Semantic map learning and externalization in an embodied neural agent: A comparison to human behavioral and neural data

All mammals can build internal cognitive maps from sensory input, supporting spatial learning and planning. While rodent studies have shown the mammalian navigation system solves the SLAM (Simultaneous Localization and Mapping) problem, its role in human spatial memory and overt recall are less understood. To investigate this, we adapted a spiking semantic SLAM algorithm for a “Treasure Hunt” task, where human participants navigate a 3D beach in virtual reality and later point to remembered object locations. Our agent integrates networks for bipedal locomotion, vision, memory, and arm control to enable first-person learning of place-object associations, and externalizing that knowledge by pointing and expressing confidence. Comparing model observables to human data, we replicate key behavioral and neural effects: monotonic scaling of accuracy with confidence, and recall-dependence on local field potential power observed in the left hippocampus. This work offers a mechanistic framework linking embodied navigation, memory, and communication in human spatial cognition.