Linguistic Information in Auditory Dynamic Events Contributes to the Detection of Fine, Not Coarse Event Boundaries

Aspectual Processing Shifts Visual Event Apprehension

What is the relationship between language and event cognition? Past work has suggested that linguistic/aspectual distinctions encoding the internal temporal profile of events map onto nonlinguistic event representations. Here, we use a novel visual detection task to directly test the hypothesis that processing telic versus atelic sentences (e.g., "Ebony folded a napkin in 10 seconds" vs. "Ebony did some folding for 10 seconds") can influence whether the very same visual event is processed as containing distinct temporal stages including a well-defined endpoint or lacking such structure, respectively. In two experiments, we show that processing (a)telicity in language shifts how people later construe the temporal structure of identical visual stimuli. We conclude that event construals are malleable representations that can align with the linguistic framing of events.

Bayesian Surprise Predicts Human Event Segmentation in Story Listening

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.

Measuring event segmentation: An investigation into the stability of event boundary agreement across groups

People spontaneously divide everyday experience into smaller units (event segmentation). To measure event segmentation, studies typically ask participants to explicitly mark the boundaries between events as they watch a movie (segmentation task). Their data may then be used to infer how others are likely to segment the same movie. However, significant variability in performance across individuals could undermine the ability to generalize across groups, especially as more research moves online. To address this concern, we used several widely employed and novel measures to quantify segmentation agreement across different sized groups (n = 2-32) using data collected on different platforms and movie types (in-lab & commercial film vs. online & everyday activities). All measures captured nonrandom and video-specific boundaries, but with notable between-sample variability. Samples of 6-18 participants were required to reliably detect video-driven segmentation behavior within a single sample. As sample size increased, agreement values improved and eventually stabilized at comparable sample sizes for in-lab & commercial film data and online & everyday activities data. Stabilization occurred at smaller sample sizes when measures reflected (1) agreement between two groups versus agreement between an individual and group, and (2) boundary identification between small (fine-grained) rather than large (coarse-grained) events. These analyses inform the tailoring of sample sizes based on the comparison of interest, materials, and data collection platform. In addition to demonstrating the reliability of online and in-lab segmentation performance at moderate sample sizes, this study supports the use of segmentation data to infer when events are likely to be segmented.

Cross-codal integration of bridging-event information in narrative understanding

Visual narratives communicate event sequences by using different code systems such as pictures and texts. Thus, comprehenders must integrate information from different codalities. This study addressed such cross-codal integration processes by investigating how the codality of bridging-event information (i.e., pictures, text) affects the understanding of visual narrative events. In Experiment 1, bridging-event information was either present (as picture or text) or absent (i.e., not shown). The viewing times for the subsequent picture depicting the end state of the action were comparable within the absent and the text conditions. Further, the viewing times for the end-state picture were significantly longer in the text condition as compared to the pictorial condition. In Experiment 2, we tested whether replacing bridging-event information with a blank panel increases viewing times in a way similar to the text condition. Bridging event information was either present (as picture) or absent (not shown vs. blank panel). The results replicated Experiment 1. Additionally, the viewing times for the end-state pictures were longest in the blank condition. In Experiment 3, we investigated the costs related to integrating information from different codalities by directly comparing the text and picture conditions with the blank condition. The results showed that the distortion caused by the blank panel is larger than the distortion caused by cross-codal integration processes. Summarizing, we conclude that cross-codal information processing during narrative comprehension is possible but associated with additional mental effort. We discuss the results with regard to theories of narrative understanding.