# Cortical Mechanisms of Visual Category Recognition and Learning

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2021 · $442,195

## Abstract

Summary and Relevance of Proposed Research
Humans and other advanced animals have an impressive capacity to recognize the behavioral significance, or
category membership, of a wide range of sensory stimuli. This ability, which is disrupted by a number of brain
diseases and conditions such as Alzheimer's disease, schizophrenia, stroke, and attention deficit disorder, is
critical because it allows us to respond appropriately to the continuous stream of stimuli and events that we
encounter in our interactions with the environment. Of course, we are not born with a built in library of
meaningful categories, such as “tables” and “chairs”, which we are preprogrammed to recognize. Instead, we
learn to recognize the meaning of such stimuli through experience. The goal of the studies proposed here is to
move towards a more detailed understanding of the brain mechanisms underlying the learning and recognition
visual categories. Recent work has shown that both the posterior parietal cortex and prefrontal cortex are
involved in encoding the category membership of visual stimuli and forming categorical decisions. In these
studies, we recorded from neurons in the parietal cortex during performance of a categorization task in which
360º of motion directions were grouped into two arbitrary categories that were divided by a learned category
boundary. These recordings revealed that neurons in both areas robustly encoded stimuli according to their
learned category membership, suggesting that parietal visual representations can reflect abstract information
about the learned significance of visual stimuli. The goals of the proposed studies are to develop a mechanistic
understanding of how visual feature representations in visual cortex are rapidly transformed into category
encoding in parietal and prefrontal cortices, to understand the mechanisms of flexible rule-based decision
making, and to determine how neuronal category representations develop in real time during the category
learning process.
While much is known about how the brain processes simple sensory features (such as color, orientation, and
direction of motion), less is known about how the brain learns and represents the meaning, or category, of
stimuli. A greater understanding of visual learning and categorization is critical for addressing a number of
brain diseases and conditions (e.g. stroke, Alzheimer's disease, attention deficit disorder, schizophrenia, and
stroke) that leave patients impaired in everyday tasks that require visual learning, recognition and/or evaluating
and responding appropriately to sensory information. The long-term goal of this project is to guide the next
generation of treatments for these brain-based diseases and disorders by helping to develop a detailed
understanding of the brain mechanisms that underlie learning, memory and recognition. These studies also
have relevance for understanding and addressing learning disabilities, such as attention deficit disorder and
dyslexia, ...

## Key facts

- **NIH application ID:** 10225995
- **Project number:** 5R01EY019041-13
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** David J Freedman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $442,195
- **Award type:** 5
- **Project period:** 2009-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10225995

## Citation

> US National Institutes of Health, RePORTER application 10225995, Cortical Mechanisms of Visual Category Recognition and Learning (5R01EY019041-13). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10225995. Licensed CC0.

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