Tactile perception as probabilistic inference

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Humans take for granted our ability to perceive the external world, but how does the nervous system accomplish this remarkable feat? The means by which our sensory organs convert physical stimuli into neuronal responses are relatively well understood, as are the stimulus-evoked firing patterns of sensory neurons in the brain. Much less understood is the mystery of perception: following sensory activation, how does the brain reconstruct an image of the physical world? Our research will address this important neuroscientific question with specific reference to the sense of touch. The response of a sensory neuron in the brain to a repeated, identical stimulus is highly variable. Perception is thus inherently uncertain, and involves the challenging "inverse problem" of inferring a cause (the physical stimulus) from its variable effects (patterns of neural activity). This challenging problem is optimally approached via Bayesian probabilistic inference. The ideal Bayesian brain evaluates uncertain sensory data in light of prior experience. Specifically, it considers two probabilities: 1) the probability of the observed neural activity, given each stimulus that could have caused it (the "likelihood"), and 2) the probability of occurrence of each stimulus, based on previous experience (the "prior"). These two elements are combined to calculate the final probability of each stimulus (the "posterior"). Bayesian models typically assume that the stimulus with highest final probability is presented to consciousness as the percept. Bayesian models have been applied frequently in studies of visual perception but only rarely in studies of tactile perception. In a series of studies, we will compare human tactile perception to the predictions from Bayesian models. Our studies will probe the effects of expectation (prior experience), adaptation (the reduction in neural response to a sustained stimulus), multi-finger integration (perception of objects felt simultaneously with several fingers), and haptic exploration (active touch for object recognition) on the ability of humans to perceive through touch. The findings will indicate whether or not tactile perception is optimal in the Bayesian sense. This work will have important theoretical and practical implications. The work will deepen basic scientific understanding of perceptual processing and may also inform the construction of next-generation tactile graphics displays, haptic virtual reality systems, and robotics. In order to design a successful virtual reality system, scientists and engineers must first understand how the end-user processes sensory input to arrive at a perceptual inference. Similarly, in order to create human-like robotics systems, scientists and engineers must first understand human perceptual processing. Our research will enable such understanding, thereby benefitting Canadian science and engineering.