Abstract

Visual statistical learning of shape sequences was examined in the context of occluded object trajectories. In a learning phase, participants viewed a sequence of moving shapes whose trajectories and speed profiles elicited either a bouncing or a streaming percept: the sequences consisted of a shape moving toward and then passing behind an occluder, after which two different shapes emerged from behind the occluder. At issue was whether statistical learning linked both object transitions equally, or whether the percept of either bouncing or streaming constrained the association between pre- and postocclusion objects. In familiarity judgments following the learning, participants reliably selected the shape pair that conformed to the bouncing or streaming bias that was present during the learning phase. A follow-up experiment demonstrated that differential eye movements could not account for this finding. These results suggest that sequential statistical learning is constrained by the spatiotemporal perceptual biases that bind two shapes moving through occlusion, and that this constraint thus reduces the computational complexity of visual statistical learning. Human sensory systems handle enormous amounts of complex information during every waking moment. An increasing number of studies supports the idea that the processing of this information is implemented by an unsupervised form of continuous observational learning, which organizes both old and new sensory information into maximally efficient codes (Fiser & Aslin, 2005). This statistical learning mechanism has been demonstrated using speech streams in adults (Saffran, Newport, & Aslin,

Citations