# Eye Movements and the Dynamics of Adaptive Learning

> **NIH NIH F32** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2021 · $70,458

## Abstract

PROJECT SUMMARY/ABSTRACT
This project examines the mechanisms of construction and updating of subjective belief distributions. Human
decision-makers must often update their beliefs in response to new observations. The degree to which beliefs
should be updated is highly dependent on context: In stable but noisy environments, new observations should
be given limited weight, while volatile environments require more rapid updating. This work focuses on
determining how decision makers (and their brains) represent the precision (or width) of subjective belief
distributions, which has not yet been explicitly demonstrated. By using eye tracking and neuroimaging
approaches, we can obtain a continuous, implicit and multifaceted set of predictions in a spatial prediction task
that samples more densely from individuals' underlying distributions of beliefs. This will allow us to investigate
whether and how entire distributions of beliefs are represented, including both their central tendency as well as
their width. Additionally, little is known about how sensory uncertainty interacts with central arousal and higher-
order cognitive processes to support adaptive learning. Combining the rich behavioral data stream of eye
tracking with fMRI will provide new tools for understanding these interactions and their neural mechanisms.
The first aim of the project is to explore the influence of amount of uncertainty and arousal in a free-viewing
implicit spatial prediction task. Eye tracking will allow us to monitor the evolution of individuals' predictive
distributions on a trial-by-trial level, and investigate the effects of reward and punishment on learning rate and
saccade dynamics. Additionally, pupillometry will allow us to probe the mechanisms of belief updating in a
fixational spatial prediction task. We will assess whether reward and punishment enhance learning rate
through common arousal/incentive mechanisms. This will begin to help us disentangle whether changes in
arousal level are part of the mechanism of dynamic learning rate adaptation. For the second aim, functional
magnetic resonance imaging will allow us to examine the effects of differing types of uncertainty (noise and
volatility), while holding the amount of uncertainty constant. We will first investigate whether the BOLD
response in frontal regions (i.e. anterior prefrontal cortex, anterior cingulate cortex, and anterior insula) is
differentially dependent on prediction error for the condition dominated by volatility as opposed to noise. We
will next test how these factors change the functional connectivity between frontal brain regions and visual
cortices. Ultimately, this research will help us understand how belief distributions are represented and updated,
and how the brain dynamically adapts this process in a changing environment.

## Key facts

- **NIH application ID:** 10186754
- **Project number:** 5F32EY029134-03
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Leah Bakst
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $70,458
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186754

## Citation

> US National Institutes of Health, RePORTER application 10186754, Eye Movements and the Dynamics of Adaptive Learning (5F32EY029134-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10186754. Licensed CC0.

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