting point of all our reasoning” (p 308). Percepts are not isolated, occasional, or singular occurrences. Rather, at any given moment, minds are being bombarded by innumerable percepts. 20 , 21 Further, those percepts continue unabated, regardless of human will, judgment, or knowledge.

In fact, these configurations are, for the most part, fleeting sensations, remaining largely outside of human awareness, 20 that is, unless those percepts garner attention or become consciously accessible. 22 It is through relational thinking that the onslaught of perceptions becomes recognizable or consciously accessible as some discernible object or idea (i.e., concepts). Without relational thinking, the innumerable percepts would remain separate pieces and never assemble into impressions or rudimentary forms that could potentially influence human thought or action. In effect, without relational thinking, there is no mechanism for building on percepts or for the coupling of those percepts with the concepts that populate the human mind. 8

Nonetheless, more is required of human performance than a reliance on more instinctual, spontaneous, and fleeting discernments of patterns (i.e., relational thinking). For the attainment of more sustained, deeper, and what are popularly regarded as higher-order forms of cognitive thought and performance, humans must build on the more innate capacity to perceive patterns. The mechanism that my colleagues and I have targeted that serves the fundamental need to purposefully harness the power of patterning is relational reasoning. 8 , 23

Although it is important to acknowledge that the boundaries between these more intuitive and intentional systems of mental processing may not be categorically distinct, 24 I nonetheless juxtapose these two forms of pattern recognition on several key dimensions, including locus, temporal frame, and cognitive demands to sharpen the salient contrasts ( Table 1 ). As the comparison offered in Table 1 suggests, relational thinking can, thus, be characterized as more fleeting, external, and rather effortless and unconscious in nature. This stands in contrast to relational reasoning, which has a more enduring, representational quality and which demands effort and intentionality on the part of the human mind. Although a more in-depth consideration of the neurobiological underpinnings of relational reasoning is beyond the scope of this overview, there is ample evidence that this capacity, which emerges within the first years of life, rapidly develops into middle and late adolescence. 11 , 25 Further, particular regions of the brain, most notably the rostrolateral prefrontal cortex, seem especially implicated when children and adult engage in relational reasoning tasks.

Relational thinking and reasoning in perspective

With the brief comparison of relational thinking and relational reasoning as a backdrop, let me situate those characterizations within the broader literatures in cognitive science and neuroscience that touch upon such underlying mental capacities and processes. For one, the distinction my colleagues and I have drawn between the more intuitive versus the more intentional systems of relational processing is not commonly addressed within the cognitive science and neuroscience literatures. Rather, a more consolidated focus on higher forms of cognition predominates, and understandably so given the emphasis on relational processes within sophisticated problem-solving areas such as mathematics, rational thought, and scientific reasoning. 13 , 26 For example, in their review of relational knowledge, Halford et al. 27 (p 488) focused on “relational representations”, which they distinguish from more automatic, modular, or nonanalytic processes.

When the discussion progresses to more intentional and effortful relational processes, the similarities and differences that arise between the conceptualizations and operationalizations offered herein and those populating the cognitive science and neuroscience literatures are more intricate and nuanced. At the conceptual level, for instance, the definition of relational reasoning framing my colleagues’ and my program of research is generally compatible with that associated with the neuroscience literature. For instance, Krawczyk et al. 28 (p 588) characterize relational reasoning as the human brain’s “unique capacity to reason about abstract relationships among items in our environment”—a conception that parallels our characterization of relational reasoning as the ability to discern meaningful patterns in the stream of data. 23 To this base definition, others 11 , 27 make explicit reference to relations between “mental” representations. Although multiple representations are involved in our conceptualization and operationalization of relational reasoning as well, those representations may be in mundi as well as in mente —a subtle but relevant distinction that allows for the percept–concept coupling with which I previously eluded.

Another similarity between the conception of relational reasoning evoked in our current program of research and that populating the cognitive science and neuroscience literatures centers on executive function factors such as working memory and inhibitory control that seemingly underlie this and other forms higher cognition. 29 – 31 Further, depending on the nature of the symbols entailed in in mundi or in mente representations (e.g., linguistic, numeric, figural, or graphic), additional individual differences factors such as visuospatial memory, reading fluency, and domain-specific knowledge can prove influential to the discernment of meaningful patterns within informational displays. 27 , 32 – 34 Moreover, when relational reasoning is examined within novel problem-solving tasks or contexts, it is indicative of fluid intelligence. 25 , 35 , 36

As the noted similarities suggest, much of the essence of relational processing represented in the cognitive science and neuroscience literatures is preserved in the theoretical and empirical work my colleagues and I have undertaken. However, what my colleagues and I have sought to contribute to the discourse pertains more directly to the manifestations of relational reasoning being explicitly explored by cognitive scientists and neuroscientists, and the manner in which those manifestations are systematically investigated. Specifically, as will be elaborated in the ensuing sections, my colleagues and I have attempted to push the exploration of relational reasoning beyond its more routine foci so as to consider multiple forms, measures, and techniques that can be utilized to unearth the varied forms of this foundational mental capacity.

Toward that end, I will now turn to the specific forms and processes associated with relational reasoning that have been the focus of theoretical and empirical work by my colleagues and me, as well as by others (e.g., references 37 – 39 ). Subsequently, I will attempt to outline what my colleagues and I have come to learn about the relational reasoning, and what remains to be understood. Although not seeking to diminish the efforts underway in the broader research communities, I will highlight the recent work my collaborators and I undertaken in this brief survey. I will also consider how relational reasoning manifests in everyday functioning and problem solving, and what steps can be taken to harness that nature in service of fostering learning and academic development.

Relational reasoning in form

Over the past 4 years, my colleagues and I have delved into the construct of relational reasoning for the purpose of finding ways to ascertain its nature and to gauge its role in human learning and performance. Among our initial realizations was that much of the recent work in cognitive science and neuroscience, while theoretically informative, did not entirely serve the needs of educational researchers for several reasons. 29 For one, the methods employed to examine relational reasoning within cognitive science and neuroscience are highly specialized (e.g., functional magnetic resonance imaging or event-related potential) and cannot be pragmatically or widely utilized in educational research. Second, although the term relational reasoning as applied within these fields is similarly defined in terms of pattern perception, only one form of such pattern perception is routinely examined (i.e., analogical reasoning 35 ).

Further, the research in neuroscience demonstrates an overreliance on a singular measure, the Raven’s Matrices, 40 thereby restricting examination to only one form (analogical reasoning) and only one mode of representation (figural 29 , 35 ). Thus, in our research, we sought to investigate multiple forms of relational reasoning and to devise multiple psychometrically sound measures of relational reasoning entailing both figural and linguistic representations. We also wanted measures that could be easily administered to children, adolescence, and adults either online or in print. Samples of items from two of the resulting measures suitable for older adolescents and adults—the Test of Relational Reasoning (TORR 41 – 43 ), and the Verbal Test of Relational Reasoning (vTORR 44 , 45 )—are provided in the Supplementary Appendix .

As the sample items in the Supplementary Appendix illustrate, the TORR is figural in form and intended to function as a more fluid measure of relational reasoning ability. In contrast, vTORR was conceived as a somewhat more crystallized test of relational reasoning due to its linguistic content, although the novelty of the items still entail fluid or flexible problem solving on the part of respondents. A third measure, the Test of Relational Reasoning-Junior (TORRjr 46 , 47 ) was developed for use with children and early adolescents. The TORRjr was devised to be an easier version of the TORR and as such parallels that measure in its scales, items, and overall format.

