# Cognitive maps for goal-directed decision making

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2022 · $504,353

## Abstract

Abstract
Cognitive maps refer to internal representations of spatial and non-spatial relationships between
entities (people or things) or events in the external world. There has been widespread
excitement generated by recent discoveries that even continuous non-spatial task dimensions
may be organized and ‘navigated’ as a cognitive map. These studies suggest the neural
representations and computations revealed in physical space may be only one instance of a
general mechanism for organizing and “navigating” any behaviorally-relevant continuous task
dimensions (e.g. space, time, sound frequency, metric length). This insight raises the intriguing
possibility that the well-established coding principles revealed during spatial navigation can also
be used to understand flexible decision making in abstract and discrete tasks that are
commonplace in the real world when they are based on a cognitive map, such as whom to
collaborate with or where to eat.
 A cognitive map of an environment or task is incredibly powerful because it enables
inferences to be made from limited experiences that can dramatically accelerate new learning
and even guide novel decisions never faced before. Moreover, similar tasks that share an
overall structure can be directly related to one another, thereby facilitating rapid generalization
from one task or entity to another. Despite this wide-ranging importance for flexible cognition,
we have only a basic understanding of how cognitive maps enable such novel inferences and
generalization. Better understanding the mechanisms involved also carry significant clinical
implications. Indeed, abnormal inferences, cognitive flexibility, and generalization are thought to
core dysfunctions in several psychiatric conditions, ranging from schizophrenia to obsessive
compulsive disorder to certain expressions of mood disorder. It follows that developing a
mechanistic model of these component processes in humans has the potential to inform
principled investigations into biomarkers and treatment targets for these disorders.
 The goal of this proposal is to develop a new neural model of how cognitive maps of
abstract and discrete tasks are represented neurally and used to guide novel inferences during
decision making in the human brain. We have developed a new experimental paradigm that
induces people to form abstract and discrete cognitive maps (e.g. of social networks) and
perform novel inferences during decision making. To develop our model, we will combine
computational models of learning and inference in this paradigm with geometric models of
neural coding derived from spatial navigation and “representational” and computational
functional magnetic resonance imaging analysis methods that allow inferences to be made
about the information represented and computations performed in different brain structures,
respectively. The insights gained from this research will lead to substantial theoretical advances
in models of goal-directed decision making...

## Key facts

- **NIH application ID:** 10414966
- **Project number:** 5R01MH123713-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Erie D Boorman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $504,353
- **Award type:** 5
- **Project period:** 2021-06-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10414966

## Citation

> US National Institutes of Health, RePORTER application 10414966, Cognitive maps for goal-directed decision making (5R01MH123713-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10414966. Licensed CC0.

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