Learning what to see in a changing world

• Main Authors: Katharina eSchmack, Veith eWeilnhammer, Jakob eHeinzle, Klaas Enno Stephan, Philipp eSterzer
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2016-05-01
• Series: Frontiers in Human Neuroscience
• Subjects: Association Learning; Visual Perception; bistable perception; sensory memory; Bayesian Brain; Hierarchical Gaussian Filter
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fnhum.2016.00263/full

Visual perception is strongly shaped by expectations, but it is poorly understood how such perceptual expectations are learned in our dynamic sensory environment. Here, we applied a Bayesian framework to investigate whether perceptual expectations are continuously updated from different aspects of ongoing experience. In two experiments, human observers performed an associative learning task in which rapidly changing expectations about the appearance of ambiguous stimuli were induced. We found that perception of ambiguous stimuli was biased by both learned associations and previous perceptual outcomes. Computational modelling revealed that perception was best explained by amodel that continuously updated priors from associative learning and perceptual history and combined these priors with the current sensory information in a probabilistic manner. Our findings suggest that the construction of visual perception is a highly dynamic process that incorporates rapidly changing expectations from different sources in a manner consistent with Bayesian learning and inference.