# Dissociating spatial and cognitive grid representations in the brain

> **NIH NIH P20** · BROWN UNIVERSITY · 2021 · $162,500

## Abstract

Humans and primates display an impressive knack for acquiring knowledge in one environment (for example the 
arrangement of produce and dairy at the grocery store) and rapidly applying it in a completely novel one (a new 
grocery store). The ability to generalize behavior from past contexts to new ones is critical to prediction, inference, 
and planning in the real world. Though models of artificial intelligence inspired by neural learning algorithms have 
achieved human levels of performance on a wide range of tasks, the most prominent such models are incapable of 
generalizing information beyond the problems that they were trained on. In principle, such transfer requires 
extracting abstract information from one situation and then applying it in another, yet the neural mechanisms of this 
abstraction process remain unknown. 
Here we propose to test the hypothesis that the brain achieves such transfer using an abstract cognitive map, much 
like that used to navigate physical space. In particular, we propose to test whether grid-like representations in the 
brain, which are thought to facilitate spatial navigation and can be observed in neural firing or fMRI BOLD activity, 
tile an abstract space (eg. the layout of a typical grocery store) and facilitate the transfer of information collected in 
one environment (eg. Whole Foods) to another (eg. Eastside Market). To do so, we developed a novel task in which 
abstract grid representations can be dissociated through neuroimaging from more standard spatial grid 
representations. We will test which of these patterns is observed in the BOLD activity of the entorhinal cortex 
concurrent with task performance, and the degree to which such patterns relate to behavioral measures of transfer 
learning. We will incorporate results from these analyses into a model that learns to associate cues with locations via 
grid-like basis functions. The results of this study will shed light on the neural mechanisms of transfer learning and 
serve as preliminary data for an R01 application focused on neural representations for efficient learning.

## Key facts

- **NIH application ID:** 10655777
- **Project number:** 5P20GM103645-09
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Matthew Nassar
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $162,500
- **Award type:** 5
- **Project period:** 2021-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10655777

## Citation

> US National Institutes of Health, RePORTER application 10655777, Dissociating spatial and cognitive grid representations in the brain (5P20GM103645-09). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10655777. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*