# Cognitive and Neural Representations of Reachable Environments

> **NIH NIH R21** · HARVARD UNIVERSITY · 2021 · $163,930

## Abstract

Project Summary
The broad goal of the proposed research is to characterize the way that humans perceive and
represent views of reachable environments. “Reachspaces” are an integral part of our world
and day-to-day experience, but how these views are processed by the visual cognitive system is
unknown. The present work aims to offer fundamental insights into the structure of internal
representations of the reachable environments. First, a large database of reachspace images
will be constructed. Next, this proposal will leverage a recently developed approach that
employs a large-scale behavioral experiment, coupled with a sparse positive embedding model,
which will be used to derive the attributes underlying the similarity structure of reachspace
views. Finally, brain responses to a selected subset of reachspace views will be measured and
modeled with the derived dimensions. These approaches have been tested and used to great
advance in domains of object and scene processing; here, we employ them on the novel
stimulus domain of reachspaces. This work has the potential to form new bridges between the
vision and visuomotor communities by characterizing high-level visual representations of
reachable environments and will expand scientific understand of how humans perceive the
visual environment.

## Key facts

- **NIH application ID:** 10219275
- **Project number:** 5R21EY031867-02
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** Talia Konkle
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $163,930
- **Award type:** 5
- **Project period:** 2020-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10219275

## Citation

> US National Institutes of Health, RePORTER application 10219275, Cognitive and Neural Representations of Reachable Environments (5R21EY031867-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10219275. Licensed CC0.

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