A Computational Model of Event Learning and Segmentation: Event Granularity, Sensory Reliability and Expectation

Event is a fuzzy term that refers to a closed spatio-temporal unit. The aim of the project is to develop a computational model that can learn event models and use learned event models to segment ongoing activities in varying granularities and compare its performance with human subjects. By doing so, we aim to clarify the effect of the reliability of sensory information and expectation on event segmentation performance by several experiments through our computational model and to develop a computational model that is capable of learning, segmenting and representing new events while being robust to noise. In addition to comparing human event segmentation performance with that of the computational model, we plan to design a new validation method to increase the reliability of assumptions of the computational model in terms of validating the psychological theory and assessing how well the computational model performs in terms of capturing human event representations. Results of our experiments and our computational model will be used to validate predictions of a psychological theory, namely Event Segmentation Theory, and to develop robotics models that are capable of simulating higher-level cognitive processes such as action segmentation in different granularities and formation of concepts representing events.