# CRCNS: Circuit mechanisms of priors and learning during decision making

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $387,750

## Abstract

When learning a new task, both rats and humans exhibit suboptimal behaviors plagued with superstitious
ticks and idiosyncratic biases. One prominent example of such suboptimality are sequential effects:
animals tend to bias their choices based on previous decisions and outcomes, hindering performance in
common laboratory tasks using independent trials.
Recurrent neural networks (RNN) have become a common tool to study potential neural mechanisms of
cognition. Yet, RNNs typically behave much closer to optimality in laboratory tasks than real subjects. We
suggest this behavioral difference is rooted in the fundamental discrepancy between how animals and
current RNNs learn: unlike animals before learning, RNNs before training are tabula rasa and their
connectivity is adjusted exclusively to the local contingencies of the task.
We hypothesize that animals’ learning of simple laboratory tasks builds mostly on pre-existing programs,
namely structural prior, that have been shaped by evolution for the species’ fitness in a given ecological
niche. Sequential effects are a manifestation of such pre-wired strategies, which may ultimately support
learning. To test this, we will characterize sequential effects during learning of a set of perceptual tasks
and identify their underlying neural circuitry. We will compare animals’ behavior with RNNs which, after
being equipped with structural priors, can mimic the animal’s ability to learn new tasks.
Objectives
Objective 1. Compare sequential effects in humans and rats with those developed by RNNs.
Objective 2. Characterize the role of the corticostriatal circuit mPFC --> DMS in the tasks and the site of
plasticity necessary for task learning.
Objective 3. Characterize the neural mechanisms underlying the representation of relevant variables in
the brain of the rat and in RNNs.

## Key facts

- **NIH application ID:** 10610167
- **Project number:** 1R01MH132172-01
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Guangyu Yang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $387,750
- **Award type:** 1
- **Project period:** 2022-09-05 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10610167

## Citation

> US National Institutes of Health, RePORTER application 10610167, CRCNS: Circuit mechanisms of priors and learning during decision making (1R01MH132172-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10610167. Licensed CC0.

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