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Related Experiment Video

Updated: Jun 23, 2026

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

Causal learning with local computations.

Philip M Fernbach¹, Steven A Sloman

¹Department of Psychology, Brown University, Providence, RI 02912, USA. Philip_Fernbach@Brown.edu

Journal of Experimental Psychology. Learning, Memory, and Cognition

|April 22, 2009

Summary

This summary is machine-generated.

This study reveals that humans use simple, local computations as a heuristic for causal structure learning. These heuristics simplify complex problems, making learning efficient even with limited data.

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Area of Science:

Cognitive Psychology
Machine Learning
Computational Neuroscience

Background:

Causal structure learning is fundamental to human cognition.
Traditional models often assume complex, global computations.
The cognitive plausibility of these complex models is debated.

Purpose of the Study:

To propose and test a psychological theory of causal structure learning based on local computations.
To investigate the efficiency and limitations of heuristic-based causal inference.

Main Methods:

Three experiments were conducted to test the theory.
Data involved participants learning causal relationships.
Model comparisons were made between local computation models and Bayesian inference models.

Main Results:

Participants made systematic inferences even with limited data.
Extraneous causal links were systematically inferred.
Data presentation order influenced causal inferences.
Pretraining reduced inferential errors.

Conclusions:

Local computations serve as a cognitive heuristic for causal structure learning.
This heuristic approach offers advantages in memory and data efficiency over normative models.
Heuristic-based learning may lead to systematic biases but is computationally tractable.