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People segment complex, ever-changing and continuous experience into basic,stable and discrete spatio-temporal experience units, called events. Eventsegmentation literature investigates the mechanisms that allow people toextract events. Event segmentation theory points out that people predictongoing activities and observe prediction error signals to find eventboundaries that keep events apart. In this study, we investigated the mechanismgiving rise to this ability by a computational model and accompanyingpsychological experiments. Inspired from event segmentation theory andpredictive processing, we introduced a self-supervised model of eventsegmentation. This model consists of neural networks that predict the sensorysignal in the next time-step to represent different events, and a cognitivemodel that regulates these networks on the basis of their prediction errors. Inorder to verify the ability of our model in segmenting events, learning themduring passive observation, and representing them in its internalrepresentational space, we prepared a video that depicts human behaviorsrepresented by point-light displays. We compared event segmentation behaviorsof participants and our model with this video in two hierarchical eventsegmentation levels. By using point-biserial correlation technique, wedemonstrated that event segmentation decisions of our model correlated with theresponses of participants. Moreover, by approximating representation space ofparticipants by a similarity-based technique, we showed that our model formed asimilar representation space with those of participants. The result suggeststhat our model that tracks the prediction error signals can produce human-likeevent boundaries and event representations. Finally, we discussed ourcontribution to the literature of event cognition and our understanding of howevent segmentation is implemented in the brain.

Predictive Event Segmentation and Representation with Neural Networks: A Self-Supervised Model Assessed by Psychological Experiments