Competition and Integration of Visual and Goal Vector Signals for Spatial Navigation

Integrating different sources of information is essential to successful spatial navigation. For instance, animals must often rely on a combination of visual impressions, self-motion, olfaction, and other signals to navigate to a goal. This is especially important when navigating in uncertain environments, where switching from one source of information to another or integrating multiple sources of information may be required to make navigation decisions. We propose a computational model of the interaction of visual and goal-vector signals based on reinforcement learning and use it to study behavior and spatial representations. Our model demonstrates that the ability to navigate using each information source independently, in addition to integrating them, is crucial to successfully navigating in uncertain environments. Counterintuitively, our model also shows that when one of the signals is removed, navigation may be improved if the remaining signal is reliable and sufficient to navigate, however, this improvement comes at the expense of robustness.