# Computational Mechanisms of Effort-Cost Decision-Making in Schizophrenia

> **NIH NIH K23** · UNIVERSITY OF MARYLAND BALTIMORE · 2021 · $165,005

## Abstract

Project Summary:
 Many people with schizophrenia experience reductions in motivation, which impair occupational
functioning, reduce quality of life, and increase public health demands. Treatments for motivational
impairments in SZ are largely ineffective, however, in part due to poor understanding of etiology. Recent work
has provided strong evidence that abnormal effort-cost decision-making – calculations performed to weigh the
“cost vs. benefits” of actions – may be a key contributor to motivational deficits in schizophrenia. Specifically,
research has shown that people with schizophrenia are less willing than controls to expend effort to obtain
rewards on experimental tasks, and that this deficit is related to motivational impairment. However, due to the
use of imprecise experimental paradigms and analytic methods that are ill-suited to disentangle the
contribution of component processes to effort-cost decision-making, it is unknown whether this reduction is
driven by reduced sensitivity to the rewards or heightened sensitivity to the effort associated with actions. This
knowledge has treatment implications, as interventions for targeting reward and effort sensitivity are different.
 We will use a combination of experimental tasks and associated computational modeling approaches,
to quantify the relative contributions of effort and reward sensitivity to effort-cost decision-making in people with
schizophrenia and healthy controls. We will also collect mobile-based assessments providing comprehensive
phenotyping of motivational impairment experienced in daily life. We aim (a) to determine whether effort-cost
decision-making deficits in schizophrenia reflects increased effort or reduced reward sensitivity, (b) to identify
the neural substrates of effort-cost decision-making impairment, and (c) to establish whether effort measures
correspond to measures of effort and reward in daily life.
 The fact that few researchers have been trained in both clinical phenomenology and computational
modeling techniques limits the translational impact these approaches may have in understanding mental
illness. With this in mind, the training plan is specifically-designed to provide hands-on instruction in 1) applying
computational models to effort-cost decision-making to choice behavior, 2) integrating computational modeling
with functional neuroimaging, and 3) relating computational modeling parameters to mobile-based
assessments of daily motivational experience. Taken together, completion of the proposal will facilitate the
applicant’s long-term goal of becoming an independent investigator examining the computational mechanisms
of motivational impairment in various psychiatric conditions. Further, the data and skills acquired will position
the applicant to competitively submit a transdiagnostic R01 proposal, designed to examine whether effort-cost
decision-making impairments in psychiatric conditions characterized by avolition (e.g., major depressive
disorder, ...

## Key facts

- **NIH application ID:** 10275979
- **Project number:** 1K23MH126986-01
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Adam J. Culbreth
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $165,005
- **Award type:** 1
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10275979

## Citation

> US National Institutes of Health, RePORTER application 10275979, Computational Mechanisms of Effort-Cost Decision-Making in Schizophrenia (1K23MH126986-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10275979. Licensed CC0.

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