# CRCNS: Decision Making in Changing Environments

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $376,298

## Abstract

Perceptual, reward-based, and other decisions are deliberative processes that depend on the ability to
accumulate uncertain information over time. In dynamic environments, this process must be adaptive to
account effectively for changes in the relevance and reliability of new inputs. For example, environmental
changes can occur mid-decision. Such changes can render previous inputs obsolete and thus require
adjustment of the accumulation process. Recent work has begun to examine decision-making under these
kinds of dynamic conditions, resulting in a growing understanding of computational, behavioral, and
physiological properties of adaptive evidence accumulation. However, a critical gap remains in our
understanding of the underlying neural mechanisms: no study to date has identified representations of this
kind of adaptive decision variable that flexibly accumulate information to drive behavior. Our goal is to fill this
gap using highly interacting theoretical and experimental approaches to understand how and where in the
brain such decision variables are encoded. Specifically, we will test the hypothesis that brain circuits that
encode near-perfect integration of evidence under static conditions are highly flexible and implement more
adaptive processes that approximate key features of ideal-observer models under dynamic conditions. We
 propose three Specific Aims, as follows. Aim 1 is to develop computational models of neural circuits that can
approximate normative evidence accumulation in dynamic environments. Aim 2 is to determine principles of
adaptive evidence accumulation used by human subjects performing dynamic decision tasks. Aim 3 is to
identify-representations-of-adaptive-evidence-accumulation-in-parietaJ-and-prefrontaJ- ne1.Jra1-acti11ity-of
monkeys performing dynamic decision tasks. Together, these integrated computational, behavioral, and
neurophysiological approaches will provide novel insights into the many aspects of higher brain function and
complex behaviors that depend on processing information in a manner that is not tied reflexively to
immediate sensory inputs or motor outputs. We also have a strong data-sharing strategy that will help
ensure that this unique data set will be made available for research and teaching purposes.
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RELEVANCE (See instructions):
The proposed work is basic research designed to identify mechanisms of flexible decision-making. In the
long run, this work will help inform new diagnoses and treatments of disorders that include deficits in
cognition and decision-making, including schizophrenia, autism, and attention-deficit/hyperactivity disorder
(ADHD).

## Key facts

- **NIH application ID:** 9920777
- **Project number:** 5R01MH115557-04
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** JOSHUA I GOLD
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $376,298
- **Award type:** 5
- **Project period:** 2017-07-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9920777

## Citation

> US National Institutes of Health, RePORTER application 9920777, CRCNS: Decision Making in Changing Environments (5R01MH115557-04). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/9920777. Licensed CC0.

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