Feature inference learning and eyetracking

Bob Rehder, Robert M. Colner, Aaron B. Hoffman

Research output: Contribution to journalArticlepeer-review

Abstract

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of diagnostic information. We tracked learners' eye movements and found in Experiment 1 that inference learners indeed fixated features that were unnecessary for inferring the missing feature, behavior consistent with acquiring the categories' internal structure. However, Experiments 3 and 4 showed that fixations were generally limited to features that needed to be predicted on future trials. We conclude that inference learning induces both supervised and unsupervised learning of category-to-feature associations rather than a general motivation to learn the internal structure of categories.

Original languageEnglish (US)
Pages (from-to)393-419
Number of pages27
JournalJournal of Memory and Language
Volume60
Issue number3
DOIs
StatePublished - Apr 2009

Keywords

  • Category learning
  • Category representation
  • Eyetracking

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Language and Linguistics
  • Experimental and Cognitive Psychology
  • Linguistics and Language
  • Artificial Intelligence

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