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Human path-planning operates differently from deterministic AI-based path-planning algorithms due to the decay and distortion in a human&#x0027;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 &#x201C;Fine-To-Coarse&#x201D; (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&#x2019; route-choices, (2) approximately three hierarchical levels in the DHCG according to participants&#x2019; 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&#x0027;s complex and dynamic nature, which traditional AI-based path-planning algorithms cannot capture.

Cognitive Path Planning With Spatial Memory Distortion