Making Probabilistic Relational Categories Learnable

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 in...

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Bibliographic Details
Published in:Cognitive science 2015-08, Vol.39 (6), p.1259-1291
Main Authors: Jung, Wookyoung, Hummel, John E.
Format: Article
Language:English
Subjects:
Cognition & reasoning
Concept Formation
Concept Formation - physiology
Educational Experiments
Experiments
Family resemblance
Higher order relations
Humans
Hypothesis Testing
Learning - physiology
Learning Processes
Neuropsychological Tests
Photic Stimulation
Predictions
Probability
Reaction Time - physiology
Relational category learning
Relational invariants
Schemata (Cognition)
Task Analysis
Thinking Skills
Who's winning task
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Summary: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.
ISSN:0364-0213
1551-6709
DOI:10.1111/cogs.12199