Learning names for novel objects has been shown to be impacted by the context in which they appear. Manipulations of context, therefore, provide a key pathway to explore these learning dynamics. Here we use a neural process model that instantiates the details of ‘context' to generate novel, counterintuitive predictions about how similarity in object properties influence learning. Specifically, we use a dynamic field model, WOLVES, to simulate and predict learning in a cross-situational word learning task in two conditions: one where the two objects presented on each learning trial are metrically similar in a property (‘NEAR') and another condition where the two objects are always dissimilar (‘FAR'). WOLVES predicts—counterintuitively—that participants should learn better in the ‘NEAR' condition (where objects are potentially confusable) than in ‘FAR' condition (where objects are distinctive). We then tested this prediction empirically, finding support for the novel prediction. This study shows the utility of process models which instantiate the details of ‘context' during learning and provides support for WOLVES. We know of no other theory of cross-situational word learning that captures these novel findings.

Recent empirical studies have affirmed the fundamental role of attention and memory processes in statistical word learning tasks. These processes interact in complex ways to guide spontaneous looking behaviors of learners as well as determine their overall learning performance. On the modelling side, studies have made it clear that computational models must provide process-based rather than only computational accounts of word learning, because these can connect to the empirically observed behaviors at a moment- to-moment timescale. Thus, here we present a neurally- grounded process model of word learning called WOLVES (Word-Object Learning Via Visual Exploration in Space) that integrates visual dynamics and word-object binding across multiple timescales. WOLVES integrates multiple established dynamic neural field models to allow fine-grained indexing of component processes driving the looking-learning loop. We report simulation results for three empirical cross-situational word learning experiments to validate the model.

The similarity between objects is judged in a wide variety of contexts from visual search to categorization to face recognition. There is a correspondingly rich history of similarity research and many known behavioral trends and models of similarity. Nevertheless, most similarity behaviors have been identified and tested only in a comparatively narrow set of unique contexts. This leaves open the question of the extent to which similarity judgments rely on common processes or resources and the specific nature of those processes if so. We tested three diverse yet well-established measures of object similarity using identical, psychometrically controlled stimuli and identical analyses across tasks. We found several consistent behavioral effects across tasks that provide clues as to the nature of task-general similarity processes and serve as diagnostic targets for computational models of similarity