Expectations are known to greatly affect our experience of the world. A growing theory in computational neuroscience is that perception can be successfully described using Bayesian inference models and that the brain is 'Bayes-optimal' under some constraints. In this context, expectations are particularly interesting, because they can be viewed as prior beliefs in the statistical inference process. A number of questions remain unsolved, however, for example: How fast do priors change over time? Are there limits in the complexity of the priors that can be learned? How do an individual’s priors compare to the true scene statistics? Can we unlearn priors that are thought to correspond to natural scene statistics? Where and what are the neural substrate of priors? Focusing on the perception of visual motion, we here review recent studies from our laboratories and others addressing these issues. We discuss how these data on motion perception fit within the broader literature on perceptual Bayesian priors, perceptual expectations, and statistical and perceptual learning and review the possible neural basis of priors.

Human perceptual learning is classically thought to be highly specific to trained stimuli9s retinal location. Together with evidence that specific learning effects can result in corresponding changes in early visual cortex, researchers have theorized that specificity implies regionalization of learning in the brain. However, other research suggests that specificity can arise from learning readout in decision areas or through top-down processes. Notably, recent research using a novel double-training paradigm reveals dramatic generalization of perceptual learning to untrained locations when multiple stimuli are trained. These data provoked significant controversy in the field and challenged extant models of perceptual learning. To resolve this controversy, we investigated mechanisms that account for retinotopic specificity in perceptual learning. We replicated findings of transfer after double training; however, we show that prolonged training at threshold, which leads to a greater number of difficult trials during training, preserves location specificity when double training occurred at the same location or sequentially at different locations. Likewise, we find that prolonged training at threshold determines the degree of transfer in single training of a peripheral orientation discrimination task. Together, these data show that retinotopic specificity depends highly upon particularities of the training procedure. We suggest that perceptual learning can arise from decision rules, attention learning, or representational changes, and small differences in the training approach can emphasize some of these over the others.

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference that is, how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: (i) how proposed theories differ in accounts of ASD vs. schizophrenia and (ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals’ likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations.