Biomimetic bayesian models of navigation: How are environment geometry-based and landmark-based strategies articulated in humans?

Julien Diard (1) , Panagiota Panagiotaki (2) , Alain Berthoz (2)

1 LPNC - Laboratoire de Psychologie et NeuroCognition
2 LPPA - Laboratoire de Physiologie de la Perception et de l'Action

We propose a computational model of human navigation, which encompasses both geometry-based and landmark-based navigation strategies. This model is based on a study of human cognitive strategies during a path memorization task in a Virtual Reality (VR) experiment. Participants were asked to memorize predefined paths in a large-scale virtual city (COSMOpoliS (c)). Our computational model qualitatively reproduces the results of this experiment. This model uses the Bayesian formalism, and focuses on the interplay between the elementary cognitive strategies hypothesized above. It offers an original interpretation of the way these strategies might be articulated, departing from the classical hierarchical structure. This novel view might be fruitful for robotic models from a biomimetic perspective, where managing representations of large-scale and complex environments is still a challenge.

Domaines: Informatique; Psychologie

Julien Diard, Panagiota Panagiotaki, Alain Berthoz. Biomimetic bayesian models of navigation: How are environment geometry-based and landmark-based strategies articulated in humans?. International Conference on Cognitive Modeling (ICCM 09), Jul 2009, Manchester, United Kingdom. pp.210-215. ⟨hal-00530372⟩