Metric information in cognitive maps: Euclidean embedding of non-Euclidean environments.

ABSTRACT: The structure of the internal representation of surrounding space, the so-called cognitive map, has long been debated. A Euclidean metric map is the most straight-forward hypothesis, but human navigation has been shown to systematically deviate from the Euclidean ground truth. Vector navigation based on non-metric models can better explain the observed behavior, but also discards useful geometric properties such as fast shortcut estimation and cue integration. Here, we propose another alternative, a Euclidean metric map that is systematically distorted to account for the observed behavior. The map is found by embedding the non-metric model, a labeled graph, into 2D Euclidean coordinates. We compared these two models using data from a human behavioral study where participants had to learn a

SUBMITTER: Baumann T 

PROVIDER: S-EPMC10775987 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature