Cognitive Path Planning With Spatial Memory Distortion

Human path-planning operates differently from deterministic AI-based path-planning algorithms due to the decay and distortion in a human's spatial memory and the lack of complete scene knowledge. Here, we present a cognitive model of path-planning that simulates human-like learning of unfamilia...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2023-08, Vol.29 (8), p.3535-3549
Main Authors: Dubey, Rohit K., Sohn, Samuel S., Thrash, Tyler, Holscher, Christoph, Borrmann, Andre, Kapadia, Mubbasir
Format: Article
Language:English
Subjects:
agglomerative hierarchical clustering
Algorithms
Cognitive path-planning
Computational modeling
Data models
Distortion
fine-to-course
Games
Graphical representations
human wayfinding
Memory
Memory tasks
Navigation
Path planning
Recall
Simulation
Solid modeling
Spatial analysis
spatial memory
Task analysis
Virtual reality
Wayfinding
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Summary:Human path-planning operates differently from deterministic AI-based path-planning algorithms due to the decay and distortion in a human's spatial memory and the lack of complete scene knowledge. Here, we present a cognitive model of path-planning that simulates human-like learning of unfamiliar environments, supports systematic degradation in spatial memory, and distorts spatial recall during path-planning. We propose a Dynamic Hierarchical Cognitive Graph (DHCG) representation to encode the environment structure by incorporating two critical spatial memory biases during exploration: categorical adjustment and sequence order effect . We then extend the "Fine-To-Coarse" (FTC), the most prevalent path-planning heuristic, to incorporate spatial uncertainty during recall through the DHCG. We conducted a lab-based Virtual Reality (VR) experiment to validate the proposed cognitive path-planning model and made three observations: (1) a statistically significant impact of sequence order effect on participants' route-choices, (2) approximately three hierarchical levels in the DHCG according to participants' recall data, and (3) similar trajectories and significantly similar wayfinding performances between participants and simulated cognitive agents on identical path-planning tasks. Furthermore, we performed two detailed simulation experiments with different FTC variants on a Manhattan-style grid. Experimental results demonstrate that the proposed cognitive path-planning model successfully produces human-like paths and can capture human wayfinding's complex and dynamic nature, which traditional AI-based path-planning algorithms cannot capture.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2022.3163794