# Computationally modeling the failure of effort to become a secondary reinforcer in schizophrenia

> **NIH NIH R21** · UNIVERSITY OF GEORGIA · 2020 · $441,455

## Abstract

PROJECT SUMMARY
In the United States, psychotic disorders (PDs) are the leading medical cause of functional disability, resulting
in extremely high rates of unemployment and high financial costs to the public healthcare system. Due to the
role that negative symptoms play in functional disability in PDs, it is critical that progress be made in
understanding mechanisms leading to negative symptoms. Current theoretical models of negative symptoms
postulate that dysfunctional cortico-striatal interactions lead to impairments in several aspects of reward
processing that prevent PD patients from utilizing decision-making processes needed to perform goal directed
behavior. Paramount among these reward processing abnormalities is effort-cost computation. Impaired
effort-cost computation has been repeatedly demonstrated in PDs and linked to greater negative symptom
severity; however, the processes underlying this association and its ties to real-world functioning are unclear.
In the current study, we aim to test the novel hypothesis that negative symptoms and poor functional outcome
are associated with the failure of effort to become a secondary reinforcer. There is evidence from studies on
animals and humans indicating that if high effort is consistently paired with high reward, this can form a
conditioned association, whereby effort itself takes on the status of a secondary reinforcer (i.e., effort itself is
learned to have value, even if it is not rewarded). Building upon translational neuroscience paradigms
developed in rodents, we will administer a novel effort as a secondary reinforcer task to outpatients with PDs (n
= 45) and healthy controls (n = 45). The task consists of four phases: 1) difficulty scaling: participants will
perform 4 cognitive tasks (anagrams, Gabor patches, mental arithmetic, trail making) and a staircase
procedure will be used to determine individualized difficulty levels set for low and high effort options utilized
in subsequent phases ; 2) baseline: a two-forced choice effort decision-making task will be performed in
relation to four cognitive tasks to determine baseline levels of effort avoidance; 3) effort training: in a two-
forced choice decision making task, selection of a high effort option will be probabilistically reinforced over
several training blocks performed for 3 tasks; 4) test phase: near and far transfer effects of training will be
examined for the 3 tasks participants were reinforced to select the high effort option on (near transfer), as well
as a novel task they were not trained on (far transfer). In addition to measuring decision-making behavior,
pupil dilation will be recorded continuously throughout the four phases as an objective marker of cognitive
effort. A novel computational model will be applied to evaluate mechanisms involved with the failure of effort
to become a secondary reinforcer, which evaluates reward sensitivity, learning rate, and effort cost. We will
explore the novel hypothesis that avo...

## Key facts

- **NIH application ID:** 9956514
- **Project number:** 1R21MH122863-01
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** AMITAI SHENHAV
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $441,455
- **Award type:** 1
- **Project period:** 2020-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9956514

## Citation

> US National Institutes of Health, RePORTER application 9956514, Computationally modeling the failure of effort to become a secondary reinforcer in schizophrenia (1R21MH122863-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/9956514. Licensed CC0.

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