EXPLORATION OF A BAYESIAN MODEL OF TACTILE SPATIAL PERCEPTION

Abstract

The remarkable ability of the human brain to draw an accurate percept from imprecise sensory information is not well understood. Bayesian inference provides an optimal means for drawing perceptual conclusions from sensorineural activity. This approach has frequently been applied to visual and auditory studies but only rarely to studies of tactile perception. We explored whether a Bayesian observer model could replicate fundamental aspects of human tactile spatial perception. The model consisted of an encoder that simulated sensorineural responses with Poisson statistics followed by a decoder that interpreted the observed firing rates. We compared the performance of our Bayesian observer on a battery of tactile tasks to human participant data collected previously by our laboratory and others. The Bayesian observer replicated human performance trends on three spatial acuity tasks: classic two-point discrimination (C2PD), sequential two-point discrimination (S2PD), and two-point orientation discrimination (2POD). We confirmed the widely reported observation that C2PD is the least reliable method of assessing tactile acuity due presumably to the presence of non-spatial cues. Additionally, the Bayesian observer performed similarly to humans on raised letter and Braille character-recognition tasks. The Bayesian observer further replicated two illusions previously reported in humans: an adaptation-induced repulsion illusion and an orientation anisotropy illusion. Taken together, these results suggest that human tactile spatial perception may arise from a Bayesian-like decoder that is unaware of the precise characteristics of its inputs.