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Related Experiment Video

Updated: Jun 13, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Distributed representations of behaviour-derived object dimensions in the human visual system.

Oliver Contier^1,2, Chris I Baker³, Martin N Hebart^4,5

¹Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. contier@cbs.mpg.de.

Nature Human Behaviour

|September 9, 2024

Summary

Object vision processing is not limited to category recognition. New brain mapping reveals behavior-derived dimensions are broadly represented, offering a more expansive view of visual processing beyond simple object categories.

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Published on: August 1, 2018

Area of Science:

Neuroscience
Cognitive Science
Computer Vision

Background:

The traditional view posits a visual processing hierarchy for object recognition and categorization.
This category-centric framework may not fully capture the diverse behavioral goals supported by object vision.

Purpose of the Study:

To investigate limitations of the category-centric framework in object vision.
To map behavior-derived representational dimensions onto the brain.
To explore how these dimensions relate to known visual features and categories.

Main Methods:

Conducted a large-scale analysis of human similarity judgments to derive behavioral dimensions.
Mapped these dimensions directly onto brain activity using neuroimaging techniques.
Compared the predictive power of behavior-derived dimensions versus categories for brain responses.

Main Results:

Identified broadly distributed representations of behaviorally relevant information across the visual cortex.
Demonstrated selectivity for a wide range of novel, behavior-derived dimensions.
Found behavior-derived dimensions were superior to categories in predicting brain responses.
Observed mixed selectivity in much of the visual cortex and sparse selectivity in category-selective regions.

Conclusions:

Object vision processing is more expansive than previously thought, involving broadly distributed representations.
Category selectivity can be explained as a specific instance of sparse response profiles within a richer representational space.
This framework reconciles findings on regional specialization and offers a more comprehensive understanding of visual processing.