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- W2950381710 abstract "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." @default.
- W2950381710 created "2019-06-27" @default.
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- W2950381710 date "2017-09-20" @default.
- W2950381710 modified "2023-10-18" @default.
- W2950381710 title "A Dynamic Bayesian Observer Model Reveals Origins of Bias in Visual Path Integration" @default.
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- W2950381710 doi "https://doi.org/10.1101/191817" @default.
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