a research report from an independent measures study states

Experimental Design: Types, Examples & Methods

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

There is unequivocal evidence that Earth is warming at an unprecedented rate. Human activity is the principal cause.

• While Earth’s climate has changed throughout its history , the current warming is happening at a rate not seen in the past 10,000 years.
• According to the Intergovernmental Panel on Climate Change ( IPCC ), "Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact." 1
• Scientific information taken from natural sources (such as ice cores, rocks, and tree rings) and from modern equipment (like satellites and instruments) all show the signs of a changing climate.
• From global temperature rise to melting ice sheets, the evidence of a warming planet abounds.

The rate of change since the mid-20th century is unprecedented over millennia.

Earth's climate has changed throughout history. Just in the last 800,000 years, there have been eight cycles of ice ages and warmer periods, with the end of the last ice age about 11,700 years ago marking the beginning of the modern climate era — and of human civilization. Most of these climate changes are attributed to very small variations in Earth’s orbit that change the amount of solar energy our planet receives.

The current warming trend is different because it is clearly the result of human activities since the mid-1800s, and is proceeding at a rate not seen over many recent millennia. 1 It is undeniable that human activities have produced the atmospheric gases that have trapped more of the Sun’s energy in the Earth system. This extra energy has warmed the atmosphere, ocean, and land, and widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere have occurred.

Earth-orbiting satellites and new technologies have helped scientists see the big picture, collecting many different types of information about our planet and its climate all over the world. These data, collected over many years, reveal the signs and patterns of a changing climate.

Scientists demonstrated the heat-trapping nature of carbon dioxide and other gases in the mid-19th century. 2 Many of the science instruments NASA uses to study our climate focus on how these gases affect the movement of infrared radiation through the atmosphere. From the measured impacts of increases in these gases, there is no question that increased greenhouse gas levels warm Earth in response.

Scientific evidence for warming of the climate system is unequivocal.

Intergovernmental Panel on Climate Change

Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that Earth’s climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly 10 times faster than the average rate of warming after an ice age. Carbon dioxide from human activities is increasing about 250 times faster than it did from natural sources after the last Ice Age. 3

The Evidence for Rapid Climate Change Is Compelling:

Global Temperature Is Rising

The planet's average surface temperature has risen about 2 degrees Fahrenheit (1 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide emissions into the atmosphere and other human activities. 4 Most of the warming occurred in the past 40 years, with the seven most recent years being the warmest. The years 2016 and 2020 are tied for the warmest year on record. 5 Image credit: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

The Ocean Is Getting Warmer

The ocean has absorbed much of this increased heat, with the top 100 meters (about 328 feet) of ocean showing warming of 0.67 degrees Fahrenheit (0.33 degrees Celsius) since 1969. 6 Earth stores 90% of the extra energy in the ocean. Image credit: Kelsey Roberts/USGS

The Ice Sheets Are Shrinking

The Greenland and Antarctic ice sheets have decreased in mass. Data from NASA's Gravity Recovery and Climate Experiment show Greenland lost an average of 279 billion tons of ice per year between 1993 and 2019, while Antarctica lost about 148 billion tons of ice per year. 7 Image: The Antarctic Peninsula, Credit: NASA

Glaciers Are Retreating

Glaciers are retreating almost everywhere around the world — including in the Alps, Himalayas, Andes, Rockies, Alaska, and Africa. 8 Image: Miles Glacier, Alaska Image credit: NASA

Snow Cover Is Decreasing

Satellite observations reveal that the amount of spring snow cover in the Northern Hemisphere has decreased over the past five decades and the snow is melting earlier. 9 Image credit: NASA/JPL-Caltech

Sea Level Is Rising

Global sea level rose about 8 inches (20 centimeters) in the last century. The rate in the last two decades, however, is nearly double that of the last century and accelerating slightly every year. 10 Image credit: U.S. Army Corps of Engineers Norfolk District

Arctic Sea Ice Is Declining

Both the extent and thickness of Arctic sea ice has declined rapidly over the last several decades. 11 Credit: NASA's Scientific Visualization Studio

Extreme Events Are Increasing in Frequency

The number of record high temperature events in the United States has been increasing, while the number of record low temperature events has been decreasing, since 1950. The U.S. has also witnessed increasing numbers of intense rainfall events. 12 Image credit: Régine Fabri,  CC BY-SA 4.0 , via Wikimedia Commons