Drawing on the extant literatures in reasoning (e.g., reference 48 ), mathematical set theory (e.g., reference 49 ), and philosophy (e.g., reference 50 ), my colleagues and I ultimately settled on four forms of relational reasoning that we felt encompassed key patterns of similarity and dissimilarity that could be discerned within any informational stream. 23 , 35 Those four forms, as illustrated in the sample items in the Supplementary Appendix , pertained to patterns of similarity (analogical reasoning), discrepancy (anomalous reasoning), opposition (antithetical reasoning), and exclusivity (antinomous reasoning). Although other forms of relational reasoning likely exist, these four forms were found to have theoretical and empirical grounding within the educational, psychological, and philosophical literatures.

Such grounding is especially apparent for the empirical investigations of analogical reasoning, which has garnered the most attention in the explicit study of relational reasoning (e.g., references 10 , 51 ), and anomalous reasoning, which has focused largely on the domains of science and mathematics (e.g., references 52 , 53 ). For example, Hofstadter 54 (p 499) has described analogies as “the very blue that fills the whole sky of cognition”. With similar conviction, Chinn and Brewer 38 (p 1) contended that “understanding how science students respond to anomalous data is essential to understanding knowledge acquisition in science classrooms”, as well as how students undergo theory change more broadly.

Although the empirical evidence for antinomous and antithetical reasoning may be somewhat less prevalent, it exists nonetheless. For instance, the research dealing with ontological categories within the sciences, especially the biological science (e.g., alive versus not alive; animal or plant), requires individuals to reason antinomously. 37 , 55 These very notions of antinomous and antithetical reasoning can be found within the writings of the pre-Socratic philosopher, Heraclitus, who wrote about the unity of opposites. 56 In essence, what Heraclitus contended was that we can only really come to know something through its relation to its true opposite. At first glance, Heraclitus would seem to be making a case for antithetical reasoning. However, his discussion of “true” opposites takes on a more paradoxical orientation. In effect, to know happiness, we must juxtapose it to “not happiness”, or to understand “good” there must be “not good”. Support for more antithetical orientations can be found in the substantial literatures dealing with persuasive text and with conceptual change that consistently indicate the power of reasoning about opposing views or counterarguments to improve comprehension 57 , 58 and to dismantle misconceptions within academic domains. 59 – 61

In vitro studies

Over the past 5 years, my colleagues and I have sought the empirical evidence of these four forms and examined the association between relational reasoning and performance in varied cognitive domains. These empirical investigations have been both in vitro and in vivo in nature. For the in vitro studies (i.e., laboratory or experimental research), we submitted the previously described measures of relational reasoning (i.e., TORR, vTORR, and TORRjr) to various analyses within a number of academic domains. These analyses were undertaken to establish the psychometric properties of these measures, examine item functioning, determine underlying factor structures, and test differing structural models of relational reasoning. We also explored the association between relational reasoning and select executive function and individual difference indicators (e.g., comprehension ability and visuospatial working memory).

What this collection of investigations has revealed is that these three formal measures are psychometrically sound assessments of relational reasoning with items that operate within acceptable difficulty parameters (i.e., references 30 – 70 ) and that factor as expected. Further, we have ascertained that visuospatial working memory and reading comprehension ability were only moderately associated with the performance on TORR and vTORR, respectively. 42 , 43 , 45 We also tested the degree of association between TORR and the Raven’s Matrices, 40 which is so commonly used in neurobiological studies of relational reasoning. We determined that there was a significant positive correlation between these two presumed measures of relational reasoning, and that the TORR was more difficult for participants than the Raven’s. 42

One recent investigation by Grossnickle et al. 62 used items from the TORR to explore the componential processes underlying the four forms of relational reasoning via Bayesian network analyses. Grossnickle et al. found that the component processes of encoding, inferring, mapping, and applying that Sternberg 48 ascribed to analogical reasoning were also evident in students’ processes of anomalies, antitheses, and antinomies. These researchers also determined that low-performing students struggled more with working memory demands at the point of inferring and mapping.

There has also been evidence of significant associations between TORR scores and performance in the domains of engineering design 63 and maternity nursing (Fountain 64 ). For example, Dumas and Schmidt 63 determined that those with higher TORR scores, especially for the antinomy scale, produced more creative solutions to engineering design problems. Similarly, Fountain 64 found that relational reasoning capacity, as measured by the TORR, was a significant predictor of maternity nurses critical thinking as measured by their analysis of medical cases.

In vivo studies

Alongside these more experimental investigations, we have been exploring relational reasoning in vivo , that is, within naturally occurring settings that involve complex problem solving. These studies have uncovered evidence of analogical, anomalous, antithetical, and antinomous reasoning in the interactions between an attending physician and resident physicians engaged in diagnosing and treating patients. 3 In this investigation, Dumas et al. also demonstrated how these various forms of relational reasoning worked in concert to lead to more effective clinical outcomes. Jablansky et al. 65 similarly identified occasions of relational reasoning as first through twelfth graders thought aloud about the form and function of more or less familiar technological tools. What was significant about this study was not only the manifestation of all forms of relational reasoning even among the youngest students but also the differences in the quantity and quality of reasoning associated with the grade level and object familiarity.

Relational reasoning in principle

Together, the in vitro and in vivo investigations of relational reasoning just overviewed have contributed to certain insights about its nature and its importance to human learning and development. Recently, my colleagues and I 66 were asked to share what we have come to learn about relational reasoning with a particular eye toward educational policies. Here I revisit those insights and then subsequently turn to the implications of this emerging literature to next steps in empirical research and instructional practices for all those broadly concerned with human learning and development. I also take the liberty to outline some of the lingering questions that each of these principles about relational reasoning brings to the surface.

Specifically, according to Alexander et al. , 66 the following claims about relational reasoning can be forwarded:

The ability to reason relationally is foundational and pervasive.

Relational reasoning can be observed and measured in diverse ways.

Relational reasoning varies by age, domain, and context.

Relational reasoning is malleable and teachable.

Foundational and pervasive

As I have sought to establish from the outset, relational reasoning, especially when coupled with its more intuitive, spontaneous counterpart, relational thinking, underlies all human performance—an observation shared by cognitive scientists and neuroscientists. 10 , 27 , 39 Early in the twentieth century, Spearman, 67 one of the progenitors of modern intelligence testing and someone strongly influenced by the Gestalt school, came to see human intellectual capacity largely in terms of pattern perception. His search for the unitary intelligence factor “ g ” was orchestrated around certain “fundamental laws”, including the law of the eduction of relations, which pertains to the power to bring relations to mind.

Although I am not seeking to make a case for any “ g ” factor of intelligence, I do see certain parallels between Spearman’s arguments for the essentialness of perception and attention to patterns and the contemporary work on relational reasoning. Simply stated, if individuals cannot perceive and do not attend to the relations embedded within sensory information that continually floods them, then they will undoubtedly be relegated to a world that consists solely of noise or fragmentary pieces of sensory data that carry little or no meaning. For these reasons, relational reasoning is unquestionably a fundamental and pervasive capacity.

Further, this underlying capacity to perceive patterns is sufficiently fluid or flexible to allow for iterations when problems are nested within specific domains (e.g., engineering, mathematics, medicine, or reading). For that reason, and as seen in the studies in the medical professions 3 , 64 and engineering, 63 certain forms of reasoning may be more evident when domain-specific problems are engaged. What cannot be ascertained at this point, however, is the precise nature of interplay between domain-general and domain-specific iterations of relational reasoning forms.

