Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models

. 2020 May ; 16 (5) : e1007489. [epub] 20200507

Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection

Typ dokumentu časopisecké články, Research Support, U.S. Gov't, Non-P.H.S.

Perzistentní odkaz   https://www.medvik.cz/link/pmid32379824
Odkazy

PubMed 32379824
PubMed Central PMC7244182
DOI 10.1371/journal.pcbi.1007489
PII: PCOMPBIOL-D-19-01761
Knihovny.cz E-zdroje

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 respective contributions. We developed a paradigm involving walking in an omnidirectional treadmill in which participants were guided on two sides of a triangle and then found their back way to origin. In Experiment 1, we tested a range of different triangle types while keeping the distance of the unguided side 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 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 configural-based 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 configural 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 sides 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.

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