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. / Dissertation / Doctor of Philosophy (PhD) / The process of categorization is an essential part of our daily life as we encounter various things in the world. Here we explore a model that attempts to explain this process. This model is derived using Bayesian inference and was applied to human behavioural data in a categorization task. We found that the model accounted for most of the performance of our participants but consistently outperformed them. We conducted simulations to explore and demonstrate that this difference is primarily due to the presence of sensory noise in participants. Once we accounted for this noise, we found that our model predicted human performance even more accurately. The work in this thesis demonstrates that a Bayesian Categorization Model which accounts for sensory noise is a good fit and predictor for human performance on categorization tasks.

Sensory adaptation is an important aspect of perception. A seemingly non-beneficial consequence of adaptation is that it produces perceptual illusions. For instance, following focal adaptation, the perceived separation between stimuli straddling the adapted attribute or region is often exaggerated. This type of illusion, known as perceptual repulsion, is both a consequence of and a clue to the brain’s coding strategies and how they are influenced by recent sensory events. Adaptation-induced perceptual repulsion has been well documented in vision (e.g. the tilt aftereffect) and to a lesser extent in audition, but rarely studied in touch. The present thesis investigated the effects of adaptation on tactile spatial perception using a combination of human psychophysics and computational modeling. In a two-interval forced choice task, participants compared the perceived separation between two point-stimuli applied on the forearms successively. The point of subjective equality was extracted as a measure of perceived two-point distance. We showed that tactile spatial perception is subject to an adaptation-induced repulsion illusion: vibrotactile adaptation focally reduced tactile sensitivity and significantly increased the perceived distance between points straddling the adapted skin site (Chapter 2). This repulsion illusion, however, was not observed when the intervening skin was desensitized with topical anesthesia instead of vibrotactile adaptation, suggesting that peripheral desensitization alone is insufficient to induce the illusion (Chapter 3). With Bayesian perceptual modeling, we showed that the illusion was consistent with the hypothesis that the brain decodes tactile spatial input without awareness of the adaptation state in the nervous system (Chapter 4). Together, the empirical and theoretical work furthers the understanding of dynamic tactile spatial coding as the somatosensory system adapts to the sensory environment. Its main findings are consistent with the adaptation- induced repulsion illusions reported in vision and audition, suggesting that perception in different sensory modalities shares common processing features and computational principles. / Thesis / Doctor of Philosophy (PhD) / Sensory adaptation can shape how we perceive the world. In this thesis, we showed that the perception of space in touch is pliable and subject to the influence of adaptation. Psychophysical testing in human participants showed that vibratory adaptation induced an illusion that expanded the perceived distance between stimuli on the skin. This illusion provides clues into how information about space in touch is normally processed and interpreted by the brain. In addition, we developed a computational model that used a powerful statistical framework – Bayesian inference – to probe touch on a theoretical basis. To the best of our knowledge, the present thesis provides the first combined psychophysical and computational study on the effects of adaptation on tactile spatial perception. Our findings suggest that touch shares some common information processing principles with vision and hearing, and adaptation plays a functionally similar role in mediating this process across the senses.