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

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 - 2019

Keywords

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

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