Episodic Memory with Interactive 3D Sequential Graph

Abstract

Episodic memory can be viewed as a learning process, not from existing knowledge, but from massive streams of news and episodic events. Sequences of episodic events can be used to predict future events. In this study, we assume that long-term memory can be simulated with a spatial and temporal database. We explore the 3D sequential graphs that offer a selection of methods to visualize episodic memory in the 3D space, a network of sequences of values, or a statistical summary of information about groups or subsets such as frequencies, ranges, and distributions. The graph can be accessed through a tablet, laptop, and AR/VR headsets. Users can navigate the graph with hand gestures, a game controller, or a mouse. The semantic graph is also connected to multimedia content such as video footage and spatial soundtracks because our episodic memory is multimedia. Finally, the applications of episodic memories are presented, including disastrous scenarios of laparoscopic cholecystectomy and malware distribution networks.

Keywords: Episodic Memory, Human-Computer Interaction, Interactive 3D Sequential Graph, Augmented Rerality, Virtual Reality, Visualization, Knowledge Engineering, Artificial Intelligence

DOI: 10.54941/ahfe1004643