A Frame-Theoretic Model of Bayesian Category Learning
Samuel D. Taylor, Peter R. Sutton · Language, cognition and mind · 2021
Abstract Bayesian models of category learning typically assume that the most probable categories are those that group input stimuli together around a maximally optimal number of shared features. One potential weakness of such feature list approaches, however, is that it is unclear how to weight observed features to be more or less diagnostic for a given category. In this theoretically oriented paper, we develop a frame-theoretic model of Bayesian category learning that weights the diagnosticity of observed attribute values in terms of their position within the structure of a frame (formalised as distance from the frame’s central node). We argue that there are good grounds to further develop and empirically test frame-based learning models, because they have theoretical advantages over unweighted feature list models, and because frame structures provide a principled means of assigning weights to attribute values without appealing to supervised training data.