No abstract

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 these units from the continuous experience.Aiming to shed light on event segmentation ability, event segmentation theory points out that people predict ongoing activities and observe prediction error signals in order 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 the principles of event segmentation theory and predictive processing, we introduced a semi-mechanistic model of event segmentation, learning, and representation. This model consists of feed-forward neural networks that predict the sensory signal in the next time-step in order to represent different events, and a cognitive model that regulates these neural networks on the basis of their prediction errors. In order to verify the ability of our model in segmenting experience into spatio-temporal units, learning them during passive observation, and representing them in its internal representational space, we prepared a video that depicts natural human behaviors represented by point-light displays. We compared event segmentation behaviors of human 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 internal representation space of participants by a similarity-based technique, we showed that our model formed a similar internal representation space with those of participants. Our results suggests that our model that tracks the prediction error signals can produce human-like event segmentation decisions 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.

No abstract

Animals exploit time to survive in the world. Temporal information is required for higher-level cognitive abilities such as planning, decision making, communication and effective cooperation. Since time is an inseparable part of cognition, there is a growing interest in the artificial intelligence approach to subjective time, which has a possibility of advancing the field. The current survey study aims to provide researchers with an interdisciplinary perspective on time perception. Firstly, we introduce a brief background from the psychology and neuroscience literature, covering the characteristics and models of time perception and the related abilities. Secondly, we summarize the emergent computational and robotic models of time perception. A general overview to the literature reveals that a substantial amount of timing models are based on a dedicated time processing like the emergence of a clock-like mechanism from the neural network dynamics and reveal a relationship between the embodiment and time perception. We also notice that most models of timing are developed for either sensory timing (i.e. the ability of assessment of an interval) or motor timing (i.e. ability to reproduce an interval). The number of timing models capable of retrospective timing, which is the ability to track time without paying attention, is insufficient. In this light, we discuss the possible research directions to promote interdisciplinary collaboration for time perception.