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Related Experiment Video

Updated: Mar 5, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.

George Cantwell¹, Maximilian Riesenhuber², Jessica L Roeder¹

¹University of California, Santa Barbara, United States.

Neural Networks : the Official Journal of the International Neural Network Society

|March 22, 2017

Summary

This study integrates computational cognitive neuroscience models to create a novel system for perceptual category learning. The HMAX/COVIS model accurately simulates human behavioral and neural data in visual categorization tasks.

Keywords:

Basal ganglia COVIS Categorization Computational cognitive neuroscience HMAX Visual neuroscience

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Area of Science:

Computational cognitive neuroscience
Computational neuroscience
Cognitive science

Background:

Computational cognitive neuroscience (CCN) develops neurobiologically detailed models for behavior and neuroscience data.
A key CCN advantage is interfacing models in a plug-and-play manner.
Existing models include HMAX for visual object processing and COVIS for category learning.

Purpose of the Study:

To create a novel, biologically detailed computational model of perceptual category learning.
To leverage the plug-and-play capability of CCN models.
To integrate the HMAX and COVIS models.

Main Methods:

Combined the HMAX and COVIS models into a new HMAX/COVIS model.
Utilized bitmap images as input.
Adjusted learning-rate parameters.
Tested the model on human category-learning data from two distinct experiments.

Main Results:

The HMAX/COVIS model demonstrated strong fits to human behavioral data.
The model successfully accounted for data from experiments with different category structures and visual stimuli.
Achieved a comprehensive neural and behavioral account of basal ganglia-mediated learning.

Conclusions:

The integrated HMAX/COVIS model provides a biologically detailed account of perceptual category learning.
Demonstrates the utility of interfacing CCN models for creating complex cognitive systems.
Offers insights into basal ganglia function in category learning.