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Simplicity and complexity of probabilistically defined concepts.

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Τίτλος: Simplicity and complexity of probabilistically defined concepts.
Συγγραφείς: Feldman J; Department of Psychology, Center for Cognitive Science, Rutgers University-New Brunswick.
Πηγή: Psychological review [Psychol Rev] 2026 Apr; Vol. 133 (3), pp. 560-583. Date of Electronic Publication: 2025 May 15.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: American Psychological Association Country of Publication: United States NLM ID: 0376476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-1471 (Electronic) Linking ISSN: 0033295X NLM ISO Abbreviation: Psychol Rev Subsets: MEDLINE
Imprint Name(s): Original Publication: Washington, DC : American Psychological Association
Ιατρικοί όροι (MeSH): Concept Formation*/physiology , Learning*/physiology , Probability Learning*, Humans ; Adult ; Young Adult ; Probability ; Male
Περίληψη: Human concept learning is known to be impaired by conceptual complexity: Simpler concepts are easier to learn, and more complex ones are more difficult. However, the simplicity bias has been studied almost exclusively in the context of deterministic concepts defined over Boolean features and is comparatively unexplored in the more general case of probabilistic concepts defined over continuous features. This article reports a series of experiments in which subjects were asked to learn probabilistic concepts defined over a novel 2D continuous feature space. Each concept was a mixture of several distinct Gaussian components, and the complexity of the concepts was varied by manipulating the positions of the mixture components relative to each other while holding the number of components constant. The results confirm that the positioning of mixture components strongly impacts learning, independent of the intrinsic statistical separability of the concepts, which was manipulated independently. Moreover, the results point to an information-theoretic basis framework for quantifying the complexity of probabilistic concepts, centered on the notion of compressive complexity: Simple concepts are those that can be approximately recovered from a projection of the concept onto a lower dimensional feature space, while more complex concepts are those that can only be represented by combining features. The framework provides a consistent, coherent, and broadly applicable measure of the complexity of probabilistic concepts. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Grant Information: National Science Foundation
Entry Date(s): Date Created: 20250515 Date Completed: 20260423 Latest Revision: 20260423
Update Code: 20260423
DOI: 10.1037/rev0000563
PMID: 40372880
Βάση Δεδομένων: MEDLINE
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  Data: Simplicity and complexity of probabilistically defined concepts.
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  Data: Human concept learning is known to be impaired by conceptual complexity: Simpler concepts are easier to learn, and more complex ones are more difficult. However, the simplicity bias has been studied almost exclusively in the context of deterministic concepts defined over Boolean features and is comparatively unexplored in the more general case of probabilistic concepts defined over continuous features. This article reports a series of experiments in which subjects were asked to learn probabilistic concepts defined over a novel 2D continuous feature space. Each concept was a mixture of several distinct Gaussian components, and the complexity of the concepts was varied by manipulating the positions of the mixture components relative to each other while holding the number of components constant. The results confirm that the positioning of mixture components strongly impacts learning, independent of the intrinsic statistical separability of the concepts, which was manipulated independently. Moreover, the results point to an information-theoretic basis framework for quantifying the complexity of probabilistic concepts, centered on the notion of compressive complexity: Simple concepts are those that can be approximately recovered from a projection of the concept onto a lower dimensional feature space, while more complex concepts are those that can only be represented by combining features. The framework provides a consistent, coherent, and broadly applicable measure of the complexity of probabilistic concepts. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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      – SubjectFull: Probability Learning
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      – TitleFull: Simplicity and complexity of probabilistically defined concepts.
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              Text: 2026 Apr
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