An associative model of adaptive inference for learning word-referent mappings |
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Authors: | Kachergis George Yu Chen Shiffrin Richard M |
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Institution: | (1) Department of Psychological & Brain Science, Cognitive Science Program, Bloomington, IN 47405, USA |
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Abstract: | People can learn word–referent pairs over a short series of individually ambiguous situations containing multiple words and
referents (Yu & Smith, 2007, Cognition 106: 1558–1568). Cross-situational statistical learning relies on the repeated co-occurrence of words with their
intended referents, but simple co-occurrence counts cannot explain the findings. Mutual exclusivity (ME: an assumption of
one-to-one mappings) can reduce ambiguity by leveraging prior experience to restrict the number of word–referent pairings
considered but can also block learning of non-one-to-one mappings. The present study first trained learners on one-to-one
mappings with varying numbers of repetitions. In late training, a new set of word–referent pairs were introduced alongside
pretrained pairs; each pretrained pair consistently appeared with a new pair. Results indicate that (1) learners quickly infer
new pairs in late training on the basis of their knowledge of pretrained pairs, exhibiting ME; and (2) learners also adaptively
relax the ME bias and learn two-to-two mappings involving both pretrained and new words and objects. We present an associative
model that accounts for both results using competing familiarity and uncertainty biases. |
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Keywords: | |
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