How Does the Mind Render Streaming Experience as Events?

Abstract

Events-the experiences we think we are having and recall having had-are constructed; they are not what actually occurs. What occurs is ongoing dynamic, multidimensional, sensory flow, which is somehow transformed via psychological processes into structured, describable, memorable units of experience. But what is the nature of the redescription processes that fluently render dynamic sensory streams as event representations? How do such processes cope with the ubiquitous novelty and variability that characterize sensory experience? How are event-rendering skills acquired and how do event representations change with development? This review considers emerging answers to these questions, beginning with evidence that an implicit tendency to monitor predictability structure via statistical learning is key to event rendering. That is, one way that the experience of bounded events (e.g., actions within behavior, words within speech) arises is with the detection of "troughs" in sensory predictability. Interestingly, such troughs in predictability are often predictable; these regions of predictable-unpredictability provide articulation points to demarcate one event from another in representations derived from the actual streaming information. In our information-optimization account, a fluent event-processor predicts such troughs and selectively attends to them-while suppressing attention to other regions-as sensory streams unfold. In this way, usage of attentional resources is optimized for efficient sampling of the most relevant, information-rich portions of the unfolding flow of sensation. Such findings point to the development of event-processing fluency-whether in action, language, or other domains-depending crucially on rapid and continual cognitive reorganization. As knowledge of predictability grows, attention is adaptively redeployed. Accordingly, event experiences undergo continuous alteration.

Keywords: Event processing; Event segmentation; Information optimization; Learning; Predictive learning.