A Dynamic Bayesian Observer Model Reveals Origins of Bias in Visual Path Integration

Abstract

Path integration is a navigation strategy by which animals track their position by integrating their self-motion velocity over time. To identify the computational origins of bias in visual path integration, we asked human subjects to navigate in a virtual environment using optic flow, and found that they generally travelled beyond the goal location. Such a behaviour could stem from leaky integration of unbiased self-motion velocity estimates, or from a prior expectation favouring slower speeds that causes underestimation of velocity. We tested both alternatives using a probabilistic framework that maximizes expected reward, and found that subjects biases were better explained by a slow-speed prior than imperfect integration. When subjects integrate paths over long periods, this framework intriguingly predicts a distance-dependent bias reversal due to build-up of uncertainty, which we also confirmed experimentally. These results suggest that visual path integration performance is limited largely by biases in processing optic flow rather than by suboptimal signal integration.