# Acquiring cognitive maps: how brains learn hidden structure

> **NIH NIH K01** · NEW YORK UNIVERSITY · 2024 · $145,174

## Abstract

Project Summary: Animals perform goal-directed behaviors in complex environments, without the need for
extensive experience, by harnessing an internal model of the world. These internal models, or cognitive maps,
are central to model-based decision making. Importantly, understanding the neural circuitry of model-based
decisions holds promise for improving treatments of neuropsychiatric disorders in which decision-making goes
awry. An outstanding question in reinforcement learning (RL) is how cognitive maps are learned from experience,
and what neural substrates support them. The orbitofrontal cortex (OFC) has been implicated in representing
cognitive maps, and this project proposes to characterize the emergence of a cognitive map in rodent OFC during
a value-based decision making task. This project asks three core questions: 1) How does a model-based RL
agent learn a cognitive map using purely model-free RL methods? Here recent advances in meta-RL in state-of-
the-art recurrent neural network (RNN) models will be trained with model-free RL, in which the emergence of a
cognitive map can be fully characterized. 2) How does the OFC represent cognitive maps? Here partially trained
rats will be implanted with Neuropixels probes, and neural recordings will capture emerging representations
of cognitive maps in population-level OFC activity as rats learn the task structure. 3) Can poor learning of
cognitive maps be bolstered with structured behavioral training? An attractive therapeutic approach for improving
decision making strategies is through behavioral training alone, and here an RNN model of rodents will be used
to characterize modes of poor learning and develop prescriptive training, and will then be employed in rats to
uniquely addresses specific learning deficits.
This proposed project employs both experimental and computational techniques as part a comprehensive career
development plan toward becoming an independent investigator. Specific experimental training for electrophysi-
ological recordings from behaving animals complements computational training on modeling neural activity with
deep neural networks. The career development plan also includes structured opportunities for collaborating with
experimentalists. It incorporates a breadth of science communication experiences through research conferences
and public talks within the local training institution. Importantly, the career development plan incorporates targeted
preparation for independent investigator applications, including chalk-talk opportunities, workshops to develop
unique research questions, and exposure to the faculty search process. The Constantinople, Savin, and Glim-
cher labs at New York University’s Center for Neural Science are a training environment uniquely positioned to
deliver this interdisciplinary training: their cutting-edge research into decision-making, animal behavior, reinforce-
ment learning, and systems neuroscience make it an ideal institution and set of labs...

## Key facts

- **NIH application ID:** 10848435
- **Project number:** 5K01MH132043-02
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** David Hocker
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $145,174
- **Award type:** 5
- **Project period:** 2023-06-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10848435

## Citation

> US National Institutes of Health, RePORTER application 10848435, Acquiring cognitive maps: how brains learn hidden structure (5K01MH132043-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10848435. Licensed CC0.

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