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. (shrink)

Understanding how people comprehend visual narratives (including picture stories, comics, and film) requires the combination of traditionally separate theories that span the initial sensory and perceptual processing of complex visual scenes, the perception of events over time, and comprehension of narratives. Existing piecemeal approaches fail to capture the interplay between these levels of processing. Here, we propose the Scene Perception & Event Comprehension Theory (SPECT), as applied to visual narratives, which distinguishes between front-end and back-end cognitive processes. Front-end processes occur (...) during single eye fixations and are comprised of attentional selection and information extraction. Back-end processes occur across multiple fixations and support the construction of event models, which reflect understanding of what is happening now in a narrative (stored in working memory) and over the course of the entire narrative (stored in long-term episodic memory). We describe relationships between front- and back-end processes, and medium-specific differences that likely produce variation in front-end and back-end processes across media (e.g., picture stories vs. film). We describe several novel research questions derived from SPECT that we have explored. By addressing these questions, we provide greater insight into how attention, information extraction, and event model processes are dynamically coordinated to perceive and understand complex naturalistic visual events in narratives and the real world. (shrink)

Event segmentation theory posits that people segment continuous experience into discrete events and that event boundaries occur when there are large transient increases in prediction error. Here, we set out to test this theory in the context of story listening, by using a deep learning language model (GPT‐2) to compute the predicted probability distribution of the next word, at each point in the story. For three stories, we used the probability distributions generated by GPT‐2 to compute the time series of (...) prediction error. We also asked participants to listen to these stories while marking event boundaries. We used regression models to relate the GPT‐2 measures to the human segmentation data. We found that event boundaries are associated with transient increases in Bayesian surprise but not with a simpler measure of prediction error (surprisal) that tracks, for each word in the story, how strongly that word was predicted at the previous time point. These results support the hypothesis that prediction error serves as a control mechanism governing event segmentation and point to important differences between operational definitions of prediction error. (shrink)

Research has shown that both adults and children organize familiar activity into discrete units with consistent boundaries, despite the dynamic, continuous nature of everyday experiences. However, less is known about how observers segment unfamiliar event sequences. In the current study, we took advantage of the novelty that is inherent in modern dance. Modern dance features natural human motion but does not contain canonical goals—therefore, observers cannot recruit prior goal‐related knowledge to segment it. Our main aims were to identify whether observers (...) segment modern dance into the steps intended by the dancers, and what types of cues contribute to segmentation under these circumstances. Experiment 1 used a classic event segmentation task and found that adults were able to consistently identify only a few of the dancers’ intended steps. Experiment 2 tested adults in an offline labeling task. Results showed that steps which could more easily be labeled offline in Experiment 2 were more likely to be segmented online in Experiment 1. (shrink)