Exploration of a Bayesian probabilistic model for categorization in the sense of touch

Abstract

Categorization is a complex decision-making process that requires observers to collect information about stimuli using their senses. While research on visual or auditory categorization is extensive, there has been little attention given to tactile categorization. Here we developed a paradigm for studying tactile categorization using 3D-printed objects. Furthermore, we derived a categorization model using Bayesian inference and tested its performance against human participants in our categorization task. This model accurately predicted participant performance in our task but consistently outperformed them, even after extending the learning period for our participants. Through theoretical exploration and simulations, we demonstrated that the presence of sensory measurement noise could account for this performance gap, which we determined was a present factor in participants undergoing our task through a follow-up experiment. Including measurement noise led to a better-fitting model that was able to match the performance of our participants much more closely. Overall, the work in this thesis provides evidence for the efficacy of a tactile categorization experimental paradigm, demonstrates that a Bayesian model is a good fit and predictor for human categorization performance, and underscores the importance of accounting for sensory measurement noise in categorization models.