Strategies in probabilistic categorization: Results from a new way of analyzing performance

Martijn Meeter, Catherine E. Myers, Daphna Shohamy, Ramona O. Hopkins, Mark A. Gluck

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

The "Weather Prediction" task is a widely used task for investigating probabilistic category learning, in which various cues are probabilistically (but not perfectly) predictive of class membership. This means that a given combination of cues sometimes belongs to one class and sometimes to another. Prior studies showed that subjects can improve their performance with training, and that there is considerable individual variation in the strategies subjects use to approach this task. Here, we discuss a recently introduced analysis of probabilistic categorization, which attempts to identify the strategy followed by a participant. Monte Carlo simulations show that the analysis can, indeed, reliably identify such a strategy if it is used, and can identify switches from one strategy to another. Analysis of data from normal young adults shows that the fitted strategy can predict subsequent responses. Moreover, learning is shown to be highly nonlinear in probabilistic categorization. Analysis of performance of patients with dense memory impairments due to hippocampal damage shows that although these patients can change strategies, they are as likely to fall back to an inferior strategy as to move to more optimal ones.

Original languageEnglish (US)
Pages (from-to)230-239
Number of pages10
JournalLearning and Memory
Volume13
Issue number2
DOIs
StatePublished - Mar 2006

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

Fingerprint Dive into the research topics of 'Strategies in probabilistic categorization: Results from a new way of analyzing performance'. Together they form a unique fingerprint.

Cite this