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Related Experiment Video

Updated: Jun 8, 2026

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

Geometric and featural representations in semantic concepts.

Wolf Vanpaemel¹, Timothy Verbeemen, Matthew Dry

¹Department of Psychology, University of Leuven, Leuven, Belgium. wolf.vanpaemel@psy.kuleuven.be

Memory & Cognition

|October 6, 2010

Summary

People use different strategies to categorize items based on familiarity. Geometric models work best for unfamiliar items, while featural models are better for familiar ones, showing a shift from perceptual to conceptual knowledge.

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Last Updated: Jun 8, 2026

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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

Area of Science:

Cognitive Psychology
Computational Linguistics
Cognitive Science

Background:

Understanding semantic concept representation is crucial in cognitive science.
Similarity representation influences categorization decisions.
Both geometric and featural models are proposed for concept representation.

Purpose of the Study:

To evaluate the adequacy of geometric versus featural similarity representations.
To determine how stimulus familiarity affects the optimal representation for categorization.
To investigate the interplay between perceptual and conceptual knowledge in categorization.

Main Methods:

Developed and evaluated categorization models based on geometric and featural representations.
Used categorization decisions for familiar and unfamiliar foods and animals as data.
Compared model performance against human categorization data.

Main Results:

Geometric categorization models best explained data for unfamiliar stimuli.
Featural categorization models best explained data for familiar stimuli.
Demonstrated a shift in representation strategy based on stimulus familiarity.

Conclusions:

Stimulus representation is not fixed but adapts to familiarity.
Unfamiliar stimuli rely on perceptual information (geometric representation).
Familiar stimuli leverage conceptual knowledge (featural representation).