Hippocampal Signal Complexity and Rate-of-Change Predict Navigational Performance: Evidence from a Two-Week VR Training Program

The hippocampus is believed to be an important region for spatial navigation, helping to represent the environment and plan routes. Evidence from rodents has suggested that the hippocampus processes information in a graded manner along its long-axis, with anterior regions encoding coarse information and posterior regions encoding fine-grained information. Brunec et al. (2018) demonstrated similar patterns in humans in a navigation paradigm, showing that the anterior-posterior gradient in representational granularity and the rate of signal change exist in the human hippocampus. However, the stability of these signals and their relationship to navigational performance remain unclear. In this study, we conducted a two-week training program where participants learned to navigate through a novel city environment. We investigated inter-voxel similarity (IVS) and temporal auto-correlation hippocampal signals, measures of representational granularity and signal change, respectively. Specifically, we investigated how these signals were influenced by navigational ability (i.e., stronger vs. weaker spatial learners), training session, and navigational dynamics. Our results revealed that stronger learners exhibited a clear anterior-posterior distinction in IVS in the right hippocampus, while weaker learners showed less pronounced distinctions. Additionally, lower general IVS levels in the hippocampus were linked to better early learning. Successful navigation was characterized by faster signal change, particularly in the anterior hippocampus, whereas failed navigation lacked the anterior-posterior distinction in signal change. These findings suggest that signal complexity and signal change in the hippocampus are important factors for successful navigation, with IVS representing information organization and auto-correlation reflecting moment-to-moment updating. These findings support the idea that efficient organization of scales of representation in an environment may be necessary for efficient navigation itself. Understanding the dynamics of these neural signals provides insights into the mechanisms underlying navigational learning in humans.