Ocean Acidification Is Increasing

Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30%. 13 , 14 This increase is due to humans emitting more carbon dioxide into the atmosphere and hence more being absorbed into the ocean. The ocean has absorbed between 20% and 30% of total anthropogenic carbon dioxide emissions in recent decades (7.2 to 10.8 billion metric tons per year). 1 5 , 16 Image credit: NOAA

1. IPCC Sixth Assessment Report, WGI, Technical Summary . B.D. Santer et.al., “A search for human influences on the thermal structure of the atmosphere.” Nature 382 (04 July 1996): 39-46. https://doi.org/10.1038/382039a0. Gabriele C. Hegerl et al., “Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method.” Journal of Climate 9 (October 1996): 2281-2306. https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2. V. Ramaswamy, et al., “Anthropogenic and Natural Influences in the Evolution of Lower Stratospheric Cooling.” Science 311 (24 February 2006): 1138-1141. https://doi.org/10.1126/science.1122587. B.D. Santer et al., “Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes.” Science 301 (25 July 2003): 479-483. https://doi.org/10.1126/science.1084123. T. Westerhold et al., "An astronomically dated record of Earth’s climate and its predictability over the last 66 million years." Science 369 (11 Sept. 2020): 1383-1387. https://doi.org/10.1126/science.1094123

2. In 1824, Joseph Fourier calculated that an Earth-sized planet, at our distance from the Sun, ought to be much colder. He suggested something in the atmosphere must be acting like an insulating blanket. In 1856, Eunice Foote discovered that blanket, showing that carbon dioxide and water vapor in Earth's atmosphere trap escaping infrared (heat) radiation. In the 1860s, physicist John Tyndall recognized Earth's natural greenhouse effect and suggested that slight changes in the atmospheric composition could bring about climatic variations. In 1896, a seminal paper by Swedish scientist Svante Arrhenius first predicted that changes in atmospheric carbon dioxide levels could substantially alter the surface temperature through the greenhouse effect. In 1938, Guy Callendar connected carbon dioxide increases in Earth’s atmosphere to global warming. In 1941, Milutin Milankovic linked ice ages to Earth’s orbital characteristics. Gilbert Plass formulated the Carbon Dioxide Theory of Climate Change in 1956.

3. IPCC Sixth Assessment Report, WG1, Chapter 2 Vostok ice core data; NOAA Mauna Loa CO2 record O. Gaffney, W. Steffen, "The Anthropocene Equation." The Anthropocene Review 4, issue 1 (April 2017): 53-61. https://doi.org/abs/10.1177/2053019616688022.

4. https://www.ncei.noaa.gov/monitoring https://crudata.uea.ac.uk/cru/data/temperature/ http://data.giss.nasa.gov/gistemp

5. https://www.giss.nasa.gov/research/news/20170118/

6. S. Levitus, J. Antonov, T. Boyer, O Baranova, H. Garcia, R. Locarnini, A. Mishonov, J. Reagan, D. Seidov, E. Yarosh, M. Zweng, " NCEI ocean heat content, temperature anomalies, salinity anomalies, thermosteric sea level anomalies, halosteric sea level anomalies, and total steric sea level anomalies from 1955 to present calculated from in situ oceanographic subsurface profile data (NCEI Accession 0164586), Version 4.4. (2017) NOAA National Centers for Environmental Information. https://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/index3.html K. von Schuckmann, L. Cheng, L,. D. Palmer, J. Hansen, C. Tassone, V. Aich, S. Adusumilli, H. Beltrami, H., T. Boyer, F. Cuesta-Valero, D. Desbruyeres, C. Domingues, A. Garcia-Garcia, P. Gentine, J. Gilson, M. Gorfer, L. Haimberger, M. Ishii, M., G. Johnson, R. Killick, B. King, G. Kirchengast, N. Kolodziejczyk, J. Lyman, B. Marzeion, M. Mayer, M. Monier, D. Monselesan, S. Purkey, D. Roemmich, A. Schweiger, S. Seneviratne, A. Shepherd, D. Slater, A. Steiner, F. Straneo, M.L. Timmermans, S. Wijffels. "Heat stored in the Earth system: where does the energy go?" Earth System Science Data 12, Issue 3 (07 September 2020): 2013-2041. https://doi.org/10.5194/essd-12-2013-2020.