Observable and measurable

As an empirical researcher, more is required than simply believing in the foundational nature of relational reasoning. What is necessary is to observe and measure relational reasoning in psychometrically sound ways. Through observation, researchers are able to bear witness to relational reasoning’s presence within naturally occurring occasions of reasoning and problem solving, as my colleagues and I have done in eavesdropping on the interactions within a medical team 3 or children whose technological literacy is being gauged. 65

By comparison, through measurement, researchers have ascertained the role that relational reasoning has in human learning and performance for a range of situations and contexts. For my colleagues and I, the TORR, vTORR, and the TORRjr have become portals onto the processes and power of relational reasoning. The connections documented between relational reasoning and creative engineering designs 63 and effective critical thinking in maternity nursing 64 are two such cases in point. Through the process of establishing the psychometric qualities of these measures, we also found that relational reasoning, although correlated at a low or moderate level with measures of comprehension, visuospatial working memory, and even the Raven’s Matrices, makes significant and unique contributions to cognitive outcomes over and above such well-established indicators.

What is less understood about these processes has more to do with the way in which relational thinking and relational reasoning work together within more dynamic and collective problem-solving situations. We see hints of this interactivity in the in vivo studies that shed some light on the process by which the patterning of one individual sparks the relational processing of others. 3 For instance, when a medical resident notes a particular anomaly in the symptoms of a patient, there is a greater likelihood that other residents will interject additional anomalies into the discussion. Or, when the attending physician, a recognized expert, reminds the residents of an analogous case, the direction of the diagnosis shifts and new similarities and differences are introduced into the discourse. Of course, much more needs to be explored about dynamic and collective problem-solving contexts and the influence that these contexts exert on the flow of relational reasoning.

Age, domain, and context dependent

Employing both observations and measures, we have come to learn that the fundamental and pervasive character of relational reasoning does not translate into uniformity across ages, domains, or contexts. Nowhere is this more evident than in the cross-sectional data that Jablansky et al. 65 gathered for students in first to twelfth grade. For one, these researchers found that although relational reasoning occurred at all these grade levels, younger students required more external support or scaffolding than older students. In addition, among younger students, there were more occasions of analogical and anomalous reasoning and relative fewer instances of antithetical and antinomous reasoning. Conversely, the older students relied more on antithetical and antinomous reasoning rather than on analogical and anomalous reasoning. Finally, Jablansky et al. determined that the problem-solving context mattered. Specifically, there were significantly more occurrences of analogical reasoning over the other four forms when the objects being analyzed for their form and function were familiar rather than unfamiliar.

Why these developmental shifts in reasoning patterns would arise is a question worthy of further exploration. At this point, there is reason to speculate that some of the shifts occurred because of the children’s increased knowledge and experiences—in this instance about various technologies and their functions. However, increased knowledge alone does not account for the greater reliance on antitheses and antinomies among the older students in this investigation. Perhaps some of the explanation lies in the neurobiological changes that occur in middle and late adolescence—changes that are seen to support relational reasoning capability. 11 , 25

Malleable and teachable

Just as intelligence has been shown to be changeable as a consequence of relevant experiences, 68 , 69 relational reasoning should likewise be regarded as malleable and teachable. However, the degree of malleability or teachability remains open to debate. For instance, as evident in the cross-sectional study of students from grades 1 to 12 by Jablansky et al. , 65 there were apparent quantitative and qualitative shifts in relational reasoning that occurred from childhood and into adolescence, even in the absence of any explicit training of these reasoning forms. By contrast, Fountain 64 found no significant change in TORR performance for maternity nurses at prelicensure through to those with >10 years of experience. Such an outcome suggests relatively stability in the level of relational reasoning after late adolescences when no cognitive maturation, pertinent experiences, or direct interventions were implicated.

Moreover, there is ample evidence that analogical reasoning performance can be directly trained, including the studies my colleagues and I have conducted with children and adults. 70 – 72 Others have documented similar effects for interventions involving analogical reasoning. 39 The question remains whether similar training outcomes to those documented for analogies would be expected for the other forms of relational reasoning, anomaly, antithesis, and antinomy.

In effect, would we anticipate that individuals’ overall relational reasoning performance could be either directly or more indirectly manipulated? On the basis of certain evidence, my response to that question is “yes”. For one, there is now empirical evidence that the component processes of analogical reasoning (i.e., encode, infer, map, and apply 48 ) that my colleagues and I employed in the training of this form of relational reasoning 72 also underlie anomalous, antithetical, and antinomous reasoning. 62 Thus, it is reasonable to assume that these same component processes can be used as the basis for training relational reasoning more broadly.

Further, it has been shown that relevant interventions that do not expressly target the four forms can produce shifts in TORR scores. Such indirect effects were apparent in Dumas and Schmidt’s 63 study of engineering design students. These researchers found that students’ exposure to a design intervention resulted in an even stronger association between creative engineering design solutions and relational reasoning, especially for antinomous reasoning. Others are presently exploring the effects that interventions aimed at critical analytic thinking on both written arguments and oral discussions. 73 , 74 For instance, students in the Murphy et al. 74 study were trained to formulate elaborated explanations in writing, which are detailed justifications for claims made. Although all four forms were identified in students’ written products, instances of analogies and antitheses were more prevalent. Similarly, when training high-school physics and chemistry students to engage in exploratory talk (i.e., two or more people exchanging responses around a provocative question or issue), Greene et al. 73 documented frequent occasions of antithetical and anomalous reasoning. Both of these investigations illustrate fruitful approaches for enhancing students’ relational reasoning when focused primarily on improving the quality of written or oral argumentation skills.

Although I am fairly confident in the teachability of relational reasoning to some level, there are lingering questions as to the form that any explicit training should take, especially with regard to the domain specificity of the intervention. What the rich literatures on strategy or problem-solving training suggest, however, is that efforts that are entirely generic in form are less likely to have lasting effects. 75 Thus, embedding such training within a knowledge domain and aligning it with problems and tasks that are central to that domain—be it medical diagnosis, mathematical problem solving, or reading comprehension—seems more likely to promote immediate and enduring effects.

Future directions for research and practice

Given what the field has come to understand about the nature and importance of relational reasoning, the question remains as to what lies ahead for this field of inquiry. Let me identify several directions that appear especially promising in light of known and emerging findings about relational reasoning.

For research

There are several obvious avenues for research in relational reasoning that need to be actively pursued to elaborate on and extend what I have outlined in this overview. Those avenues pertain to: (a) longitudinal examinations; (b) studies that incorporate physiological or neurological data; and (c) cross-cultural and cross-context investigations. For example, to date the construction of relational reasoning’s developmental trajectory has relied largely on cross-sectional research. Thus, it is imperative to undertake more longitudinal investigations of relational reasoning, especially ones that encompass points of significant cognitive, neurobiological, experiential, and psychosocial transition such as that marked by the movement from middle school into high school or into college. Such longitudinal studies could be incorporated into ongoing studies of expertise development as well, in order to ascertain whether transitions from acclimation into competence or expertise are accompanied by concomitant transformations in relational reasoning capacity or performance patterns.

As I noted, much of the work on relational reasoning within neuroscience has looked exclusively at analogical reasoning 3 , 29 and has relied extensively on items from the Raven’s Matrices. 40 Consequently, what is not understood is whether the engagement in anomalous, antithetical, or antinomous reasoning would produce similar brain activation patterns to those documented for analogical reasoning. As all four forms apparently share underlying componential processes, and are moderately correlated, 62 there is reason to hypothesize that they operate in neurologically similar ways. Yet, there is also cause to presume that there is sufficiently neurological variability, especially for the processing of antinomies, which demand the establishment of exclusion between two sets of information.

