Abstract
Missing information is a challenge for sensory motor processing. Missing information is ubiquitous - portions of sensory data may be occluded due to conditions like scene clutter and camouflage; or missing at the present time - task demands may require anticipation of future states, such as when we negotiate a busy intersection. Rather than being immobilized by missing information, predictive processing fills in the gaps so we may continue to act in the world. While much of perceptual-motor research implicitly studies predictive processing, a specific set of predictive principles used by the brain has not been adequately formalized. I will draw upon our recent work on visual extrapolation, which requires observers to predict an object's location behind an occluder as well as its reemergence point. Through the results, I will demonstrate that these predictions are derived from model-based forward look ahead—current sensory data is applied to an internal model of the world. I will also show that predictions are subject to performance trade-offs, such that the choice of internal model may be a flexible one that appropriately weights the quality (i.e. uncertainty) of the sensory measurements and the quality (i.e. complexity) of the internal model. Finally, having established the role of internal models in prediction, I will conclude with a discussion about how prediction may be used as a tool in the experimental context to encourage general model learning, with evidence from our recent work on perceptual learning.