7. I. Velicogna, Yara Mohajerani, A. Geruo, F. Landerer, J. Mouginot, B. Noel, E. Rignot, T. Sutterly, M. van den Broeke, M. Wessem, D. Wiese, "Continuity of Ice Sheet Mass Loss in Greenland and Antarctica From the GRACE and GRACE Follow-On Missions." Geophysical Research Letters 47, Issue 8 (28 April 2020): e2020GL087291. https://doi.org/10.1029/2020GL087291.

8. National Snow and Ice Data Center World Glacier Monitoring Service

9. National Snow and Ice Data Center D.A. Robinson, D. K. Hall, and T. L. Mote, "MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0, Version 1 (2017). Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0530.001 . http://nsidc.org/cryosphere/sotc/snow_extent.html Rutgers University Global Snow Lab. Data History

10. R.S. Nerem, B.D. Beckley, J. T. Fasullo, B.D. Hamlington, D. Masters, and G.T. Mitchum, "Climate-change–driven accelerated sea-level rise detected in the altimeter era." PNAS 15, no. 9 (12 Feb. 2018): 2022-2025. https://doi.org/10.1073/pnas.1717312115.

11. https://nsidc.org/cryosphere/sotc/sea_ice.html Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock, 2003) http://psc.apl.washington.edu/research/projects/arctic-sea-ice-volume-anomaly/ http://psc.apl.uw.edu/research/projects/projections-of-an-ice-diminished-arctic-ocean/

12. USGCRP, 2017: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 470 pp, https://doi.org/10.7930/j0j964j6 .

13. http://www.pmel.noaa.gov/co2/story/What+is+Ocean+Acidification%3F

14. http://www.pmel.noaa.gov/co2/story/Ocean+Acidification

15. C.L. Sabine, et al., “The Oceanic Sink for Anthropogenic CO2.” Science 305 (16 July 2004): 367-371. https://doi.org/10.1126/science.1097403.

16. Special Report on the Ocean and Cryosphere in a Changing Climate , Technical Summary, Chapter TS.5, Changing Ocean, Marine Ecosystems, and Dependent Communities, Section 5.2.2.3. https://www.ipcc.ch/srocc/chapter/technical-summary/

Header image shows clouds imitating mountains as the sun sets after midnight as seen from Denali's backcountry Unit 13 on June 14, 2019. Credit: NPS/Emily Mesner Image credit in list of evidence: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

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Revolutionizing the Study of Mental Disorders

March 27, 2024 • Feature Story • 75th Anniversary

At a Glance:

• The Research Domain Criteria framework (RDoC) was created in 2010 by the National Institute of Mental Health.
• The framework encourages researchers to examine functional processes that are implemented by the brain on a continuum from normal to abnormal.
• This way of researching mental disorders can help overcome inherent limitations in using all-or-nothing diagnostic systems for research.
• Researchers worldwide have taken up the principles of RDoC.
• The framework continues to evolve and update as new information becomes available.

President George H. W. Bush proclaimed  the 1990s “ The Decade of the Brain  ,” urging the National Institutes of Health, the National Institute of Mental Health (NIMH), and others to raise awareness about the benefits of brain research.

“Over the years, our understanding of the brain—how it works, what goes wrong when it is injured or diseased—has increased dramatically. However, we still have much more to learn,” read the president’s proclamation. “The need for continued study of the brain is compelling: millions of Americans are affected each year by disorders of the brain…Today, these individuals and their families are justifiably hopeful, for a new era of discovery is dawning in brain research.”

Still, despite the explosion of new techniques and tools for studying the brain, such as functional magnetic resonance imaging (fMRI), many mental health researchers were growing frustrated that their field was not progressing as quickly as they had hoped.

For decades, researchers have studied mental disorders using diagnoses based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)—a handbook that lists the symptoms of mental disorders and the criteria for diagnosing a person with a disorder. But, among many researchers, suspicion was growing that the system used to diagnose mental disorders may not be the best way to study them.

“There are many benefits to using the DSM in medical settings—it provides reliability and ease of diagnosis. It also provides a clear-cut diagnosis for patients, which can be necessary to request insurance-based coverage of healthcare or job- or school-based accommodations,” said Bruce Cuthbert, Ph.D., who headed the workgroup that developed NIMH’s Research Domain Criteria Initiative. “However, when used in research, this approach is not always ideal.”

