# Neural computations of learning, decision-making and memory

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $673,131

## Abstract

Most of our behavior consists of learning about the environment, choosing between options that we learned
about, and storing the learning in memory for future use. These processes rely on a set of basic computations,
including estimations of value and uncertainty and calculations of prediction errors. Prior research has
identified neural correlates of these computations, and linked aberrations in these computations to maladaptive
behavior and psychopathology. This research, however, typically focused on one process (e.g. learning,
decision-making or memory), within one domain (e.g. rewards or punishments). Our goal in this application is to
characterize core computations of value, uncertainty and prediction error, across processes and domains, within
the individual, and to identify associations between these computations and symptoms of anxiety and stress-
related disorders. We plan to do this in a behavioral study with 1000 participants (Aim 1), and a functional MRI
study of 100 participants (Aim 2), including both men and women from the general population. We will employ a
well-validated decision-making paradigm, together with a novel naturalistic game-like experimental paradigm,
which combines a passive-learning stage, a decision-making stage, and a memory stage administered on a
second day. Our design will include explicit measures, and latent variables which will be derived from
computational modeling. In Aim 1, we will characterize the computations behaviorally, by constructing a
distribution for each measure, based on the whole sample, and characterizing each subject based on their
location in this distribution. This will allow us to estimate similarities and differences between computations of
the same measure (e.g. value) across processes (learning, decision-making and memory) and domains (rewards
and punishments). In Aim 2 we will characterize the computations neurally, by examining activation and
connectivity patterns. We will estimate to what extent these patterns for each measure (e.g. value) in one process
(e.g. learning) predict the patterns for the same measure in other processes (e.g. decision-making and memory)
and to what extent representations in the reward domain predict those in the punishment domain. Aim 3 will
classify participants based on the behavioral and neural characteristics, and identify associations between these
classifications and clinical symptoms of anxiety and stress-related disorders. We hypothesize that different
psychopathology symptoms are associated with the type and magnitude of alterations in basic computations.
Together, the proposed studies will inform our basic understanding of the computations of value, uncertainty and
prediction error, and will unravel links between these computations and clinical symptoms, in a dimensional
manner, consistent with the RDoC approach. In the long term, we expect the methodology developed as part of
the proposed studies to be useful in characterizing neurobehavioral ...

## Key facts

- **NIH application ID:** 10881432
- **Project number:** 1R01MH133886-01A1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Ifat Levy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $673,131
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10881432

## Citation

> US National Institutes of Health, RePORTER application 10881432, Neural computations of learning, decision-making and memory (1R01MH133886-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10881432. Licensed CC0.

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