Finally, the tests that my colleagues and I have devised were intended to serve as more novel or fluid measures of relational reasoning capacity. 42 That is even true of the vTORR, which relies on linguistic information but still entails the performance of non-traditional reasoning tasks. At present, we do not know whether there is measure non-invariance for any of these tests for different ethnic or gender groups, although the ongoing study by Dumas 76 on the TORR has found no evidence of non-invariance at the item level. Similarly, the international studies currently underway in Israel and New Zealand are expected to shed light on any cultural differences that might manifest on the TORRjr. But much more needs to be learned about how relational reasoning might iterate not only within diverse cultures but also across varied contexts such as in professional practices like medicine, nursing, or engineering and the complex problem solving they involve.

For practice

Within the educational and psychological research communities to which I belong, there are certainly avenues that merit exploration related to practice. Those avenues include: (a) predictive studies that explore the use of relational reasoning as a measure to identify academic potential; (b) classroom-based investigations that seek to expressly train relational reasoning within such a dynamic environment; and (c) domain-specific studies that explore the enactment of relational reasoning for contrasting fields such as physics or history. I raise the notion of predictive studies because one of my primary purposes for embarking on the study of relational reasoning and the development of relevant measures was to forge tools that could lead effectively to the identification of fundamental cognitive capabilities that might otherwise be overlooked by more traditional screening measures. Will performance on the TORR signal unrecognized potential missed by traditional crystallized measures of achievement or aptitude? In what way will performance on the TORR or one of its iterations unearth future success in academics or in later professional practice? To what extent do students with identified learning or cognitive problems perform differently on the TORR, vTORR, or TORRjr compared with their non-identified peers? Ultimately, having psychometrically sound measures of relational reasoning is only the first step toward addressing such important questions.

This issue of screening for academic potential raises another ethical concern regarding relational reasoning. To be more precise, if relational reasoning is a foundational and pervasive capability and if relational reasoning can be trained or improved, then is there no obligation to train individuals to reason better analogically, antithetically, antithetically, and antinomously? This has long been my personal position. For that very reason, my colleagues and I are committed to articulating models for relational reasoning training based on the componential processes we have utilized in the past.

As mentioned, one of the lessons that I learned from those prior forays into classroom-based training is that the processes of reasoning relationally cannot be treated generically, that is, relational reasoning training should not be isolated from the content with which students are typically or routinely engaged. Rather, it makes sense that relational reasoning be naturally nested within the academic domains that frame the educational experience for students. Thus, future efforts to enact relational reasoning within learning environments demand that educators recognize the place of analogies, anomalies, antitheses, and antinomies in the subjects they teach—whether those subjects are reading, history, mathematics, or science.

Further, teachers must themselves be familiar and comfortable with all forms of relational reasoning and their manifestations in the content of schooling. Likewise, educators at all levels of educational practice must become models of relational reasoning. Those who do not reason relationally, as a habit of mind, or who do not engage in relational reasoning, as a course of action, cannot be expected to promote relational reasoning in those whose academic development they seek to foster. Consequently, it would seem that the path to improved relational reasoning in students must pass through the teachers and the educational systems that those teachers and their students inhabit.

Conclusions

It was my goal in this treatise to introduce the reader to the construct of relational reasoning and to grapple with the way in which relational reasoning may be manifested and measured. The past 5 years have been replete with discoveries and insights about this foundational capacity to find meaningful patterns within the deluge of information that washes over us all. Although there is unquestionably more to be discovered about relational reasoning, I feel that the field has garnered sufficient evidence to move to action. In effect, it is the time to put the knowledge of relational reasoning to work, to harness its potential in service of improved learning and development.

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Peak respiratory severity was classified by an ordinal scale as follows: (1) no oxygen therapy; (2) standard-flow oxygen therapy (<30 L/min); (3) high-flow nasal cannula (≥30 L/min) or noninvasive ventilation; (4) invasive mechanical ventilation; and (5) death.

A, 177 RSV-A sequences from adults aged 18 years and older hospitalized within the Investigating Respiratory Viruses in the Acutely Ill Network of 25 hospitals in 20 US States (tips color coded by State) and 68 contextual sequences from outside the United States collected between January and March 2020, available on Global Initiative on Sharing All Influenza Data. B, 32 RSV-B sequences from adults aged 18 years and older hospitalized within the Investigating Respiratory Viruses in the Acutely Ill Network of 25 hospitals in 20 US States (tips color coded by State) with 46 contextual sequences from outside the United States collected between January and March 2020, available on Global Initiative on Sharing All Influenza Data.

eAppendix 1. Investigators and Collaborators

eTable 1. Underlying Medical Conditions Obtained Through Medical Record Review

eTable 2. Characteristics and In-Hospital Outcomes of Adults Aged ≥18 Years Hospitalized With Coinfections of RSV, SARS-CoV-2, or Influenza

eTable 3. Components of Composite in-Hospital Outcomes Among Adults Aged ≥18 Years Hospitalized With RSV, COVID-19, or Influenza by Vaccination Status

eTable 4. Severity of RSV-Associated Hospitalizations vs COVID-19-Associated Hospitalizations, by Vaccination Status, Among US Adults Aged ≥60 Years

eTable 5. Severity of RSV-Associated Hospitalizations vs Influenza-Associated Hospitalizations, by Vaccination Status, Among US Adults Aged ≥60 Years

eFigure 1. RSV Testing Among Adults Aged ≥18 Years Hospitalized With Acute Respiratory Illness (ARI)

eFigure 2. Adults Aged ≥18 Years Hospitalized With RSV, COVID-19, or Influenza

eFigure 3. Frequency of Adults Aged ≥18 Years Hospitalized With RSV, COVID-19, or Influenza Disease by Admission Week

eFigure 4. Frequency of Adults Aged ≥18 Years Hospitalized With RSV, COVID-19, or Influenza Disease by Admission Week and US Department of Health and Human Services (HHS) Region

eAppendix 2. RSV Primer Pools

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• 17 Department of Medicine, Baystate Medical Center, Springfield, Massachusetts
• 18 School of Public Health, University of Michigan, Ann Arbor
• 19 Department of Medicine, Oregon Health and Sciences University, Portland
• 20 Department of Medicine, Emory University, Atlanta, Georgia
• 21 Department of Medicine, Cleveland Clinic, Cleveland, Ohio
• 22 Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California
• 23 Department of Medicine, University of California, Los Angeles
• 24 Department of Medicine, University of Miami, Miami, Florida
• 25 Department of Medicine, Washington University in St Louis, St Louis, Missouri
• 26 Department of Medicine, The Ohio State University, Columbus
• 27 Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
• 28 Baylor Scott &White Health, Dallas, Texas
• 29 Texas A&M University College of Medicine, Dallas
• 30 Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan
• 31 Division of Infectious Diseases, Henry Ford Health, Detroit, Michigan
• 32 Department of Emergency Medicine, University of Arizona, Tucson
• 33 Yale University School of Medicine, New Haven, Connecticut
• 34 Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 35 Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee
• 36 Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
• 37 Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 38 Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
• 39 Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia

Question   How does disease severity of respiratory syncytial virus (RSV) compare with COVID-19 and influenza among adults hospitalized with these infections?

Findings   In this cohort study of 7998 hospitalized adults aged 18 years and older in 20 US states during February 2022 to May 2023, RSV disease severity was similar to unvaccinated patients hospitalized with COVID-19 or influenza, but substantially more severe than vaccinated patients hospitalized with COVID-19 or influenza disease.

Meaning   These findings suggest that before RSV vaccine introduction in the US, RSV disease was at least as severe as COVID-19 or influenza among unvaccinated patients and more severe than COVID-19 or influenza among vaccinated patients hospitalized with those diseases.

Importance   On June 21, 2023, the Centers for Disease Control and Prevention recommended the first respiratory syncytial virus (RSV) vaccines for adults aged 60 years and older using shared clinical decision-making. Understanding the severity of RSV disease in adults can help guide this clinical decision-making.