Researchers would often test people with a specific diagnosed DSM disorder against those with a different disorder or with no disorder and see how the groups differed. However, different mental disorders can have similar symptoms, and people can be diagnosed with several different disorders simultaneously. In addition, a diagnosis using the DSM is all or none—patients either qualify for the disorder based on their number of symptoms, or they don’t. This black-and-white approach means there may be people who experience symptoms of a mental disorder but just miss the cutoff for diagnosis.

Dr. Cuthbert, who is now the senior member of the RDoC Unit which orchestrates RDoC work, stated that “Diagnostic systems are based on clinical signs and symptoms, but signs and symptoms can’t really tell us much about what is going on in the brain or the underlying causes of a disorder. With modern neuroscience, we were seeing that information on genetic, pathophysiological, and psychological causes of mental disorders did not line up well with the current diagnostic disorder categories, suggesting that there were central processes that relate to mental disorders that were not being reflected in DMS-based research.”

Road to evolution

Concerned about the limits of using the DSM for research, Dr. Cuthbert, a professor of clinical psychology at the University of Minnesota at the time, approached Dr. Thomas Insel (then NIMH director) during a conference in the autumn of 2008. Dr. Cuthbert recalled saying, “I think it’s really important that we start looking at dimensions of functions related to mental disorders such as fear, working memory, and reward systems because we know that these dimensions cut across various disorders. I think NIMH really needs to think about mental disorders in this new way.”

Dr. Cuthbert didn’t know it then, but he was suggesting something similar to ideas that NIMH was considering. Just months earlier, Dr. Insel had spearheaded the inclusion of a goal in NIMH’s 2008 Strategic Plan for Research to “develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.”

Unaware of the new strategic goal, Dr. Cuthbert was surprised when Dr. Insel's senior advisor, Marlene Guzman, called a few weeks later to ask if he’d be interested in taking a sabbatical to help lead this new effort. Dr. Cuthbert soon transitioned into a full-time NIMH employee, joining the Institute at an exciting time to lead the development of what became known as the Research Domain Criteria (RDoC) Framework. The effort began in 2009 with the creation of an internal working group of interdisciplinary NIMH staff who identified core functional areas that could be used as examples of what research using this new conceptual framework looked like.

The workgroup members conceived a bold change in how investigators studied mental disorders.

“We wanted researchers to transition from looking at mental disorders as all or none diagnoses based on groups of symptoms. Instead, we wanted to encourage researchers to understand how basic core functions of the brain—like fear processing and reward processing—work at a biological and behavioral level and how these core functions contribute to mental disorders,” said Dr. Cuthbert.

This approach would incorporate biological and behavioral measures of mental disorders and examine processes that cut across and apply to all mental disorders. From Dr. Cuthbert’s standpoint, this could help remedy some of the frustrations mental health researchers were experiencing.

Around the same time the workgroup was sharing its plans and organizing the first steps, Sarah Morris, Ph.D., was a researcher focusing on schizophrenia at the University of Maryland School of Medicine in Baltimore. When she first read these papers, she wondered what this new approach would mean for her research, her grants, and her lab.

She also remembered feeling that this new approach reflected what she was seeing in her data.

“When I grouped my participants by those with and without schizophrenia, there was a lot of overlap, and there was a lot of variability across the board, and so it felt like RDoC provided the pathway forward to dissect that and sort it out,” said Dr. Morris.

Later that year, Dr. Morris joined NIMH and the RDoC workgroup, saying, “I was bumping up against a wall every day in my own work and in the data in front of me. And the idea that someone would give the field permission to try something new—that was super exciting.”

The five original RDoC domains of functioning were introduced to the broader scientific community in a series of articles published in 2010  .

To establish the new framework, the RDoC workgroup (including Drs. Cuthbert and Morris) began a series of workshops in 2011 to collect feedback from experts in various areas from the larger scientific community. Five workshops were held over the next two years, each with a different broad domain of functioning based upon prior basic behavioral neuroscience. The five domains were called:

• Negative valence (which included processes related to things like fear, threat, and loss)
• Positive valence (which included processes related to working for rewards and appreciating rewards)
• Cognitive processes
• Social processes
• Arousal and regulation processes (including arousal systems for the body and sleep).

