# Stochastic Models of Visual Decision Making and Visual Search

> **NIH NIH R01** · VANDERBILT UNIVERSITY · 2022 · $384,363

## Abstract

PROJECT SUMMARY
Support is requested to advance an innovative, productive collaboration aimed at linking mind, brain, and
behavior using performance, neurophysiological, and electrophysiological measures from monkeys and
humans performing visual search and visual decision making tasks. The general goal is to derive the
connections from spike trains in monkeys to behavior in humans using computational models that specify
mental states mathematically, link them to brain states in particular neurons, and explain how the neural
computations produces behavior. Our Gated Accumulator Model (GAM) assumes a stochastic accumulation of
evidence to threshold for alternative responses. Model assessment involves quantitatively testing alternative
model architectures on predictions of behavioral measures, response probabilities and distributions of correct
and error response times, as well as neural measures and how these change with set size and target-distractor
discriminability in previously collected data from monkeys performing visual search. While our previously
funded research aimed to understand the architecture of evidence accumulation in GAM and the relationship of
model accumulators to the observed dynamics of movement-related neurons in FEF, our newly proposed
research aims to understand computationally the nature of the evidence that drives that accumulation and its
relationship to the measured dynamics of visually-responsive neurons in FEF. Aim 1 compares the quality of
salience evidence in lateralized EEG signals and neural discharges from visually-responsive neurons in
monkeys performing visual search as input evidence to a network of stochastic accumulators to predict
behavior. Aim 2 addresses a major challenge to the neural accumulator framework by determining whether
movement neuron dynamics in FEF actually ramp or step. Aim 3 evaluates alternative architectures for an
abstract Visual Attention Model (VAM) of the evidence driving accumulation to jointly predict observed behavior
and the measured dynamics of visually-responsive neurons. Aim 4 extends VAM to more complex visual tasks
involving filtering and selection. The result will be a broader and deeper understanding of the visual processes
that select targets and control eye movements. Computational models like VAM and GAM may be at the “just
right” level of abstraction. They capture essential details of the computation in ways that explain neural activity
and behavior in single participants, whether monkey or human. These models can be used to understand
normal behavior as well as illness, disability, and disease; the best-fitting parameters can characterize
individual differences in behavior and provide markers for brain measures. These models can also inform
neurological conditions that have a biophysical basis at the level of individual neurons and neural circuits,
offering insight into what neurons and circuits compute and how they do it.

## Key facts

- **NIH application ID:** 10480866
- **Project number:** 5R01EY021833-10
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Gordon Dennis Logan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $384,363
- **Award type:** 5
- **Project period:** 2011-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10480866

## Citation

> US National Institutes of Health, RePORTER application 10480866, Stochastic Models of Visual Decision Making and Visual Search (5R01EY021833-10). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10480866. Licensed CC0.

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