More than one kind of inference

Re-examining what's learned in feature inference and classification

Naomi Sweller*, Brett K. Hayes

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Citations (Scopus)


Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

Original languageEnglish
Pages (from-to)1568-1589
Number of pages22
JournalQuarterly Journal of Experimental Psychology
Issue number8
Publication statusPublished - Aug 2010


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