Objective   To describe disease severity among adults hospitalized with RSV and compare it with the severity of COVID-19 and influenza disease by vaccination status.

Design, Setting, and Participants   In this cohort study, adults aged 18 years and older admitted to the hospital with acute respiratory illness and laboratory-confirmed RSV, SARS-CoV-2, or influenza infection were prospectively enrolled from 25 hospitals in 20 US states from February 1, 2022, to May 31, 2023. Clinical data during each patient’s hospitalization were collected using standardized forms. Data were analyzed from August to October 2023.

Exposures   RSV, SARS-CoV-2, or influenza infection.

Main Outcomes and Measures   Using multivariable logistic regression, severity of RSV disease was compared with COVID-19 and influenza severity, by COVID-19 and influenza vaccination status, for a range of clinical outcomes, including the composite of invasive mechanical ventilation (IMV) and in-hospital death.

Results   Of 7998 adults (median [IQR] age, 67 [54-78] years; 4047 [50.6%] female) included, 484 (6.1%) were hospitalized with RSV, 6422 (80.3%) were hospitalized with COVID-19, and 1092 (13.7%) were hospitalized with influenza. Among patients with RSV, 58 (12.0%) experienced IMV or death, compared with 201 of 1422 unvaccinated patients with COVID-19 (14.1%) and 458 of 5000 vaccinated patients with COVID-19 (9.2%), as well as 72 of 699 unvaccinated patients with influenza (10.3%) and 20 of 393 vaccinated patients with influenza (5.1%). In adjusted analyses, the odds of IMV or in-hospital death were not significantly different among patients hospitalized with RSV and unvaccinated patients hospitalized with COVID-19 (adjusted odds ratio [aOR], 0.82; 95% CI, 0.59-1.13; P  = .22) or influenza (aOR, 1.20; 95% CI, 0.82-1.76; P  = .35); however, the odds of IMV or death were significantly higher among patients hospitalized with RSV compared with vaccinated patients hospitalized with COVID-19 (aOR, 1.38; 95% CI, 1.02-1.86; P  = .03) or influenza disease (aOR, 2.81; 95% CI, 1.62-4.86; P  < .001).

Conclusions and Relevance   Among adults hospitalized in this US cohort during the 16 months before the first RSV vaccine recommendations, RSV disease was less common but similar in severity compared with COVID-19 or influenza disease among unvaccinated patients and more severe than COVID-19 or influenza disease among vaccinated patients for the most serious outcomes of IMV or death.

Respiratory syncytial virus (RSV) is increasingly recognized as an important cause of severe respiratory disease in adults. An estimated 60 000 to 160 000 RSV-associated hospitalizations and 6000 to 10 000 deaths occur each year among adults aged 65 years and older in the US. 1 - 6 In May 2023, the US Food and Drug Administration approved 2 RSV vaccines for use in adults aged 60 years and older. 7 On June 21, 2023, the Centers for Disease Control and Prevention recommended these new RSV vaccines for adults aged 60 years and older with decisions on whether to be vaccinated based on shared clinical decision-making between patient and health care practitioner. 7 Understanding the severity of RSV disease in adults can help guide this clinical decision-making.

Disease severity from an infection can be affected by host immunity, pathogen virulence, as well as use of therapeutics targeting either the host response or the pathogen. 8 Vaccination strengthens host immunity against infection and its sequelae and has been shown to attenuate both COVID-19 and influenza disease severity. 9 - 11 Because vaccines against COVID-19 and influenza are routinely used by adults in the US, a comparison of disease severity caused by RSV with that of COVID-19 or influenza, by vaccination status, could be useful for framing the potential benefits of RSV vaccination, which may include reduction in disease severity, as observed with COVID-19 and influenza vaccination.

We assessed disease severity among adults hospitalized in the US with RSV during the 16 months immediately preceding recommendations by the Advisory Committee on Immunization Practices for RSV vaccine use. To provide context for the observed severity of RSV disease in hospitalized patients, we compared it with the severity of adults hospitalized with COVID-19 and influenza disease, stratified by vaccination status, during the same period.

This cohort study was determined to be public health surveillance, with a waiver of ethics board review and participant informed consent by each participating institution and the CDC and was conducted consistent with applicable federal law and CDC policy (45 CFR part 46.102(l)(2), 21 CFR part 56; 42 USC §241(d); 5 USC §552a; 44 USC §3501, et seq). This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We prospectively enrolled patients admitted to any of 25 hospitals in 20 US states within the Investigating Respiratory Viruses in the Acutely Ill (IVY) Network. The IVY Network has been continuously conducting observational analyses on respiratory viruses since 2019, is funded by CDC, is coordinated by Vanderbilt University Medical Center, and includes enrolling centers geographically dispersed throughout the continental US (eAppendix 1 in Supplement 1 ). 10 - 13 Enrollment of patients with influenza and COVID-19 began in 2019 and 2020, respectively. Enrollment of patients with RSV began on February 1, 2022. The current analysis included patients enrolled between February 1, 2022, and May 31, 2023, which was the 16 months preceding recommendations for adult RSV vaccines in the US. This analysis expands on information previously published, which was limited to adults aged 60 years and older by assessing different clinical outcomes, stratified by vaccination status, as well as differences by virus subtype and phylogeny among all adults aged 18 years and older hospitalized within the IVY Network during same period. 14

Hospital admissions at each site were reviewed daily and assessed for eligibility. Hospitalized adults aged 18 years and older with symptoms or signs compatible with acute respiratory illness (ARI) who underwent clinical testing for RSV, SARS-COV-2, or influenza as standard of care were eligible for enrollment. ARI was defined as presence of at least 1 of the following: fever, cough, dyspnea, chest imaging findings consistent with pneumonia, or hypoxemia (ie, an oxygen saturation as measured by pulse oximetry <92% or supplemental oxygen use for patients without chronic oxygen needs, or escalation of oxygen therapy for patients who receive chronic supplemental oxygen). Nasal specimens were also obtained from enrolled patients and tested at a central laboratory (Vanderbilt University Medical Center) by reverse transcription–polymerase chain reaction (RT-PCR) for RSV, SARS-CoV-2, and influenza. Patients with nasal specimens with positive test results for RSV, SARS-CoV-2 or influenza within 10 days of illness onset and 3 days of hospital admission, based on either clinical or central laboratory testing, were included.

Medical records were reviewed by trained personnel who abstracted demographic and clinical data, including in-hospital outcomes within 28 days of admission. Patients (or proxies) were interviewed for information on race, ethnicity, current illness, COVID-19 and influenza vaccination status, residence in a long-term care facility, and health care utilization in the previous year. Race and ethnicity were categorized as Black or African American, non-Hispanic; Hispanic or Latino, any race; White, non-Hispanic; other race, non-Hispanic (includes Asian, Native American or Alaska Native, and Native Hawaiian or Other Pacific Islander); and other (includes patients who self-reported their race and ethnicity as other and those for whom race and ethnicity were unknown). Data on race and ethnicity were collected because the association between respiratory viral infection and severe outcomes may vary by race or ethnicity.

The enrollment period for this analysis was prior to the availability of RSV vaccines; hence all patients with RSV infection were unvaccinated. Vaccination status for COVID-19 and influenza were obtained from electronic medical records (EMRs), state or jurisdictional registries, and by self-report. Final vaccination status was determined by combining data from verified documented sources (EMR and registry data) as well as plausible self-report based on date of vaccination. 13 For this analysis, enrolled patients were excluded if they had confirmed or inconclusive laboratory test results indicating coinfection or possible coinfection with RSV, SARS-CoV-2, or influenza; had unknown vaccination status for COVID-19 or influenza when hospitalized with the respective virus; had only 1 mRNA vaccine (BNT162b2 [Pfizer-BioNTech] or mRNA-1273 [Moderna]) dose or 1 recombinant S protein vaccine (NVX-CoV2373 [Novavax]) dose when hospitalized with COVID-19; had unknown in-hospital outcomes; or were identified as ineligible for enrollment after data cleaning.

