A computational model of context-dependent encodings during category learning

Abstract

Although current exemplar models of category learning are flexible and can capture how different features are emphasized for different categories, they still lack in the flexibility to adapt to local pressures in category learning, such as the effect of different sequences of study. In this paper we introduce a new model of category learning, the Sequential Attention Theory Model (SAT-M), in which the encoding of each presented item is influenced not only by its category assignment (global context) as in other exemplar models, but also by how its properties relate to the properties of temporally neighboring items (local context). We demonstrate that SAT-M is able to capture the effect of local context and predict not only learning but also learners’ attentional patterns during learning.