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

On the nature of implicit categorization.

F G Ashby¹, E M Waldron

¹Department of Psychology, University of California, Santa Barbara, CA 93106, USA. ashby@psych.ucsb.edu

Psychonomic Bulletin & Review

|August 30, 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

The effects of concurrent task interference on category learning: evidence for multiple category learning systems.

Psychonomic bulletin & review·2001

Same author

Suboptimality in human categorization and identification.

Journal of experimental psychology. General·2001

Same author

On the dominance of unidimensional rules in unsupervised categorization.

Perception & psychophysics·1999

Same author

A neuropsychological theory of positive affect and its influence on cognition.

Psychological review·1999

Same author

A neuropsychological theory of multiple systems in category learning.

Psychological review·1998

Same author

Response time distributions in multidimensional perceptual categorization.

Perception & psychophysics·1998

Same journal

Mind wandering during first- and foreign-language reading.

Psychonomic bulletin & review·2026

Same journal

Lexical word processing is unaffected by rapid invisible frequency tagging in reading: Evidence from eye movements.

Psychonomic bulletin & review·2026

Same journal

Anxiety modulates voluntary attentional orienting to emotional gaze cues: Eye movements for pro- and anti-saccades.

Psychonomic bulletin & review·2026

Same journal

Faster key-press responses to front vowels than back vowels when matching heard vowels with represented vowels.

Psychonomic bulletin & review·2026

Same journal

Testing the interleaving effect without response bias: A forced-choice reevaluation of Kornell and Bjork (2008).

Psychonomic bulletin & review·2026

Same journal

The impact of social interaction on abstract concepts.

Psychonomic bulletin & review·2026

See all related articles

People do not make assumptions about unfamiliar category structures. This finding challenges existing models and supports a new procedural learning approach to category learning.

Area of Science:

Cognitive psychology
Neuroscience
Machine learning

Background:

Current models of category learning propose differing views on whether humans make a priori assumptions about the structure of novel categories.
This disagreement impacts the understanding of underlying cognitive mechanisms and computational approaches to categorization.

Purpose of the Study:

To investigate whether people make a priori assumptions about the structure of unfamiliar categories.
To challenge existing categorization models, including prototype and decision bound models.
To propose a new category learning model based on experimental and neuropsychological evidence.

Main Methods:

Two experiments were conducted to gather data on human categorization behavior.
Analysis of previously published neuropsychological results was performed.

Related Experiment Videos

A new computational model of category learning was developed.

Main Results:

Experimental data strongly indicated that individuals do not make a priori assumptions about category structure.
These findings provide evidence against prototype and many decision bound models of categorization.
Neuropsychological data supports a procedural memory system over an instance-based system for category learning.

Conclusions:

Human category learning does not rely on pre-existing assumptions about category structure.
Existing models, such as prototype and decision bound models, are insufficient.
A novel category learning model emphasizing procedural learning and memory, without a priori structural assumptions, is proposed.