Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Distinguishing prototype-based and exemplar-based processes in dot-pattern category learning.

J David Smith¹, John Paul Minda

¹Department of Psychology and Center for Cognitive Science, State University of New York at Buffalo, 14260, USA. psysmith@acsu.buffalo.edu

Journal of Experimental Psychology. Learning, Memory, and Cognition

|July 12, 2002

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A longitudinal investigation of school absenteeism and mental health challenges among Canadian children and youth in the COVID-19 context.

Frontiers in child and adolescent psychiatry·2025

Same author

A systematic review of stress reduction interventions among graduate students.

Journal of American college health : J of ACH·2025

Same author

The spacing effect in remote information-integration category learning.

Memory & cognition·2024

Same author

Social and moral psychology of COVID-19 across 69 countries.

Scientific data·2023

Same author

Reexamining the "brain drain" effect: A replication of Ward et al. (2017).

Acta psychologica·2022

Same author

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning.

PNAS nexus·2022

Same journal

Testing the predictions of a distinctiveness model of memory: The production effect in backward recall.

Journal of experimental psychology. Learning, memory, and cognition·2026

Same journal

On the impact of adjacency on transposed-word effects under serial presentation.

Journal of experimental psychology. Learning, memory, and cognition·2026

Same journal

It's time to opt out: Metacognitive analysis of time regulation under uncertainty.

Journal of experimental psychology. Learning, memory, and cognition·2026

Same journal

The role of statistical learning in attentional guidance during search through naturalistic scenes.

Journal of experimental psychology. Learning, memory, and cognition·2026

Same journal

Representing objects and features in long-term memory: A case for direct feature-feature binding.

Journal of experimental psychology. Learning, memory, and cognition·2026

Same journal

Crossmodal correspondences influence adaptation during rule-based category learning of objects.

Journal of experimental psychology. Learning, memory, and cognition·2026

See all related articles

This study on dot-pattern categorization reveals that people categorize new patterns based on a central concept (prototype), not specific examples (exemplars). This finding clarifies how we form and use mental categories.

Area of Science:

Cognitive Psychology
Computational Neuroscience
Machine Learning

Background:

Categorization research distinguishes between exemplar-based and prototype-based models.
Understanding categorization processes is crucial for cognitive modeling and artificial intelligence.

Purpose of the Study:

To empirically contrast exemplar-based and prototype-based cognitive processes in dot-pattern categorization.
To determine whether categorization relies on stored exemplars or abstract prototypes.

Main Methods:

Participants rated similarity of dot-pattern distortions in Experiments 1A and 1B.
Experiment 2 involved participants learning dot-pattern categories and then classifying new patterns.
Typicality gradients and prototype-enhancement effects were analyzed.

Related Experiment Videos

Main Results:

Comparing distortions to surrounding exemplars resulted in flat typicality gradients and small prototype effects.
Comparing distortions to a central prototype yielded steep typicality gradients and large prototype effects.
Prototype models showed a superior fit, indicating reliance on a central category representation.

Conclusions:

Findings suggest that categorization predominantly involves reference to a central prototype rather than specific training exemplars.
Prototype-based models better explain the observed steep typicality gradients and prototype-enhancement effects.
This supports the hypothesis that abstract representations guide human categorization of visual patterns.