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

Path integration is a strategy by which animals track their position by integrating their self-motion velocity. 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 traveled beyond the goal location. Such a behavior could stem from leaky integration of unbiased self-motion velocity estimates or from a prior expectation favoring slower speeds that causes velocity underestimation. Testing both alternatives using a probabilistic framework that maximizes expected reward, we 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 buildup of uncertainty, which we also confirmed experimentally. These results suggest that visual path integration in noisy environments is limited largely by biases in processing optic flow rather than by leaky integration. Humans are typically biased while navigating by integrating their self-motion. Using virtual reality and probabilistic modeling, Lakshminarasimhan et al. demonstrate that systematic errors in visual path integration originate from the combined influence of a slow-velocity prior and growing position uncertainty.