Literature DB >> 11064802

Optic flow helps humans learn to navigate through synthetic environments.

M P Kirschen1, M J Kahana, R Sekuler, B Burack.   

Abstract

Self-movement through an environment generates optic flow, a potential source of heading information. But it is not certain that optic flow is sufficient to support navigation, particularly navigation along complex, multi-legged paths. To address this question, we studied human participants who navigated synthetic environments with and without salient optic flow. Participants used a keyboard to control realistic simulation of self-movement through computer-rendered, synthetic environments. Because these environments comprised series of identically textured virtual corridors and intersections, participants had to build up some mental representation of the environment in order to perform. The impact of optic flow on learning was examined in two experiments. In experiment 1, participants learned to navigate multiple T-junction mazes with and without accompanying optic flow. Optic flow promoted faster learning, mainly by preventing disorientation and backtracking in the maze. In experiment 2, participants found their way around a virtual city-block environment, experiencing two different kinds of optic flow as they went. By varying the rate at which the display was updated, we created optic flow that was either fluid or choppy. Here, fluid optic flow (as compared with choppy optic flow) enabled participants to locate a remembered target position more accurately. When other cues are unavailable, optic flow can be a significant aid in wayfinding. Among other things, optic flow can facilitate path integration, which involves updating a mental representation of place by combining the trajectories of previously travelled paths [corrected].

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Year:  2000        PMID: 11064802     DOI: 10.1068/p3096

Source DB:  PubMed          Journal:  Perception        ISSN: 0301-0066            Impact factor:   1.490


  3 in total

Review 1.  The cognitive correlates of human brain oscillations.

Authors:  Michael J Kahana
Journal:  J Neurosci       Date:  2006-02-08       Impact factor: 6.167

2.  MAGELLAN: a cognitive map-based model of human wayfinding.

Authors:  Jeremy R Manning; Timothy F Lew; Ningcheng Li; Robert Sekuler; Michael J Kahana
Journal:  J Exp Psychol Gen       Date:  2014-02-03

3.  A Biologically-Inspired Model to Predict Perceived Visual Speed as a Function of the Stimulated Portion of the Visual Field.

Authors:  Fabio Solari; Martina Caramenti; Manuela Chessa; Paolo Pretto; Heinrich H Bülthoff; Jean-Pierre Bresciani
Journal:  Front Neural Circuits       Date:  2019-10-30       Impact factor: 3.492

  3 in total

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