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We incorporate social reasoning about groups of informants into a model of word learning, and show that the model accounts for infant looking behavior in tasks of both word learning and recognition. Simulation 1 models an experiment where 16-month-old infants saw familiar objects labeled either correctly or incorrectly, by either adults or audio talkers. Simulation 2 reinterprets puzzling data from the Switch task, an audiovisual habituation procedure wherein infants are tested on familiarized associations between novel objects and labels. Eight-month-olds outperform 14-month-olds on the Switch task when required to distinguish labels that are minimal pairs (e.g., “buk” and “puk”), but 14-month-olds' performance is improved by habituation stimuli featuring multiple talkers. Our modeling results support the hypothesis that beliefs about knowledgeability and group membership guide infant looking behavior in both tasks. These results show that social and linguistic development interact in non-trivial ways, and that social categorization findings in developmental psychology could have substantial implications for understanding linguistic development in realistic settings where talkers vary according to observable features correlated with social groupings, including linguistic, ethnic, and gendered groups. 

We introduce a method for measuring the correspondence between low-level speech features and human perception, using a cognitive model of speech perception implemented directly on speech recordings. We evaluate two speaker normalization techniques using this method and find that in both cases, speech features that are normalized across speakers predict human data better than unnormalized speech features, consistent with previous research. Results further reveal differences across normalization methods in how well each predicts human data. This work provides a new framework for evaluating low-level representations of speech on their match to human perception, and lays the groundwork for creating more ecologically valid models of speech perception. 

Listeners track distributions of speech sounds along perceptual dimensions. We introduce a method for evaluating hypotheses about what those dimensions are, using a cognitive model whose prior distribution is estimated directly from speech recordings. We use this method to evaluate two speaker normalization algorithms against human data. Simulations show that representations that are normalized across speakers predict human discrimination data better than unnormalized representations, consistent with previous research. Results further reveal differences across normalization methods in how well each predicts human data. This work provides a framework for evaluating hypothesized representations of speech and lays the groundwork for testing models of speech perception on natural speech recordings from ecologically valid settings. 

Listeners draw on their knowledge of phonetic categories when identifying speech sounds, extracting meaningful structural features from auditory cues. We use a Bayesian model to investigate the extent to which their perceptions of linguistic content incorporate their full knowledge of the phonetic category structure, or only certain aspects of this knowledge. Simulations show that listeners are best modeled as attending primarily to the most salient phonetic feature of a category when interpreting a cue, possibly attending to other features only in cases of high ambiguity. These results support the conclusion that listeners ignore potentially informative correlations in favor of efficient communication.