Feature inference learning and eyetracking

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

Research output: Contribution to journalArticle

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 1 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

Fingerprint Dive into the research topics of 'Feature inference learning and eyetracking'. Together they form a unique fingerprint.

  • Cite this