Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners

Autor/inn/en	Vong, Wai Keen; Hendrickson, Andrew T.; Navarro, Danielle J.; Perfors, Amy
Titel	Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners
Quelle	In: Cognitive Science, 43 (2019) 3, (25 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1551-6709
DOI	10.1111/cogs.12724
Schlagwörter	Learning Processes; Classification; Models; Performance; Computation; Experiments + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Achievement; Leistung; Erprobung
Abstract	The curse of dimensionality, which has been widely studied in statistics and machine learning, occurs when additional features cause the size of the feature space to grow so quickly that learning classification rules becomes increasingly difficult. How do people overcome the curse of dimensionality when acquiring real-world categories that have many different features? Here we investigate the possibility that the structure of categories can help. We show that when categories follow a family resemblance structure, people are unaffected by the presence of additional features in learning. However, when categories are based on a single feature, they fall prey to the curse, and having additional irrelevant features hurts performance. We compare and contrast these results to three different computational models to show that a model with limited computational capacity best captures human performance across almost all of the conditions in both experiments. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01