Patients were classified into 2 COVID-19 vaccination groups: unvaccinated, defined as no prior receipt of COVID-19 vaccination; and vaccinated, if they had received at least a primary COVID-19 vaccine series or bivalent COVID-19 vaccine dose at least 7 days before illness onset (eMethods in Supplement 1 ). Patients who received bivalent COVID-19 vaccination may have previously received 1 to 5 doses of the original (ancestral strain) monovalent vaccines. Influenza vaccination status was classified into 2 groups: unvaccinated, defined as no receipt of influenza vaccine during the current season based on admission date or vaccinated, if they had received the current season’s influenza vaccination at least 14 days before illness onset.

Disease severity was characterized using clinical data during the patient’s hospital admission, beginning at initial hospital presentation, and ending at the earliest of hospital discharge, patient death, or the end of hospital day 28. Using these data, we created 6 nonmutually exclusive in-hospital outcomes: (1) supplemental oxygen therapy, defined as supplemental oxygen at any flow rate and by any device for patients not receiving chronic oxygen therapy or with escalation of oxygen therapy for patients receiving chronic oxygen therapy; (2) respiratory failure treated with advanced respiratory support, defined as a composite of new receipt of high-flow nasal cannula (HFNC), noninvasive ventilation (NIV) or invasive mechanical ventilation (IMV); (3) acute organ failure, defined as a composite of respiratory failure (new receipt of HFNC, NIV, or IMV), cardiovascular failure (use of vasopressors), or kidney failure (new receipt of kidney replacement therapy); (4) intensive care unit (ICU) admission; (5) hospital-free days to day 28, which is an ordinal composite of in-hospital death and hospital length of stay, defined as the number of days alive and out of the hospital between admission and 28 days later, with in-hospital death coded as −1 15 ; and (6) a composite of IMV or death (eMethods in Supplement 1 ).

In addition to these nonmutually exclusive outcomes, we also generated mutually exclusive outcome categories based on a hierarchy of respiratory disease severity. An ordinal outcome, peak respiratory disease severity, was constructed with the following 5 categories: no oxygen therapy, standard-flow oxygen therapy, HFNC or NIV, IMV, and death. Each patient was classified into 1 category based on the highest category achieved during the hospitalization through day 28.

Nasal swabs were obtained from enrolled patients and tested for RSV, SARS-CoV-2, and influenza by RT-PCR at a central laboratory (Vanderbilt University Medical Center, Nashville, Tennessee) (eMethods in Supplement 1 ). Viral whole genome sequencing was performed at the University of Michigan on specimens with positive results by central RT-PCR testing for RSV, SARS-CoV-2, or influenza (eMethods in Supplement 1 and eAppendix 2 in Supplement 2 ). Maximum likelihood phylogenetic trees were generated using IQ-TREE with a GTR model and visualized and annotated using ggtree. 16 Strain names and Global Initiative on Sharing All Influenza Data accession identifications are provided in eAppendix 3 Supplement 3 .

Demographics, clinical characteristics, and outcomes of enrolled patients were described by infecting virus (RSV, SARS-CoV-2, influenza) as well as by viral subtype (A and B subtypes for RSV; Omicron sublineages BA.1, BA.2, BA.4/5, BQ.1, and XBB.1.5 for SARS-CoV-2; and influenza A[H3N2] and A[H1N1]) and vaccination status (unvaccinated or vaccinated against COVID-19 or influenza). In-hospital outcomes were compared between patients hospitalized with RSV, COVID-19, and influenza disease among enrolled patients. A series of multivariable regression models were used to compare outcomes of patients hospitalized with RSV disease with 4 comparator groups: (1) patients hospitalized with COVID-19 without prior COVID-19 vaccination (unvaccinated COVID-19 group), (2) patients hospitalized with COVID-19 and previous COVID-19 vaccination (vaccinated COVID-19 group), (3) patients hospitalized with influenza disease without prior vaccination with the current season’s influenza vaccine (unvaccinated influenza group), and (4) patients hospitalized with influenza with receipt of the current season’s influenza vaccine (vaccinated influenza group). Models were adjusted for age, sex, self-reported race and ethnicity, number of organ systems with a chronic medical condition (eTable 1 in Supplement 1 ), and geographic region (US Department of Health and Human Services Region). Dichotomous outcomes were analyzed with multivariable logistic regression models. Ordinal outcomes, including hospital-free days and peak respiratory disease severity, were analyzed with multivariable proportional odds models. Because of the potential for type I error owing to multiple comparisons, findings from secondary analyses should be interpreted as exploratory. Statistical significance was indicated by a 2-sided P  < .05.

In a sensitivity analysis, we repeated the severity comparisons for RSV, COVID-19, and influenza while restricting the population to adults aged 60 years and older. Currently, RSV vaccines are only recommended for adults aged 60 years and older, although adults of different ages may be considered in the future. Thus, we reported results for adults of all ages (≥18 years) as the primary analysis and results for adults aged 60 years and older as a secondary analysis.

Only patients with complete data for models were analyzed; missing data were not imputed. The numbers of patients with missing data were reported. All analyses were conducted using SAS software version 9.4 (SAS Institute). Data were analyzed from August to October 2023.

Between February 1, 2022 and May 31, 2023, a total of 9117 adults aged 18 years and older hospitalized with ARI had laboratory-confirmed RSV, SARS-CoV-2, or influenza based on either clinical or central laboratory testing and were enrolled. This included 34 patients (0.5%) identified with RSV after central testing of 6759 enrolled patients who did not undergo clinical testing for RSV (eFigure 1 in Supplement 1 ). Among 9117 patients with clinical or central laboratory-confirmed RSV, SARS-CoV-2, or influenza infection, 1119 were excluded from this analysis primarily due to confirmed coinfection (200 patients) (eTable 2 in Supplement 1 ), possible coinfection (376 patients), patients with COVID-19 with only 1 mRNA or 1 recombinant S protein vaccine dose (230 patients), unknown COVID-19 or influenza vaccination history (198 patients), or unknown clinical outcomes (70 patients) (eFigure 2 in Supplement 1 ). The final sample included 7998 adults (median [IQR] age, 67 [54-78] years; 4047 [50.6%] female).

Among 7998 included patients with test results positive for RSV, COVID-19, or influenza, 484 (6.1%) were hospitalized with RSV, 6422 (80.3%) were hospitalized with COVID-19, and 1092 (13.7%) were hospitalized with influenza. Peak hospitalizations for both RSV and influenza occurred during November to December 2022, whereas high numbers of COVID-19 hospitalizations occurred throughout the analysis period (eFigure 3 and eFigure 4 in Supplement 1 ).

Adults hospitalized with RSV were younger than those hospitalized with COVID-19 (median [IQR] age, 65 [53-75] years vs 68 [56-78] years; P  = .002), with no difference compared with those hospitalized with influenza disease (median [IQR] age, 64 [50-74] years; P  = .09) ( Table 1 ). A higher percentage of patients hospitalized with RSV had an ethnicity and race described as non-Hispanic Black compared with those hospitalized with COVID-19 (23.8% vs 19.4%; P  < .01), but this percentage was similar among influenza patients (115 patients [23.8%] vs 271 patients [27.9%]; P  = .34). Patients hospitalized with RSV and COVID-19 had similar proportions of underlying immunocompromising conditions (99 patients [20.5%] vs 1135 patients [17.7%]; P  = .12), but patients with RSV were more likely to have immunocompromising conditions than influenza patients (149 patients [13.6%]; P  < .001). Chronic cardiovascular and pulmonary conditions were common among patients with each of the viruses. Patients hospitalized with RSV were more likely to self-report dyspnea than patients with either COVID-19 (385 patients [79.6%] vs 3916 patients [61.0%]; P  < .001) or influenza (788 patients [72.2%]; P  = .002). Of 6422 patients with COVID-19, 5000 (77.9%) were classified as vaccinated and were a median (IQR) of 259 days (152-407) days from last COVID-19 vaccination. Of 1092 patients with influenza, 393 (36.0%) had received seasonal influenza vaccination.

