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Related Experiment Video

Updated: Jun 7, 2026

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

Analogical and category-based inference: a theoretical integration with Bayesian causal models.

Keith J Holyoak¹, Hee Seung Lee, Hongjing Lu

¹Department of Psychology, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1563, USA. holyoak@lifesci.ucla.edu

Journal of Experimental Psychology. General

|November 3, 2010

Summary

This summary is machine-generated.

Human induction relies on causal models to make accurate predictions, balancing accuracy with uncertainty. This research explores how people use source information and prior knowledge to build these models for effective reasoning.

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Area of Science:

Cognitive Science
Psychology
Artificial Intelligence

Background:

Human induction requires constraints on potential inferences.
Inferences often rely on category membership, analogy, or relational schemas.
The goal is accurate, goal-relevant inferences sensitive to uncertainty.

Purpose of the Study:

To propose a computational theory for human causal induction.
To test Bayesian inference predictions in human judgment tasks.
To understand how source information and prior knowledge constrain target inferences.

Main Methods:

Developed a Bayesian inference computational theory.
Conducted experiments on causal predictions and attributions.
Assessed probability judgments based on source knowledge of causes.

Main Results:

The theory successfully accounted for systematic patterns in causal judgments.
Demonstrated that analogical inferences are partly separable from mapping quality.
Showed how people integrate abstract and specific source information with prior causal knowledge.

Conclusions:

Bayesian inference provides a viable framework for human causal induction.
Causal models built from diverse information sources effectively constrain inferences.
Understanding these constraints is key to explaining human inductive reasoning.