- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT07900893
Cognitive Neurocomputational Task Reliability and Clinical Applications Consortium: Study 3 (CNTRACS)
Cognitive Neurocomputational Task Reliability and Clinical Applications Consortium
In Study 3, all tasks and models from Aim 1 will be evaluated in a large community sample of online participants recruited through Prolific.
Aim 3 will establish 1) requirements for testing computational algorithms in population-based samples, 2) whether task length is optimal, and 3) whether computational parameters help predict psychopathology (hallucinations and delusions in particular) in the general population, and how these predictors may be impacted by demographic variables, such as race, ethnicity, gender, age and other social determinants of health.
Participant demographics will be matched to the U.S. Census 2023 American Community Survey (ACS) data release (updated as available). MPI MacDonald has successfully recruited large online samples for over a decade.
Specifically, researchers will recruit two samples of n=2,000 to preregister hypotheses and then n=3,000 to independently confirm those hypotheses. Multiple methods will affirm the highest quality human self-report and behavioral data for online samples.
Researchers use Prolific, which is known for high quality research participation, maintaining low levels of bots through strategies such as requiring a photo ID and a waiting period. Second, inclusion criteria will require an approval rating of 90-100% from previous researchers. Participants will complete three sessions, each limited to ~60 min to avoid burnout.
Study Overview
Status
Detailed Description
Researchers will recruit two samples of 2,000 to preregister hypotheses and then 3,000 to independently confirm those hypotheses.
Multiple methods will affirm the highest quality human self-report and behavioral data for online samples. First, researchers will use Prolific, which is known for high quality research participation, maintaining low levels of bots through strategies such as requiring a photo ID and a waiting period. Participants will complete 3 sessions, each limited to ~60 min to avoid burnout. Researchers anticipate complete data from 74% of participants, based on data completion rates in the previous online study.
General Cognitive Ability. Researchers will include standard general cognitive measures to assess the specificity of effects beyond general cognitive impairment. Specifically, researchers will include validated online measures from TestMyBrain (https://www.testmybrain.org/using-tmb/research.html; Digit Symbol Coding, Vocabulary, and Matrix Reasoning), as in our prior work in both clinical and healthy populations.
Self-Report Questionnaires: Researchers will use the Beliefs about Voices Scale which assesses attributions about the origin and power of voices, and the Yale Control Over Perceptual Experiences (COPE) scale, which evaluates the extent to which people feel they can exert control over their voices. Researchers will also use the Peters Delusion Inventory to assess the extent of delusional thinking, supplemented by the Green Paranoia Scale, which provides additional coverage of persecutory beliefs. To assess specificity, researchers will use the Patient Health Questionnaire (PHQ-9) to assess depression, the Generalized Anxiety Disorder Scale (GAD-7)78 to assess anxiety, and the Motivation and Pleasure Scale-Self-Report (MAP-SR) to assess anhedonia and motivation with the addition of additional measures of psychosis (Aberrant Salience Inventory, Schizotypal Personality Questionnaire - Brief), alcohol use (AUDIT), cannabis use (Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory), and tobacco and other substance use (World Health Organization (WHO) Alcohol, Smoking and Substance Involvement Screening Test), as researchers will not have a Stuctured Clinical Interview (SCID) for on-line participants.
Researchers will also measure daily functioning using the World Health Organization Disability Assessment Schedule (WHODAS) 2.0. Validity questions and procedures will be included. These measures will take 30 mins to complete, as researchers found in prior studies. Researchers will conduct a principal components analysis (PCA) on measures of the same construct (e.g., delusions) and perform primary analyses using the first principal component, followed by secondary analyses of specific measures False Discovery Rate (FDR) correction.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: Deanna Barch, PhD
- Phone Number: 314-935-8729
- Email: dbarch@wustl.edu
Study Locations
-
-
Minnesota
-
Minneapolis, Minnesota, United States, 55455
- Recruiting
- University of Minnesota
-
Contact:
- Angus MacDonald III, PhD
- Phone Number: 612-624-3813
- Email: angus@umn.edu
-
Contact:
- UMN Study Team
- Phone Number: 651-558-1275
- Email: CNTRACS@umn.edu
-
-
Missouri
-
St Louis, Missouri, United States, 63130
- Recruiting
- Washington University
-
Contact:
- Deanna Barch, PhD
- Phone Number: 314-935-8729
- Email: dbarch@wustl.edu
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Residents of the U.S.
- between 18-59 years of age
Exclusion Criteria:
- Resides outside of U.S.
