One problem language learners face is extracting word meanings from scenes with many possible referents. Despite the ambiguity of individual situations, a large body of empirical work shows that people are able to learn cross-situationally when a word occurs in different situations. Many computational models of cross-situational word learning have been proposed, yet there is little consensus on the main mechanisms supporting learning, in part due to the profusion of disparate studies and models, and lack of systematic model comparisons across a wide range of studies. This study compares the performance of several extant models on a dataset of 44 experimental conditions and a total of 1,696 participants. Using cross-validation, we fit multiple models representing theories of both associative learning and hypothesis-testing theories of word learning, find two best-fitting models, and discuss issues of model and mechanism identifiability. Finally, we test the models' ability to generalize to additional experiments, including developmental data.

In his famous address “You can’t play 20 questions with nature and win”, Newell (1973) predicted that experimental psychology’s focus on falsifying theories of individual phenomena (e.g., subitizing, directed forgetting, etc.) would not lead to a unifying theory subsuming several (let alone all) of the phenomena. Instead of seeking to detect effects, and binary searching our way to the explananda of these phenomena, Newell argued psychologists should build predictive theories of behavior by modeling 1) the structure of the task environment (i.e., context), 2) the subjects’ own goals, and 3) the invariant structure of the subjects’ processing mechanisms. Newell concludes that psychology will only make real progress in understanding the mind by 1) targeting tasks that are complex enough to cover the space of naturalistic behavior, 2) developing models that can competently perform the task, and 3) generalizing such models to perform multiple complex tasks. For 1), Newell chose to study chess; we believe he would find video games an even more compelling paradigm (see Gobet, 2017).

One problem language learners face is extracting word meanings from scenes with many possible referents. Despite the ambiguity of individual situations, a large body of empirical work shows that people are able to learn cross-situationally when a word occurs in different situations. Many computational models of cross-situational word learning have been proposed, yet there is little consensus on the main mechanisms supporting learning, in part due to the profusion of disparate studies and models, and lack of systematic model comparisons across a wide range of studies. This study compares the performance of several extant models on a dataset of 44 experimental conditions and a total of 1,696 participants. Using cross-validation, we fit multiple models representing theories of both associative learning and hypothesis-testing theories of word learning, find two best-fitting models, and discuss issues of model and mechanism identifiability. Finally, we test the models' ability to generalize to additional experiments, including develop- mental data.