Making Probabilistic Relational Categories Learnable

Wookyoung Jung, John E. Hummel

Research output: Contribution to journalArticlepeer-review


Theories of relational concept acquisition (e.g., schema induction) based on structured intersection discovery predict that relational concepts with a probabilistic (i.e., family resemblance) structure ought to be extremely difficult to learn. We report four experiments testing this prediction by investigating conditions hypothesized to facilitate the learning of such categories. Experiment 1 showed that changing the task from a category-learning task to choosing the "winning" object in each stimulus greatly facilitated participants' ability to learn probabilistic relational categories. Experiments 2 and 3 further investigated the mechanisms underlying this "who's winning" effect. Experiment 4 replicated and generalized the "who's winning" effect with more natural stimuli. Together, our findings suggest that people learn relational concepts by a process of intersection discovery akin to schema induction, and that any task that encourages people to discover a higher order relation that remains invariant over members of a category will facilitate the learning of putatively probabilistic relational concepts.

Original languageEnglish (US)
Pages (from-to)1259-1291
Number of pages33
JournalCognitive Science
Issue number6
StatePublished - Aug 1 2015


  • Family resemblance
  • Higher order relations
  • Relational category learning
  • Relational invariants
  • Who's winning task

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence


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