At each workshop, experts defined several specific functions, termed constructs, that fell within the domain of interest. For instance, constructs in the cognitive processes domain included attention, memory, cognitive control, and others.

The result of these feedback sessions was a framework that described mental disorders as the interaction between different functional processes—processes that could occur on a continuum from normal to abnormal. Researchers could measure these functional processes in a variety of complementary ways—for example, by looking at genes associated with these processes, the brain circuits that implement these processes, tests or observations of behaviors that represent these functional processes, and what patients report about their concerns. Also included in the framework was an understanding that functional processes associated with mental disorders are impacted and altered by the environment and a person’s developmental stage.

Preserving momentum

Over time, the Framework continued evolving and adapting to the changing science. In 2018, a sixth functional area called sensorimotor processes was added to the Framework, and in 2019, a workshop was held to better incorporate developmental and environmental processes into the framework.;

Since its creation, the use of RDoC principles in mental health research has spread across the U.S. and the rest of the world. For example, the Psychiatric Ratings using Intermediate Stratified Markers project (PRISM)   , which receives funding from the European Union’s Innovative Medicines Initiative, is seeking to link biological markers of social withdrawal with clinical diagnoses using RDoC-style principles. Similarly, the Roadmap for Mental Health Research in Europe (ROAMER)   project by the European Commission sought to integrate mental health research across Europe using principles similar to those in the RDoC Framework.;

Dr. Morris, who has acceded to the Head of the RDoC Unit, commented: “The fact that investigators and science funders outside the United States are also pursuing similar approaches gives me confidence that we’ve been on the right pathway. I just think that this has got to be how nature works and that we are in better alignment with the basic fundamental processes that are of interest to understanding mental disorders.”

The RDoC framework will continue to adapt and change with emerging science to remain relevant as a resource for researchers now and in the future. For instance, NIMH continues to work toward the development and optimization of tools to assess RDoC constructs and supports data-driven efforts to measure function within and across domains.

“For the millions of people impacted by mental disorders, research means hope. The RDoC framework helps us study mental disorders in a different way and has already driven considerable change in the field over the past decade,” said Joshua A. Gordon, M.D., Ph.D., director of NIMH. “We hope this and other innovative approaches will continue to accelerate research progress, paving the way for prevention, recovery, and cure.”

Publications

Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine , 11 , 126. https://doi.org/10.1186/1741-7015-11-126  

Cuthbert B. N. (2014). Translating intermediate phenotypes to psychopathology: The NIMH Research Domain Criteria. Psychophysiology , 51 (12), 1205–1206. https://doi.org/10.1111/psyp.12342  

Cuthbert, B., & Insel, T. (2010). The data of diagnosis: New approaches to psychiatric classification. Psychiatry , 73 (4), 311–314. https://doi.org/10.1521/psyc.2010.73.4.311  

Cuthbert, B. N., & Kozak, M. J. (2013). Constructing constructs for psychopathology: The NIMH research domain criteria. Journal of Abnormal Psychology , 122 (3), 928–937. https://doi.org/10.1037/a0034028  

Garvey, M. A., & Cuthbert, B. N. (2017). Developing a motor systems domain for the NIMH RDoC program.  Schizophrenia Bulletin , 43 (5), 935–936. https://doi.org/10.1093/schbul/sbx095  

Insel, T. (2013). Transforming diagnosis . http://www.nimh.nih.gov/about/director/2013/transforming-diagnosis.shtml

Kozak, M. J., & Cuthbert, B. N. (2016). The NIMH Research Domain Criteria initiative: Background, issues, and pragmatics. Psychophysiology , 53 (3), 286–297. https://doi.org/10.1111/psyp.12518  

Morris, S. E., & Cuthbert, B. N. (2012). Research Domain Criteria: Cognitive systems, neural circuits, and dimensions of behavior. Dialogues in Clinical Neuroscience , 14 (1), 29–37. https://doi.org/10.31887/DCNS.2012.14.1/smorris  

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• Presidential Proclamation 6158 (The Decade of the Brain) 
• Research Domain Criteria Initiative website
• Psychiatric Ratings using Intermediate Stratified Markers (PRISM)  
• Roadmap for Mental Health Research in Europe (ROAMER)