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E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Related Experiment Video

Updated: Apr 14, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

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A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

Hongjing Lu^1,2, Randall R Rojas³, Tom Beckers^4,5

¹Department of Psychology, University of California, Los Angeles.

Cognitive Science

|April 24, 2015

Summary

People learn abstract causal rules from sequential data, not just specific links. Bayesian models explain how abstract outcome features guide rule selection for causal inference and transfer.

Keywords:

Abstract transfer Bayesian inference Blocking Causal learning Model selection Sequential causal inference

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Area of Science:

Cognitive Science
Psychology
Machine Learning

Background:

Causal learning research focuses on sequential data acquisition and learning abstraction levels.
Studies show prior causal cue experience alters new cue learning, suggesting abstract transfer.

Purpose of the Study:

To propose a Bayesian theory for sequential causal learning.
To explain how abstract causal constraints are acquired and utilized.

Main Methods:

Developed a Bayesian theory of sequential causal learning with alternative causal generative models.
Used computer simulations to test the theory against human learning phenomena.

Main Results:

Humans use abstract outcome variable characteristics (e.g., binary vs. continuous) to select causal integration rules.
Model selection adapts to the learning environment, influencing causal learning in blocking and overshadowing paradigms.

Conclusions:

The proposed Bayesian theory accounts for abstract transfer and various blocking effects in sequential causal learning.
Humans dynamically select causal integration models based on environmental fit and outcome properties.