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    This study introduces a cognitive path-planning model that mimics human spatial memory decay and distortion. The model, using a Dynamic Hierarchical Cognitive Graph, successfully replicates human-like navigation in unfamiliar environments.

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    Area of Science:

    • Cognitive Science
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Human path-planning deviates from AI due to imperfect spatial memory and incomplete environmental knowledge.
    • Existing AI path-planning algorithms lack human-like cognitive processes like memory decay and distortion.

    Purpose of the Study:

    • To develop a cognitive model simulating human path-planning in unfamiliar environments.
    • To incorporate spatial memory biases (categorical adjustment, sequence order effect) into path-planning.
    • To extend the Fine-To-Coarse (FTC) heuristic with spatial uncertainty.

    Main Methods:

    • Proposed a Dynamic Hierarchical Cognitive Graph (DHCG) for environment encoding.
    • Extended the Fine-To-Coarse (FTC) path-planning heuristic.
    • Validated the model through a Virtual Reality (VR) experiment and simulations.

    Main Results:

    • Observed a significant sequence order effect on human route choices in the VR experiment.
    • Identified approximately three hierarchical levels in the DHCG from participant recall data.
    • Demonstrated similar trajectories and wayfinding performance between humans and the cognitive model.

    Conclusions:

    • The cognitive path-planning model successfully generates human-like paths.
    • The model captures the complex, dynamic nature of human wayfinding, outperforming traditional AI algorithms.
    • The DHCG representation effectively models spatial memory biases in path-planning.