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

ABSTRACT: 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

SUBMITTER: Lakshminarasimhan KJ 

PROVIDER: S-EPMC6190923 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature