Skip to main content

Explaining Compound Generalization in Associative and Causal Learning Through Rational Principles of Dimensional Generalization

Author(s): Soto, Fabian A.; Gershman, Samuel J.; Niv, Yael

To refer to this page use:
Abstract: How do we apply learning from one situation to a similar, but not identical, situation? The principles governing the extent to which animals and humans generalize what they have learned about certain stimuli to novel compounds containing those stimuli vary depending on a number of factors. Perhaps the best studied among these factors is the type of stimuli used to generate compounds. One prominent hypothesis is that different generalization principles apply depending on whether the stimuli in a compound are similar or dissimilar to each other. However, the results of many experiments cannot be explained by this hypothesis. Here we propose a rational Bayesian theory of compound generalization that uses the notion of consequential regions, first developed in the context of rational theories of multidimensional generalization, to explain the effects of stimulus factors on compound generalization. The model explains a large number of results from the compound generalization literature, including the influence of stimulus modality and spatial contiguity on the summation effect, the lack of influence of stimulus factors on summation with a recovered inhibitor, the effect of spatial position of stimuli on the blocking effect, the asymmetrical generalization decrement in overshadowing and external inhibition, and the conditions leading to a reliable external inhibition effect. By integrating rational theories of compound and dimensional generalization, our model provides the first comprehensive computational account of the effects of stimulus factors on compound generalization, including spatial and temporal contiguity between components, which have posed longstanding problems for rational theories of associative and causal learning.
Publication Date: 2014
Electronic Publication Date: 2014
Citation: Soto, Fabian A, Gershman, Samuel J, Niv, Yael. (2014). Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization.. Psychological Review, 121 (3), 526 - 558. doi:10.1037/a0037018
DOI: doi:10.1037/a0037018
ISSN: 0033-295X
EISSN: 1939-1471
Pages: 526 - 558
Type of Material: Journal Article
Journal/Proceeding Title: Psychological Review
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.