Spatial inference without a cognitive map: the role of higher‐order path integration. (16th September 2020)

• Record Type: Journal Article
• Authors: Bouchekioua, Youcef; Blaisdell, Aaron P.; Kosaki, Yutaka; Tsutsui‐Kimura, Iku; Craddock, Paul; Mimura, Masaru; Watanabe, Shigeru
• Is Part Of: Biological reviews. Volume 96:Number 1(2021)
• Page Start: 52
• Page End: 65
• Publication Date: 2020-09-16
• Subjects: cognitive map -- path integration -- inference -- goal‐directed behaviour -- head‐direction vectors -- vector learning; Biology -- Periodicals; 570
• DOI: 10.1111/brv.12645
• Languages: English
• ISSNs: 1464-7931

Abstract

ABSTRACT: The cognitive map has been taken as the standard model for how agents infer the most efficient route to a goal location. Alternatively, path integration – maintaining a homing vector during navigation – constitutes a primitive and presumably less‐flexible strategy than cognitive mapping because path integration relies primarily on vestibular stimuli and pace counting. The historical debate as to whether complex spatial navigation is ruled by associative learning or cognitive map mechanisms has been challenged by experimental difficulties in successfully neutralizing path integration. To our knowledge, there are only three studies that have succeeded in resolving this issue, all showing clear evidence of novel route taking, a behaviour outside the scope of traditional associative learning accounts. Nevertheless, there is no mechanistic explanation as to how animals perform novel route taking. We propose here a new model of spatial learning that combines path integration with higher‐order associative learning, and demonstrate how it can account for novel route taking without a cognitive map, thus resolving this long‐standing debate. We show how our higher‐order path integration (HOPI) model can explain spatial inferences, such as novel detours and shortcuts. Our analysis suggests that a phylogenetically ancient, vector‐based navigational strategy utilizing associative processes is powerful enough to support complex spatial inferences.