# Modeling Dimensions of Individual Variation in Adaptive Foraging Decisions

> **NIH NIH R21** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2022 · $206,250

## Abstract

PROJECT SUMMARY/ABSTRACT
Persistence toward prospective rewards is a critical element of normative real-world decision making. Equally
important is the ability to disengage from goals that have diminished in value. Deficits in regulating goal-
directed behavior are associated with impulsivity-related traits. The present project will develop and test
computational models to account for individual differences in the context-appropriate calibration of persistence.
We will employ a willingness-to-wait task paradigm in which human decision makers are given repeated
opportunities to persist voluntarily toward delayed monetary rewards in a foraging-like environment. The
distribution of uncertain delay durations in the paradigm can be experimentally manipulated to create an
environment in which either high or low persistence is advantageous. Previous results from the same paradigm
have shown that, on average, decision makers tend to adjust their behavior appropriately for their environment.
However, substantial differences across individuals have been observed in (1) overall levels of behavioral
persistence, (2) the consistency of behavior while the environment remains stable, and (3) flexible adaptation
when the environment changes. We hypothesize that inter-individual heterogeneity can be accounted for in
terms of individual differences in the latent parameters of behavior-generating computational models. We
further hypothesize that individual-specific parameter estimates will be proximally associated with dimensional
trait measures of impulsivity. We will test the hypotheses by implementing two novel theoretical models of
adaptive persistence toward delayed rewards. The first, a "statistical learning" model, hones an internal
representation of reward timing on the basis of experience and produces adaptive persistence decisions using
a planning mechanism. The second, a "motor preparation" model, produces responses in a habit-like manner
at times when responses have been cued in the past. The internal structure of each model will be refined using
task data from 160 community-based healthy volunteers, and parameter estimates will be tested for
associations with trait variables. The models will then be validated and compared using an independent
confirmatory sample (n = 400). The two target models will be compared to one another, to a null model, and to
an existing reinforcement learning model. The results will establish a basis for future back-translational
research in non-human model systems, given that the experimental foraging task is experience-based and
non-linguistic. It will also establish a basis for future studies examining the computational basis of dimensional
constructs relevant to psychopathology in clinical populations.

## Key facts

- **NIH application ID:** 10458065
- **Project number:** 5R21MH124095-02
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** DANIEL C FULFORD
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $206,250
- **Award type:** 5
- **Project period:** 2021-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458065

## Citation

> US National Institutes of Health, RePORTER application 10458065, Modeling Dimensions of Individual Variation in Adaptive Foraging Decisions (5R21MH124095-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10458065. Licensed CC0.

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