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Sensitivity to sampling in Bayesian word learning
Author(s) -
Xu Fei,
Tenenbaum Joshua B.
Publication year - 2007
Publication title -
developmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.801
H-Index - 127
eISSN - 1467-7687
pISSN - 1363-755X
DOI - 10.1111/j.1467-7687.2007.00590.x
Subject(s) - psychology , word (group theory) , object (grammar) , associative learning , associative property , bayesian inference , inference , bayesian probability , meaning (existential) , context (archaeology) , sampling (signal processing) , sample (material) , cognitive psychology , word learning , artificial intelligence , natural language processing , linguistics , computer science , vocabulary , mathematics , paleontology , philosophy , chemistry , filter (signal processing) , chromatography , computer vision , pure mathematics , psychotherapist , biology
We report a new study testing our proposal that word learning may be best explained as an approximate form of Bayesian inference ( Xu & Tenenbaum, in press ). Children are capable of learning word meanings across a wide range of communicative contexts. In different contexts, learners may encounter different sampling processes generating the examples of word–object pairings they observe. An ideal Bayesian word learner could take into account these differences in the sampling process and adjust his/her inferences about word meaning accordingly. We tested how children and adults learned words for novel object kinds in two sampling contexts, in which the objects to be labeled were sampled either by a knowledgeable teacher or by the learners themselves. Both adults and children generalized more conservatively in the former context; that is, they restricted the label to just those objects most similar to the labeled examples when the exemplars were chosen by a knowledgeable teacher, but not when chosen by the learners themselves. We discuss how this result follows naturally from a Bayesian analysis, but not from other statistical approaches such as associative word‐learning models.