- under 18 or over 59 years of age
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Basic Science
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Sine Wave Speech Task Performance
Time Frame: 1 hour
|
Percentage of trials coded as speech or not vs Dprime |
1 hour
|
|
Minnesota Trust Game Performance
Time Frame: 1 hour
|
Percent of trusting choices depending on decision-agent and temptation vs. spite-guilt beliefs estimate |
1 hour
|
|
Dynamic Trust Game Performance
Time Frame: 1 hour
|
Trust indexed by mean investment versus learning rates |
1 hour
|
|
Motion Perception Task Performance
Time Frame: 1 hour
|
With 15 degrees of initial or changed direction vs. the probability of failing to update |
1 hour
|
|
Volatility Task Performance
Time Frame: 1 hour
|
Percent correct bag captures during periods of stability vs. instability - patients worse in both, but particularly impaired ininstability vs. Environmental volatility belief parameter |
1 hour
|
|
Choice Probabilistic Reversal Performance Task
Time Frame: 1 hour
|
Win-switch rate (higher in patients, particularly paranoid patients) vs. (prior on volatility belief updating in response tovolatility |
1 hour
|
|
Kamin Blocking Task Performance
Time Frame: 1 hour
|
Number of times participant predict allergy following blocked cue vs. prediction errors and learning rates |
1 hour
|
Collaborators and Investigators
Collaborators
Investigators
- Principal Investigator: Deanna Barch, PhD, Washington University School of Medicine
Publications and helpful links
General Publications
- Li C, Boudewyn MA, Barch DM, Carter CS, Frank MJ, Gold JM, MacDonald AW 3rd, Ragland JD, Silverstein SM, Yonelinas A, Luck SJ, Erickson MA. Impacted and preserved sub-domains of cognitive control in schizophrenia. Neuropsychologia. 2026 Sep 9;230:109503. doi: 10.1016/j.neuropsychologia.2026.109503. Epub 2026 May 25.
- MacDonald AW, Patzelt E, Kurth-Nelson Z, Barch DM, Carter CS, Gold JM, Ragland JD, Silverstein SM. Computational modeling of reversal learning impairments in schizophrenia and bipolar disorder reveals shared failure to exploit rewards. J Psychopathol Clin Sci. 2025 Apr;134(3):262-271. doi: 10.1037/abn0000944. Epub 2025 Mar 10.
- Erickson MA, Boudewyn MA, Winsler K, Li C, Barch DM, Carter CS, Frank MJ, Gold JM, MacDonald AW 3rd, Ragland JD, Silverstein SM, Yonelinas A, Luck SJ. Dysfunctional Alpha Modulation as a Mechanism of Working Memory Impairment in Serious Mental Illness. Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Dec;9(12):1271-1280. doi: 10.1016/j.bpsc.2024.07.022. Epub 2024 Aug 6.
- Boudewyn MA, Erickson MA, Winsler K, Ragland JD, Yonelinas A, Frank M, Silverstein SM, Gold J, MacDonald AW 3rd, Carter CS, Barch DM, Luck SJ. Managing EEG studies: How to prepare and what to do once data collection has begun. Psychophysiology. 2023 Nov;60(11):e14365. doi: 10.1111/psyp.14365. Epub 2023 Jun 14.
- Kwashie AN, Ma Y, Barch DM, Chafee M, Ragland JD, Silverstein SM, Carter CS, Gold JM, MacDonald AW 3rd. Comparing the functional neuroanatomy of proactive and reactive control between patients with schizophrenia and healthy controls. Cogn Affect Behav Neurosci. 2023 Feb;23(1):203-215. doi: 10.3758/s13415-022-01036-6. Epub 2022 Nov 23.
- Liu RG, Frank MJ. Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning. Artif Intell. 2022 Nov;312:103770. doi: 10.1016/j.artint.2022.103770. Epub 2022 Aug 5.
- Pratt DN, Barch DM, Carter CS, Gold JM, Ragland JD, Silverstein SM, MacDonald AW. Reliability and Replicability of Implicit and Explicit Reinforcement Learning Paradigms in People With Psychotic Disorders. Schizophr Bull. 2021 Apr 29;47(3):731-739. doi: 10.1093/schbul/sbaa165.
- Hamid AA, Frank MJ, Moore CI. Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. Cell. 2021 May 13;184(10):2733-2749.e16. doi: 10.1016/j.cell.2021.03.046. Epub 2021 Apr 15.
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 201904012: Study 3
- 2R01MH084840 (U.S. NIH Grant/Contract)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Our plan is to share de-identified participant data through the National Data Archive (NDA) as required bythe National Institute of Mental Health (NIMH)
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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