Studying Human Spatial Cognition in Large-Scale, Virtual Environments: Learning, Memory, and Representational Flexibility

Abstract

The representation of space in humans is influenced by a variety of factors during learning including the size, geometry, and complexity of the environment as well as how we interact with it. While the networks that underlie human spatial cognition are believed to be similar or homologous to other mammalian species, we may be the only species that uses figural representations of space (i.e., maps) to improve navigational efficacy for prospective goals. Behavioral and neurophysiological evidence have suggested a framework whereby self-referenced egocentric representations and world-referenced allocentric representations develop independently but interact and influence one another such that information from one of these reference frames can be translated into the other and vice versa. This framework provides the cognitive flexibility required for navigating large, complex spaces such as cities. The study of these large-scale environments has benefitted greatly from recent advances in video game technology, including immersive virtual reality (VR), but the time and effort to integrate these tools can be detrimental to the empirical process. In this dissertation, I provide a brief overview of the theories, methods, and results that motivated this work (Chapter 1), introduce tools I have developed to aid researchers in designing and implementing VR experiments (Chapter 2), and report research findings in support of a view that egocentric and allocentric affordances during learning and demands at retrieval result in dynamic and flexible spatial representations (Chapters 3 & 4). Chapter 5 summarizes the theoretical linkage of my doctoral portfolio and provides avenues for future study.