Evidence map›Paper›PMID 41691110›Full record

ArticleMolecular psychiatry2026

Computational phenotypes underlying effort-based decision-making and negative symptoms in a transdiagnostic severe mental illness sample.

Lauren Luther, Jessica A Cooper, Michael T Treadway, Anna R Knippenberg, Elaine F Walker, James M Gold, James A Waltz, Jason Schiffman, Lauren M Ellman, Vijay A Mittal and 8 more

Abstract read
In one paragraph

Article in Molecular psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Lauren LutherDepartment of Psychology, University of Georgia, Athens, GA, USA.
Jessica A CooperDepartment of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.
Michael T TreadwayDepartment of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.ORCID 0000-0002-5913-114X
Anna R KnippenbergDepartment of Psychology, University of Georgia, Athens, GA, USA.
Elaine F WalkerDepartment of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.ORCID 0000-0002-9798-8101
James M GoldDepartment of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
James A WaltzDepartment of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-9236-3719
Jason SchiffmanDepartment of Psychological Science, University of California, Irvine, CA, USA.
Lauren M EllmanDepartment of Psychology & Neuroscience, Temple University, Philadelphia, PA, USA.
Vijay A MittalDepartment of Psychology, Northwestern University, Evanston, IL, USA.ORCID 0000-0001-9017-5119
Richard E ZinbargDepartment of Psychology, Northwestern University, Evanston, IL, USA.
Steve M SilversteinDepartments of Psychiatry, Neuroscience and Ophthalmology, University of Rochester Medical Center, Rochester, NY, USA.
Philip R CorlettDepartment of Psychiatry, Yale University, New Haven, CT, USA.ORCID 0000-0002-5368-1992
Albert R PowersDepartment of Psychiatry, Yale University, New Haven, CT, USA.ORCID 0000-0003-3057-5043
Scott W WoodsDepartment of Psychiatry, Yale University, New Haven, CT, USA.ORCID 0000-0002-3103-5228
Daniel N AllenDepartment of Psychology, University of Las Vegas Nevada, Las Vegas, NV, USA.
Adrienne C LahtiDepartment of Psychiatry, University of Alabama at Birmingham, Birmingham, AL, USA.ORCID 0000-0002-5565-7662
Gregory P StraussDepartment of Psychology, University of Georgia, Athens, GA, USA. gstrauss@uga.edu.

Funding

2/9 - Predictors and Mechanisms of Conversion to PsychosisU01MH081988 · NIMH · EMORY UNIVERSITY · PI WALKER, ELAINE FRAN · 2008 to 2018
$5.4M
5/5 CAPER: Computerized Assessment of Psychosis RiskR01MH120089 · NIMH · YALE UNIVERSITY · PI CORLETT, PHILIP, WOODS, SCOTT W · 2020 to 2024
$3.1M
Prodromal Inventory for Negative Symptoms (PINS): A Development and Validation StudyR01MH116039 · NIMH · UNIVERSITY OF GEORGIA · PI MITTAL, VIJAY A, STRAUSS, GREGORY P · 2019 to 2023
$3.1M
1/5 CAPER: Computerized Assessment of ProdromE RiskR01MH120090 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI GOLD, JAMES M. · 2020 to 2024
$2.9M
4/5 CAPER: Computerized assessment of psychosis riskR01MH120092 · NIMH · UNIVERSITY OF GEORGIA · PI STRAUSS, GREGORY P · 2020 to 2024
$2.0M
2/5 CAPER Computerized assessment of psychosis riskR01MH120088 · NIMH · NORTHWESTERN UNIVERSITY · PI MITTAL, VIJAY A · 2020 to 2024
$2.0M
1/3-Community Psychosis Risk Screening: An Instrument Development Study SupplementR01MH112613 · NIMH · TEMPLE UNIV OF THE COMMONWEALTH · PI ELLMAN, LAUREN M · 2017 to 2022
$1.8M
CAPER: Computerized Assessment of Psychosis Risk SupplementR01MH120091 · NIMH · TEMPLE UNIV OF THE COMMONWEALTH · PI ELLMAN, LAUREN M · 2020 to 2024
$1.6M
Administrative Supplement - 3/3: Community Psychology Risk Screening: An Instrument Development StudyR01MH112612 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE COUNTY · PI PITTS, STEVEN C, SCHIFFMAN, JASON E · 2017 to 2022
$1.3M
2/3 Community psychosis risk screening: An instrument development studyR01MH112545 · NIMH · NORTHWESTERN UNIVERSITY · PI MITTAL, VIJAY A · 2017 to 2021
$1.2M
From stress to anhedonia: examining inflammation and altered glutamate function as distinct or common etiological pathwaysK01MH126308 · NIMH · EMORY UNIVERSITY · PI Jessica Cooper Robinson · 2022 to 2026
$900k
Modeling Anhedonia in Schizophrenia: A Stochastic Dynamical Systems ApproachR21MH112925 · NIMH · UNIVERSITY OF GEORGIA · PI STRAUSS, GREGORY P · 2017 to 2018
$417k
NIMH NIH HHS R01 MH112545NIMH NIH HHS R01 MH112612NIMH NIH HHS R01 MH112613NIMH NIH HHS R01 MH116039NIMH NIH HHS R01 MH120088NIMH NIH HHS R01 MH120089NIMH NIH HHS R01 MH120090NIMH NIH HHS R01 MH120091NIMH NIH HHS R01 MH120092NIMH NIH HHS R21 MH112925NIMH NIH HHS R21 MH119438NIMH NIH HHS U01 MH081988U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) K01MH126308U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH1120088U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH112545U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH112612U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH112613U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH112925U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH116039U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH119438U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH120089U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH120090U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH120091U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) MH120092
6 · The paper itself

Abstract

Effort-based decision-making (EBDM) impairments predict negative symptoms across multiple psychiatric diagnoses. However, it is unclear whether equifinality is present and different disorders reach the same clinical endpoint of negative symptoms via different mechanistic EBDM processes. This study used computational modeling to isolate processes underlying EBDM in a large severe mental illness-spectrum sample. The Effort Expenditure for Rewards Task, negative symptom measures, and neuropsychological tests were administered to 920 participants: schizophrenia (SZ; n = 147), first-episode psychosis (FEP; n = 54), bipolar disorder (n = 53), depressive disorder (n = 37), clinical high-risk for psychosis (CHR; n = 231), other clinical (n = 99), and healthy control groups (HC; n = 299). Computational modeling identified whether participants' EBDM behavior was best fit by models indexing full or partial subjective value (use reward magnitude and/or probability) or bias (failure to use reward magnitude and probability). Best fitting models significantly differed across diagnostic groups. SZ and FEP were best fit by the bias model and less likely to use reward magnitude and probability to guide EBDM. The CHR, other clinical, depressive disorder, and HC groups were best fit by the full subjective value model and were more likely to use reward magnitude and probability, while the bipolar disorder group's behavior was more variable. Across groups, participants best fit by the bias model had the greatest negative symptoms and cognitive impairments. Results indicate mood and psychosis-spectrum disorders differentially approach EBDM. Equifinality in the pathway to negative symptoms was not supported; those with difficulty utilizing reward and probability information had the greatest negative symptoms, independent of diagnosis.

Indexed as

Decision MakingMental DisordersAdultBipolar DisorderComputer SimulationDepressive DisorderFemaleHumansMaleNeuropsychological TestsPhenotypePsychotic DisordersRewardSchizophreniaSchizophrenic PsychologyYoung Adult

Identifiers

PMID41691110
PMCPMC13285297

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