Visual navigation is a task that involves processing two-dimensional light patterns on the retinas to obtain knowledge of how to move through a three-dimensional environment. Therefore, modifying the basic characteristics of the two-dimensional information provided to navigators should have important and informative effects on how they navigate. Despite this, few basic research studies have examined the effects of systematically modifying the available levels of spatial visual detail on navigation performance. In this study, we tested the effects of a range of visual blur levels—approximately equivalent to various degrees of low-pass spatial frequency filtering—on participants' visually guided route-learning performance using desktop virtual renderings of the Hebb–Williams mazes. Our findings show that the function of blur and time to finish the mazes follows a sigmoidal pattern, with the inflection point around +2 D of experienced defocus. This suggests that visually guided route learning is fairly robust to blur, with the threshold level being just above the limit for legal blindness. These findings have implications for models of route learning, as well as for practical situations in which humans must navigate under conditions of blur.
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