# Parametrically Detailed Computational Analyses of Human Foraging Behavior

> **NIH NIH R21** · UNIVERSITY OF MINNESOTA · 2020 · $192,500

## Abstract

The overall goal of this project is the development and validation of parametric mathematical assays of human
decision-making, based on an online information-foraging task (WebSurf). Decisional impairments in general
are common in mental illnesses, but the exact pattern of deficits varies within and between diagnostic
categories. Those deficits often involve multiple decision-making systems and the interactions between those
systems. For instance, patients with obsessive-compulsive disorders rely overly on habitual/procedural action
(leading to ritualizing behavior), but also show impairment in change-of-mind systems (inability to interrupt
rituals) and deliberation (“analysis paralysis” in the face of uncertainty and a chance of negative outcomes). A
major challenge in computational psychiatry is the need for tasks/paradigms that measure these multi-
system dysfunctions, including interactions between systems. A further need is tasks that are viable for
clinical settings, i.e. that are valid for repeated-measures use, sensitive to clinical-level impairment, and
usable without highly trained experimenters present. We propose to address these needs with WebSurf, an
information-foraging task developed by co-PIs MacDonald and Redish. These investigators and their
colleagues have used WebSurf (and its rodent version, Restaurant Row) to demonstrate a common “sunk
costs” fallacy across rodents and humans, to identify the neural basis of regret, and to quantify differences in
rule-based decision making in patients with eating disorders. Those studies have demonstrated WebSurf’s
general utility as a cross-species paradigm and shown the richness of parametric descriptions that can be
extracted from task behavior. They have also identified difficulties with the base version of the task,
including needs for greater subject engagement and higher trial counts. As importantly, although Restaurant
Row appears to elicit stable day-to-day behavior in mice, we do not yet know if the same is true for humans.
We will close these gaps in task validation by assessing the performance of multiple variants using
Amazon’s Mechanical Turk platform. Data from those variants, as well as ongoing data collection with the
baseline task in our psychiatric clinics, will validate newer and more robust approaches to decision
parameter estimation (Aim 1), grounded in Bayesian hierarchical modeling. They will demonstrate repeated-
measures stability (Aim 2) and ability to describe variation between and within clinical populations (Aim
3). Executing these Aims will build on WebSurf’s success as a (reverse) translatable experimental paradigm,
demonstrating a tool for clinical computational psychiatry. Our team’s broad experience includes computational
science, experimental psychology and neuroscience, and clinical psychiatry, making us well-suited both to
perform the Aims and apply the results in future psychiatric neuroscience studies.

## Key facts

- **NIH application ID:** 9989188
- **Project number:** 5R21MH120785-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** ANGUS W MACDONALD
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $192,500
- **Award type:** 5
- **Project period:** 2019-08-15 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989188

## Citation

> US National Institutes of Health, RePORTER application 9989188, Parametrically Detailed Computational Analyses of Human Foraging Behavior (5R21MH120785-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9989188. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*