Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization

Fabian A. Soto, Samuel J. Gershman, Yael Niv

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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 long-standing problems for rational theories of associative and causal learning.

Original languageEnglish (US)
Pages (from-to)526-558
Number of pages33
JournalPsychological Review
Issue number3
StatePublished - Jul 2014

All Science Journal Classification (ASJC) codes

  • General Psychology


  • Associative learning
  • Bayesian model
  • Causal learning
  • Dimensional separability
  • Generalization


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