# Enhancing evaluation of reward learning using computational modeling methods

> **NIH NIH R21** · TEMPLE UNIV OF THE COMMONWEALTH · 2022 · $228,975

## Abstract

Studies of frequently rely on behavioral tasks as means of understanding etiological processes, correlates, and
consequences of psychopathology. In the context of reward learning and decision making, the Iowa Gambling
Task (IGT) is frequently used. Ultimately, studies routinely rely on the original implementation of the task that
permits participants to fully direct the exploration and learning in the task. This reduces researchers' ability to
distinguish reward and punishment learning. Moreover, despite multiple processes being involved in IGT
performance, the task is frequently summarized as a single performance metric (i.e., proportion of plays on
advantageous/disadvantageous stimuli). More modern approaches, including computational modeling,
provides a means to distinguish between processes (e.g., reward learning, punishment learning, reward
sensitivity, perseveration tendency) that are of interest to substantive research questions about altered
functioning in psychopathology. A critical limitation of the field is that studies that have used computational
modeling approaches with the IGT have only used the original version of the task. A stronger experimental
paradigm may increase the precision of the estimated parameters. Moreover, computational modeling studies
of the IGT, particularly in developmental samples, rely on cross-sectional designs that precludes examining
test-retest reliability or longitudinal change. This project will estimate computational modeling of the IGT using
an updated version of the task that has full experimental control of the stimulus presentation across all trials. In
independent samples (Study 1 n = 50 undergraduates; Study 2 [R01 MH107495] offspring and parents (n =
248), the IGT was administered on multiple occasions (Study 1 twice, approximately 4 weeks apart; Study 2 up
to five occasions, approximately 9 months apart for offspring and parents). Using data from Study 1, we will
develop reward learning computational models to describe task behavior in the updated version of the IGT.
Using data from Study 2, we will examine the generalizability of the model to independent samples. We will
examine test-retest reliability of performance using the computational modeling approaches, but expand the
number of repeated assessments. We will also examine the validity of model parameters against indices of
reward functioning measures (including self-reports in both offspring and parents; brain function in offspring;
and family history of depression). Finally, we will also adapt the computational model to include indices of
development to model changes in the offspring sample. In addition to the substantive contributions to the
assessment of reward learning and decision making by these task and modeling adaptations, we will also use
our work to aid in the dissemination of these models by updating open source software (the hBayesDM
package) to include our models for use by other investigators.

## Key facts

- **NIH application ID:** 10510360
- **Project number:** 1R21MH130792-01
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** Thomas M Olino
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $228,975
- **Award type:** 1
- **Project period:** 2022-08-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10510360

## Citation

> US National Institutes of Health, RePORTER application 10510360, Enhancing evaluation of reward learning using computational modeling methods (1R21MH130792-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10510360. Licensed CC0.

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