Path integration in large-scale space and with novel geometries: Comparing Vector Addition and Encoding-Error Models

Abstract

Path integration is thought to rely on vestibular and proprioceptive cues yet most studies in humans involve primarily visual input, providing limited insight into their contributions. We developed a paradigm involving walking in an omnidirectional treadmill in which participants were guided on two legs of a triangle and then found their back way to origin. In Experiment 1, we tested a range of different triangle types while keeping distance relatively constant to determine the influence of spatial geometry. Participants overshot the angle they needed to turn and undershot the distance they needed to walk, with no consistent effect of triangle type. In Experiment 2, we manipulated distance while keeping angle relatively constant to determine how path integration operated over both shorter and longer distances. Participants underestimated the distance they needed to walk to the origin, with error increasing as a function of the walked distance. To attempt to account for our findings, we developed computational models involving vector addition, the second of which included terms for the influence of past trials on the current one. We compared against a previously developed model of human path integration, the Encoding Error model. We found that the vector addition models captured the tendency of participants to under-encode guided legs of the triangles and an influence of past trials on current trials. Together, our findings expand our understanding of body-based contributions to human path integration, further suggesting the value of vector addition models in understanding these important components of human navigation.\n\nAuthor SummaryHow do we remember where we have been? One important mechanism for doing so is called path integration, which refers to the ability to track ones position in space with only self-motion cues. By tracking the direction and distance we have walked, we can create a mental arrow from the current location to the origin, termed the homing vector. Previous studies have shown that the homing vector is subject to systematic distortions depending on previously experienced paths, yet what influences these patterns of errors, particularly in humans, remains uncertain. In this study, we compare two models of path integration based on participants walking two legs of a triangle without vision and then completing the third leg based on their estimate of the homing vector. We found no effect of triangle shape on systematic errors, while path length scaled the systematic errors logarithmically, similar to Weber-Fechner law. While we show that both models captured participants behavior, a model based on vector addition best captured the patterns of error in the homing vector. Our study therefore has important implications for how humans track their location, suggesting that vector-based models provide a reasonable and simple explanation for how we do so.