Integration and Inference: Cross-Situational Word Learning Involves More than Simple Co-occurrences


Statistical word learning involves forming and aggregating associations between words and objects that co-occur across contexts (e.g., Vouloumanos & Werker, 2009; Smith & Yu, 2008; Yu & Smith, 2007). However, the mechanisms that support such learning are currently under debate, including the extent to which learners carry forward multiple ambiguous associations (e.g., Trueswell et al., 2013). The current study presented adults with a set of statistical word learning tasks designed to measure the statistical computations learners employ to build label-object mappings and to probe what information from past contexts is available to further process and integrate with new information. Results reveal that learners use the co-occurrence of label-object pairings to make inferences both about objects and labels currently present and those presented on previous trials. Further, the strength of learners’ memory for past contexts moderated their inferences, suggesting a role for a rich information structure in cross-situational word learning.

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