Despite the importance of navigational skill, people’s ability to find their way around and the approaches they take vary widely. Yet we do not know why this variation exists or how different strategies are used during everyday navigation. In robotics, self-driving cars, and other autonomous systems, it has become increasingly clear that a one-size-fits-all approach is not viable for all environments and user needs. Similarly, producing autonomous systems with different navigational strengths could improve the capacity of autonomous systems as a whole, such as teams of exploring or search-and-rescue robots. This project brings together researchers from neuroscience, cognitive psychology, computer science, and robotics to study variability in navigation abilities and strategies in complex environments. The researchers combine behavioral, neuroscience, and computational approaches. Pinpointing the neural and behavioral markers that underlie individual differences will lead to customized solutions to navigational challenges and optimize the performance of autonomous systems for differing environmental conditions. The outcomes of this research will have the following specific benefits to society and scientific discovery: 1) advancing theoretical understanding of the processes involved in spatial navigation, 2) understanding the advantages of variation in both humans and robots, impacting how people approach both fields, 3) implications for improvements in self-driving cars, GPS wayfinding devices, and transportation signage, and 4) broader dissemination of virtual reality (VR) technology.
The overarching cross-disciplinary aims of this study are to 1) test whether human spatial navigation is a singular competence or whether multiple abilities contribute, 2) establish the neural markers of human navigational abilities through multi-modal imaging, and 3) implement and test different navigational abilities in robots in real-world situations. This study will be the largest to date (n = 270) to study human navigation abilities, using both structural equation modeling and multivariate analysis of imaging data to deeply address this question. Furthermore, the project will broaden the scope of abilities to relate navigation skills to working memory, learning, personality, and other factors. Implementing navigational strategies and abilities in robots provides a controlled test of their tradeoffs. By manipulating the planning and mapping strategies of robots, the researchers can isolate particular abilities and their contributions to navigation, testing both navigational theory and practical advantages for autonomous systems. The interdisciplinary approach of this project harnesses the strengths of cognitive science, robotics, and neuroscience to test the fundamental nature of human individual variability.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
