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Integrating Physical Constraints in Statistical Inference by 11‐Month‐Old Infants
Author(s) -
Denison Stephanie,
Xu Fei
Publication year - 2010
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6709.2010.01111.x
Subject(s) - probabilistic logic , constraint (computer aided design) , inference , domain (mathematical analysis) , statistical inference , cognition , set (abstract data type) , computer science , domain knowledge , statistical model , psychological research , artificial intelligence , psychology , cognitive psychology , machine learning , cognitive science , mathematics , statistics , social psychology , mathematical analysis , geometry , neuroscience , programming language
Much research on cognitive development focuses either on early‐emerging domain‐specific knowledge or domain‐general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11‐ to 12.5‐month‐old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a statistical inference mechanism. Results from three experiments suggest that, first, infants were able to use domain‐specific knowledge to override statistical information, reasoning that sometimes a physical constraint is more informative than probabilistic information. Second, we provide the first evidence that infants are capable of applying domain‐specific knowledge in probabilistic reasoning by using a physical constraint to exclude one set of objects while computing probabilities over the remaining sets.