Related Experiment Video
Updated: Mar 3, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Prior beliefs influence symmetrical or asymmetrical generalizations in human causal learning
Ryoji Nishiyama1, Takatoshi Nagaishi2, Takahisa Masaki3
1Graduate School of Education, Kyoto University, Kyoto, Japan. ryoji.nishiyama@gmail.com.
Abstract:
The generalization decrement between element A and compound AX has shown both symmetrical (Thorwart & Lachnit, 2009) and asymmetrical (Glautier, 2004) patterns in human contingency learning. In a series of experiments we examined the hypothesis that prior beliefs about the relationship between a distinctive element X and an outcome are important for determining the different generalization patterns. Participants learned which given enterobacteria caused a negative or a positive effect on gastrointestinal conditions. Subsequently, they were asked to evaluate learned cues and novel cues in which distinctive elements were added to or removed from the enterobacteria. The results generally demonstrated that relatedness between the elements and outcomes, such as negative features combined with a negative outcome or positive features combined with a positive outcome, resulted in asymmetrical generalization patterns. By contrast, unrelated combinations, such as positive features and a negative outcome, produced symmetrical patterns of generalization. Configural and elemental models of stimulus generalization are discussed.
Related Concept Videos
Cognitive Learning
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.
Tolman introduced the idea that behavior is influenced by...
Cause and Effect
Schemas
Associative Learning
Classical conditioning, also known...
Purposive Learning
Fundamental Attribution Error

