The Effect of Outcome Probability on Generalization in Predictive Learning

Hadar Ram, Dieter Struyf, Bram Vervliet, Gal Menahem, Nira Liberman

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

People apply what they learn from experience not only to the experienced stimuli, but also to novel stimuli. But what determines how widely people generalize what they have learned? Using a predictive learning paradigm, we examined the hypothesis that a low (vs. high) probability of an outcome following a predicting stimulus would widen generalization. In three experiments, participants learned which stimulus predicted an outcome (S+) and which stimulus did not (S-) and then indicated how much they expected the outcome after each of eight novel stimuli ranging in perceptual similarity to S+ and S-. The stimuli were rings of different sizes and the outcome was a picture of a lightning bolt. As hypothesized, a lower probability of the outcome widened generalization. That is, novel stimuli that were similar to S+ (but not to S-) produced expectations for the outcome that were as high as those associated with S+.

Original languageEnglish
Pages (from-to)23-39
Number of pages17
JournalExperimental Psychology
Volume66
Issue number1
DOIs
StatePublished - Jan 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Hogrefe Publishing.

Funding

This work was supported by the I-CORE Program of the Planning and Budgeting Committee and the Israel Science Foundation (Grant 51/11) and by a Center for Excellence grant from the University of Leuven – KU Leuven (PF/10/005).

FundersFunder number
University of LeuvenPF/10/005
Israel Science Foundation51/11
Planning and Budgeting Committee of the Council for Higher Education of Israel

    Keywords

    • Generalization
    • learning from experience
    • partial reinforcement
    • predictive learning

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