Forward and backward prediction in learning and perception

Predictive processing frameworks have emphasized the role of forward prediction as a critical ingredient for learning and perceptual inference. We anticipate sensory events that are likely in the future on the basis of past and current sensory events. By comparing these forward predictions against incoming input, we can obtain an accurate estimate of the environment (i.e. perceive) and improve the predictions themselves (i.e. learn). Interestingly however, research in the field of statistical learning has taught us that backward predictive relationships - reflecting the probability of past events given present events - are learnt equally well. This questions the privileged status of forward-looking mechanisms. Here we discuss commonalities and differences between implications for learning and perception. We conclude that while forward and backward predictive relationships both shape learning, we retrieve future, but not past, predicted states during perception.