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

Wai Keen Vong, Andrew T Hendrickson, Danielle J Navarro, Amy Perfors

    Research output: Contribution to journalArticleScientificpeer-review

    11 Citations (Scopus)

    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.

    Original languageEnglish
    Article number12724
    Pages (from-to)e12724
    Number of pages25
    JournalCognitive Science
    Volume43
    Issue number3
    DOIs
    Publication statusPublished - Mar 2019

    Keywords

    • CATEGORIZATION
    • CLASSIFICATION
    • Category learning
    • Curse of dimensionality
    • IDENTIFICATION
    • KNOWLEDGE
    • MODELS
    • Supervised learning

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