An associative model of adaptive inference for learning word-referent mappings.
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.
Department of Psychological & Brain Science, Cognitive Science Program, Bloomington, IN 47405, USA. gkacherg@indiana.edu
George
G
Kachergis
Chen
C
Yu
Richard M
RM
Shiffrin
D000223:Adaptation, Psychological; D001245:Association Learning; D005793:Generalization, Psychological; D006801:Humans; D008960:Models, Psychological