Error Discounting in Probabilistic Category Learning

Stewart Craig, Stephan Lewandowsky*, Daniel R. Little

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)

Abstract

The assumption in some current theories of probabilistic categorization is that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report 2 probabilistic-categorization experiments in which we investigated error discounting by shifting feedback probabilities to new values after different amounts of training. In both experiments, responding gradually became less responsive to errors, and learning was slowed for some time after the feedback shift. Both results were indicative of error discounting. Quantitative modeling of the data revealed that adding a mechanism for error discounting significantly improved the fits of an exemplar-based and a rule-based associative learning model, as well as of a recency-based model of categorization. We conclude that error discounting is an important component of probabilistic learning.

Original languageEnglish
Pages (from-to)673-687
Number of pages15
JournalJournal of Experimental Psychology: Learning, Memory, and Cognition
Volume37
Issue number3
DOIs
Publication statusPublished - May 2011

Structured keywords

  • Memory

Keywords

  • categorization
  • learning
  • error
  • relevance shifts
  • ADAPTIVE NETWORK MODEL
  • CONNECTIONIST MODEL
  • DELAYED EXPOSURE
  • CATEGORIZATION
  • AUTOMATION
  • EXEMPLAR
  • HUMANS
  • CHOICE
  • INFORMATION
  • SITUATION

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