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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Creating Something Different: Similarity, Contrast, and Representativeness in Categorization.

Joseph L Austerweil¹, Shi Xian Liew², Nolan Conaway¹

¹Department of Psychology, University of Wisconsin-Madison, 1202 West Johnson Street, Madison, 53706 WI USA.

Computational Brain & Behavior

|July 16, 2026

Summary

Humans generate new concepts by contrasting them with existing ones, distributing new ideas into unoccupied mental spaces. This study models concept generation using exemplar dissimilarity and representativeness heuristics.

Keywords:

Categorization Category learning Computational modeling Concepts Exemplar models Generation Representativeness

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Area of Science:

Cognitive Science
Psychology
Computational Modeling

Background:

Human concept generation is not fully understood.
Previous research focused on generalization, neglecting category contrast.

Purpose of the Study:

Investigate the role of contrast in concept generation.
Propose and evaluate computational models of category contrast.

Main Methods:

Developed two models: exemplar dissimilarity and representativeness heuristic.
Conducted three behavioral experiments on category generation.
Compared model performance against human data.

Main Results:

People generate new categories that contrast with known ones.
Exemplars are distributed in unoccupied stimulus space.
The representativeness heuristic model performed better for Gaussian-distributed categories.
The exemplar-based model performed better for non-Gaussian categories.

Conclusions:

Contrast is a fundamental principle in generating new category exemplars.
Model choice depends on the statistical properties of known categories.