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Related Experiment Video

Updated: Apr 20, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Making Probabilistic Relational Categories Learnable.

Wookyoung Jung¹, John E Hummel¹

¹Department of Psychology, University of Illinois.

Cognitive Science

|November 18, 2014

Summary

This summary is machine-generated.

Learning probabilistic relational concepts is easier than expected. A "who's winning" task, compared to standard category learning, significantly improved participants' ability to acquire these complex concepts.

Keywords:

Family resemblance Higher order relations Relational category learning Relational invariants Who's winning task

Related Experiment Videos

Last Updated: Apr 20, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Cognitive Psychology
Concept Learning
Machine Learning Theory

Background:

Relational concept acquisition theories, like schema induction, predict difficulty with probabilistic categories.
These theories are based on structured intersection discovery, which may not align with family resemblance structures.

Purpose of the Study:

To test the prediction that probabilistic relational concepts are difficult to learn.
To investigate conditions that facilitate the learning of such categories.

Main Methods:

Four experiments were conducted to test the learning of probabilistic relational categories.
Tasks were manipulated, including a category-learning task and a "who's winning" object choice task.
Naturalistic stimuli were used in later experiments to generalize findings.

Main Results:

A "who's winning" task significantly facilitated learning of probabilistic relational categories compared to standard category learning.
This effect was investigated further in Experiments 2 and 3.
The "who's winning" effect was replicated and generalized with natural stimuli in Experiment 4.

Conclusions:

People learn relational concepts through intersection discovery, similar to schema induction.
Tasks promoting the discovery of invariant higher-order relations enhance learning of probabilistic relational concepts.