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Updated: Jun 22, 2026
06:28
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
Published on: August 26, 2018
Cortical dynamics of navigation and steering in natural scenes: Motion-based object segmentation, heading, and
N Andrew Browning1, Stephen Grossberg, Ennio Mingolla
1Department of Cognitive and Neural Systems, Boston University, Boston, MA 02215, USA
Summary
The ViSTARS neural model simulates primate navigation by using motion cues to identify objects and determine heading. This model accurately predicts human-like trajectories for goal approach and obstacle avoidance.
Area of Science:
- Computational neuroscience
- Primate vision
- Neural modeling
Background:
- Visually guided navigation in cluttered environments is complex for animals and humans.
- Existing models often struggle to integrate object and self-motion information for navigation.
Purpose of the Study:
- To propose the ViSTARS neural model for visually guided navigation.
- To explain how primates use motion information for object segmentation and heading determination.
- To simulate human navigational trajectories and steering decisions.
Main Methods:
- Developed the ViSTARS neural model processing video input.
- Modeled computations in cortical areas MT(-)/MSTv (object motion) and MT(+)/MSTd (self-motion).
- Incorporated attentive feedback loops between areas for accurate motion and heading estimation.
Main Results:
- The model's retina responds to input transients, and V1 estimates local motion.
- MT(+) and MSTd interact for accurate heading estimation, simulating human data.
- MT(-) and MSTv interact for object motion estimation, guiding steering decisions.
Conclusions:
- The ViSTARS model successfully simulates primate navigation by integrating object and self-motion information.
- Navigational trajectories generated by the model closely match human performance.
- The model provides insights into the neural mechanisms underlying visually guided navigation.
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