  People segment complex, ever-changing and continuous experience into basic,
stable and discrete spatio-temporal experience units, called events. Event
segmentation literature investigates the mechanisms that allow people to
extract events. Event segmentation theory points out that people predict
ongoing activities and observe prediction error signals to find event
boundaries that keep events apart. In this study, we investigated the mechanism
giving rise to this ability by a computational model and accompanying
psychological experiments. Inspired from event segmentation theory and
predictive processing, we introduced a self-supervised model of event
segmentation. This model consists of neural networks that predict the sensory
signal in the next time-step to represent different events, and a cognitive
model that regulates these networks on the basis of their prediction errors. In
order to verify the ability of our model in segmenting events, learning them
during passive observation, and representing them in its internal
representational space, we prepared a video that depicts human behaviors
represented by point-light displays. We compared event segmentation behaviors
of participants and our model with this video in two hierarchical event
segmentation levels. By using point-biserial correlation technique, we
demonstrated that event segmentation decisions of our model correlated with the
responses of participants. Moreover, by approximating representation space of
participants by a similarity-based technique, we showed that our model formed a
similar representation space with those of participants. The result suggests
that our model that tracks the prediction error signals can produce human-like
event boundaries and event representations. Finally, we discussed our
contribution to the literature of event cognition and our understanding of how
event segmentation is implemented in the brain.