Overall, most outcomes revealed disease severity among patients with RSV that was not significantly different from patients with unvaccinated COVID-19 and influenza and substantially higher than patients with vaccinated COVID-19 and influenza ( Table 2 ). For example, the outcome of IMV or death was experienced by 58 of 484 patients with RSV (12.0%); 201 of 1422 unvaccinated patients with COVID-19 (14.1%) (adjusted odds ratio [aOR], 0.82; 95% CI, 0.59-1.13); 458 of 5000 vaccinated patients with COVID-19 (9.2%) (aOR, 1.38; 95% CI, 1.02-1.86); 72 of 699 unvaccinated patients with influenza (10.3%) (aOR, 1.20; 95% CI, 0.82-1.76); and 20 of 393 vaccinated patients with influenza (5.1%) (aOR, 2.81; 95% CI, 1.62-4.86). Patients with RSV were more than twice as likely to receive advanced respiratory support than vaccinated patients with COVID-19 (aOR, 2.03; 95% CI, 1.64-2.51) or vaccinated patients with influenza (aOR, 2.71; 95% CI, 1.89-3.87). Frequencies and proportions of each outcome and their components are shown in eTable 3 in Supplement 1 .

Similar results were found when comparing RSV disease severity with COVID-19 and influenza disease among adults aged 60 years and older (eTable 4 and eTable 5 in Supplement 1 ).

When evaluating peak respiratory disease severity, patients with RSV had higher overall severity compared with the unvaccinated COVID-19 group (aOR, 1.54; 95% CI, 1.27-1.86) and the unvaccinated influenza group (aOR, 1.48; 95% CI, 1.18-1.85) and substantially higher overall disease severity compared with the vaccinated COVID-19 group (aOR, 2.16; 95% CI, 1.82-2.57) and the vaccinated influenza group (aOR, 2.40; 95% CI, 1.83-3.14) ( Figure 1 ).

Of 368 patients with RSV subtype identified, 250 (67.9%) were subtype A and 118 (32.1%) were subtype B. Whole-genome sequencing of RSV subtypes A and B found that lineages circulating during the period of this analysis derived from lineages circulating in early 2020 (before the COVID-19 pandemic in the US) ( Figure 2 ). Clinical outcomes were overall similar between RSV subtype A and B ( Table 3 ). All patients hospitalized with COVID-19 had SARS-CoV-2 Omicron lineages. Among 3466 patients with identified SARS-CoV-2 lineages, BA.1 was the least frequent Omicron lineage (217 patients [6.3%]) but demonstrated the greatest severity ( Table 3 ). Among 649 patients hospitalized with influenza infection and identified subtype, influenza A(H3N2) predominated during the analysis period (474 patients [73.0%]) and had lower or similar severity than influenza A(H1N1) ( Table 3 ).

In this prospective, multicenter cohort study of adults hospitalized in 20 US states during the 16 months preceding the firstUS adult RSV vaccination recommendation, the frequency of RSV hospitalizations was substantially lower than for COVID-19 and influenza; RSV hospitalizations were approximately one-fourteenth as common as COVID-19 hospitalizations and one-half as common as influenza hospitalization. Disease severity of RSV was similar to COVID-19 and influenza among unvaccinated patients and higher than COVID-19 and influenza among vaccinated patients hospitalized with those diseases for the critical outcomes of ICU admission and IMV or death. RSV genomes sequenced from this cohort derived from early 2020 lineages, suggesting the virus circulating during the period of this analysis was similar to RSV that circulated before the COVID-19 pandemic. This evaluation of RSV epidemiology during a period of endemic COVID-19 demonstrates that RSV is a serious respiratory infection in adults, and especially older adults. 14 Newly approved RSV vaccines for adults aged 60 years and older have the potential to reduce this severity, similar to attenuation of disease severity achieved with COVID-19 and influenza vaccination, as previously reported 9 - 11 and also observed in this analysis.

Although several studies have previously compared severity of RSV with influenza disease, strengths of this analysis include comparisons of RSV (which was not vaccine preventable during the period of this analysis) with unvaccinated COVID-19 and influenza disease to separate the association of vaccination in attenuating disease severity from the direct effects of the pathogen. By stratifying our COVID-19 and influenza populations by vaccination status, we show that critical outcomes of ICU admission and IMV or death occurred in a similar proportion of unvaccinated adults hospitalized with RSV compared with unvaccinated adults hospitalized with COVID-19 or influenza. Although outcome definitions varied across studies and analyses were not stratified by vaccination status in prior studies, most prior work suggested RSV disease was more severe than influenza disease among hospitalized adults, including use of supplemental oxygen, IMV, or ICU admission. 17 - 20

Three prior studies have compared RSV disease severity with COVID-19 and suggested lower severity for RSV compared with COVID-19. 19 , 21 , 22 However, 2 of these studies 19 , 21 were conducted using data from 2020, when COVID-19 vaccines were not available and Omicron lineages were not in circulation. Between 2020 and 2022 to 2023, the clinical manifestations of COVID-19 evolved considerably due to the emergence of the Omicron variant, increases in population-level immunity from both vaccination and infection, and improvements in clinical care, including increases in use of antiviral treatments. 23 , 24 As a consequence, the severity of COVID-19 has declined, and the severity of RSV disease is now relatively high compared with COVID-19 from Omicron lineages.

An additional strength of this analysis is that respiratory specimens were obtained from participants, which allowed for subsequent characterization into subtypes by RT-PCR and into lineages based on whole genome sequencing. RSV A and B subtypes have been shown to cocirculate, although 1 subtype often predominates in each season. 25 There are limited data comparing severity between RSV A and B subtypes in adults. Our findings demonstrate very similar patient characteristics and clinical outcomes between RSV A and B subtypes, consistent with studies from before the COVID-19 pandemic. 26 , 27 Similar to prior work during the 2022 RSV surge, we demonstrated expected RSV genomic divergence from early 2020 lineages, suggesting that the severity of RSV disease observed in this analysis is unlikely to have resulted from major genomic changes in RSV during the COVID-19 pandemic. 28

The high disease severity observed among older adults without previous RSV vaccination in this analysis is important to guide decision-making for RSV vaccination in this population. Both clinical trials that led to Food and Drug Administration approval of RSV vaccines for adults aged 60 years and older showed moderate to high efficacy of RSV vaccination against lower respiratory tract disease, which is in the causal pathway leading to severe disease. 29 , 30 Although additional studies are needed to assess the protection of these vaccines against severe respiratory disease in older adults, our results suggest that there is a burden of disease beyond prevention of RSV hospitalization—the reduction of in-hospital RSV disease severity—that could occur with RSV vaccination, as shown for COVID-19 and influenza vaccination in both previous studies and this analysis. 9 - 11

This analysis is subject to limitations. First, it is possible that RSV was preferentially detected among patients who were more severely ill and therefore more likely to receive clinical testing for RSV at participating hospitals and be subsequently enrolled. However, most enrolled patients had nasal swabs tested at a central laboratory for RSV, SARS-CoV-2, and influenza, lessening the potential bias of detecting RSV among patients who were more severely ill based on clinical testing only. During the period of this analysis, we enrolled 6759 adults aged 18 years and older hospitalized with ARI who did not have clinical testing for RSV; only 34 (0.5%) of these patients had a positive RSV test result based on central testing, suggesting the number of undetected RSV hospitalizations was likely low. Second, treatment with antiviral and immunomodulatory medications was not considered in analyses. Quantifying the effect of treatment on observed severity and accounting for it in severity comparisons is complicated by several factors, including that there are currently no routine treatments available RSV; indications for COVID-19 inpatient treatment is often based on severity, making it difficult to disentangle the associations between treatment and observed severity; and COVID-19 treatment practice varied considerably during the analysis period. We presented respiratory virus groups (RSV, COVID-19, and influenza) stratified by vaccination status without stratification or adjustment for acute treatments; the presented severity levels for COVID-19 and influenza represent a mix of patients who did and did not receive antiviral and immunomodulatory treatments.

