干旱区生态环境知识资源中心(Arid): Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models

Arid

DOI	10.1371/journal.pcbi.1007489
	Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models
	Harootonian, Sevan K.1,2,6; Wilson, Robert C.2,3,4; Hejtmanek, Lukas 1,5; Ziskin, Eli M.1,2; Ekstrom, Arne D.1,2,4
通讯作者	Ekstrom, Arne D.
来源期刊	PLOS COMPUTATIONAL BIOLOGY
ISSN	1553-734X
EISSN	1553-7358
出版年	2020
卷号	16 期号:5
英文摘要	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. Author summary How do we remember where we have been? One important mechanism for doing so is called path integration, which refers to the computation of one's 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 sides of a triangle without vision and then completing the third side based on their estimate of the homing vector. We found no effect of triangle shape on systematic errors, while the systematic errors scaled with path length 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.
类型	Article
语种	英语
国家	USA ; Czech Republic
开放获取类型	Green Submitted, gold, Green Published
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000538053200013
WOS关键词	COGNITIVE MAPS ; DESERT ANTS ; NAVIGATION ; KNOWLEDGE ; LOCOMOTION ; DIRECTION ; RANGE ; REPRESENTATION ; BEHAVIOR ; INPUTS
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
来源机构	University of Arizona ; University of California, Davis
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319023
作者单位	1.Univ Calif Davis, Ctr Neurosci, Davis, CA 95616 USA; 2.Univ Arizona, Dept Psychol, Tucson, AZ 85721 USA; 3.Univ Arizona, Cognit Sci Program, Tucson, AZ USA; 4.Univ Arizona, Evelyn McKnight Brain Inst, Tucson, AZ 85721 USA; 5.Charles Univ Prague, Fac Med 3, Prague, Czech Republic; 6.Princeton Univ, Dept Psychol, Princeton, NJ 08544 USA
推荐引用方式 GB/T 7714	Harootonian, Sevan K.,Wilson, Robert C.,Hejtmanek, Lukas,et al. Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models[J]. University of Arizona, University of California, Davis,2020,16(5).
APA	Harootonian, Sevan K.,Wilson, Robert C.,Hejtmanek, Lukas,Ziskin, Eli M.,&Ekstrom, Arne D..(2020).Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models.PLOS COMPUTATIONAL BIOLOGY,16(5).
MLA	Harootonian, Sevan K.,et al."Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models".PLOS COMPUTATIONAL BIOLOGY 16.5(2020).