In this cohort study among adults hospitalized in the US during the 16 months preceding recommendations for the first adult RSV vaccines, RSV disease severity was similar to severity of COVID-19 and influenza disease among unvaccinated patients and substantially higher than COVID-19 and influenza disease among vaccinated patients. Severity of RSV disease among adults may be important to consider as RSV vaccine policy evolves.

Accepted for Publication: February 6, 2024.

Published: April 4, 2024. doi:10.1001/jamanetworkopen.2024.4954

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Surie D et al. JAMA Network Open .

Corresponding Author: Diya Surie, MD, Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS H18-6, Atlanta, GA 30329 ( [email protected] ).

Author Contributions: Ms Yuengling and Dr Zhu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Surie, DeCuir, Brown, Gibbs, Khan, Bender, Wilson, Qadir, Casey, Rice, Halasa, Grijalva, Lewis, Ellington, McMorrow, Martin, Self.

Acquisition, analysis, or interpretation of data: Surie, Yuengling, DeCuir, Zhu, Lauring, Gaglani, Ghamande, Peltan, Brown, Ginde, Martinez, Mohr, Hager, Ali, Prekker, Gong, Mohamed, N. Johnson, Srinivasan, Steingrub, Leis, Khan, Hough, Bender, Duggal, Bendall, Wilson, Qadir, Chang, Mallow, Kwon, Exline, Shapiro, Columbus, Vaughn, Ramesh, Mosier, Safdar, Casey, Talbot, Rice, Halasa, Chappell, Baughman, Womack, Swan, C. Johnson, Lwin, Lewis, Ellington, McMorrow, Martin, Self.

Drafting of the manuscript: Surie, Hager, Khan, Shapiro, Casey, Halasa, Self.

Critical review of the manuscript for important intellectual content: Surie, Yuengling, DeCuir, Zhu, Lauring, Gaglani, Ghamande, Peltan, Brown, Ginde, Martinez, Mohr, Gibbs, Hager, Ali, Prekker, Gong, Mohamed, N. Johnson, Srinivasan, Steingrub, Leis, Khan, Hough, Bender, Duggal, Bendall, Wilson, Qadir, Chang, Mallow, Kwon, Exline, Columbus, Vaughn, Ramesh, Mosier, Safdar, Talbot, Rice, Chappell, Grijalva, Baughman, Womack, Swan, C. Johnson, Lwin, Lewis, Ellington, McMorrow, Martin, Self.

Statistical analysis: Yuengling, DeCuir, Zhu, Bender, C. Johnson, Lwin.

Obtained funding: Surie, Talbot, Halasa, McMorrow, Martin, Self.

Administrative, technical, or material support: Surie, Lauring, Martinez, Mohr, Hager, Ali, Prekker, Gong, Steingrub, Bender, Duggal, Wilson, Qadir, Mallow, Kwon, Exline, Vaughn, Mosier, Safdar, Baughman, Womack, Ellington, McMorrow, Martin, Self.

Supervision: Surie, Gaglani, Ghamande, Gibbs, Hager, Gong, N. Johnson, Srinivasan, Bender, Mallow, Kwon, Exline, Vaughn, Talbot, Halasa, McMorrow, Martin, Self.

Conflict of Interest Disclosures: Dr Lauring reported receiving personal fees from Roche outside the submitted work. Dr Gaglani reported receiving grants from the Centers for Disease Control and Prevention (CDC), Abt Associates, and Westat; personal fees from the CDC; and serving as cochair for the Texas Pediatric Society, Texas Chapter of the American Academy of Pediatrics, Infectious Diseases and Immunization Committee outside the submitted work. Dr Peltan reported receiving grants from Janssen Pharmaceuticals and Regeneron outside the submitted work. Dr Brown reported having a patent for an airway device with royalties paid from ReddyPort. Dr Ginde reported receiving grants from the National Institutes of Health (NIH), Department of Defense (DOD), Faron Pharmaceuticals, AbbVie, and Biomeme and personal fees from SeaStar outside the submitted work. Dr Gibbs reported receiving grants from NIH and grants from DOD outside the submitted work. Dr Hager reported receiving grants from NIH outside the submitted work. Dr Gong reported receiving grants from NIH and personal fess from Philips outside the submitted work. Dr N. Johnson reported receiving grants from NIH and serving on an advisory board for Neuroptics outside the submitted work. Dr Khan reported receiving grants from Dompe Pharmaceuticals, 4D Medical, Eli Lilly, and United Therapeutics outside the submitted work. Dr Hough reported receiving grants from NIH outside the submitted work. Dr Duggal reported receiving grants from NIH and personal fees from ALung Technologies outside the submitted work. Dr Wilson reported receiving grants from NIH outside the submitted work. Dr Chang reported receiving personal fees from PureTech Health outside the submitted work. Dr Mallow reported receiving personal fees from Medical Legal Consulting outside the submitted work. Dr Vaughn reported receiving grants from CDC outside the submitted work. Dr Ramesh reported receiving personal fees from Moderna, Pfizer, and AstraZeneca outside the submitted work. Dr Safdar reported receiving grants from CDC, National Heart, Lung, and Blood Institute (NHLBI), and Comprehensive Research Associates outside the submitted work. Dr Casey reported receiving personal fees from Fisher & Paykel outside the submitted work. Dr Rice reported receiving grants from NIH NHLBI and personal fees from Cumberland Pharmaceuticals and Sanofi outside the submitted work. Dr Halasa reported receiving grants from Sanofi, Quidel, and Merck outside the submitted work. Dr Chappell reported receiving grants from Merck and research support from CDC, NIH, and DOD outside the submitted work. Dr Grijalva reported receiving grants from NIH, CDC, Agency for Healthcare Research and Quality (AHRQ), and Food and Drug Administration and personal fees from Merck and Syneos Health outside the submitted work. Dr Martin reported receiving grants from Merck outside the submitted work. No other disclosures were reported.

Funding/Support: This work was funded by the CDC (contract No. 75D30122C12914 and 75D30122C14944; paid to Vanderbilt University Medical Center).

Role of the Funder/Sponsor: Investigators from CDC were involved in all aspects of the analysis, including the design and conduct of the activity, collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The CDC had the right to control decisions about publication via the CDC publication clearance process.

Group Information: The investigators and collaborators of the Investigating Respiratory Viruses in the Acutely Ill (IVY) Network are listed in eAppendix 1 in Supplement 1 .

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.

Data Sharing Statement: See Supplement 4 .

Additional Contributions: We gratefully acknowledge all the participants and data contributors, including laboratories for generating the genetic sequence and metadata and sharing via the Global Initiative on Sharing All Influenza Data Initiative, on which some of these results are based.

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