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Spatial perspective taking is an essential cognitive ability that enables people to imagine how an object or scene would appear from a perspective different from their current physical viewpoint. This process is fundamental for successful navigation, especially when people utilize navigational aids (e.g., maps) and the information provided is shown from a different perspective. Research on spatial perspective taking is primarily conducted using paper-pencil tasks or computerized figural tasks. However, in daily life, navigation takes place in a three-dimensional (3D) space and involves movement of human bodies through space, and people need to map the perspective indicated by a 2D, top down, external representation to their current 3D surroundings to guide their movements to goal locations. In this study, we developed an immersive viewpoint transformation task (iVTT) using ambulatory virtual reality (VR) technology. In the iVTT, people physically walked to a goal location in a virtual environment, using a first-person perspective, after viewing a map of the same environment from a top-down perspective. Comparing this task with a computerized version of a popular paper-and-pencil perspective taking task (SOT: Spatial Orientation Task), the results indicated that the SOT is highly correlated with angle production error but not distance error in the iVTT. Overall angular error in the iVTT was higher than in the SOT. People utilized intrinsic body axes (front/back axis or left/right axis) similarly in the SOT and the iVTT, although there were some minor differences. These results suggest that the SOT and the iVTT capture common variance and cognitive processes, but are also subject to unique sources of error caused by different cognitive processes. The iVTT provides a new immersive VR paradigm to study perspective taking ability in a space encompassing human bodies, and advances our understanding of perspective taking in the real world. 

Humans and other animals have a remarkable capacity to translate their position from one spatial frame of reference to another. The ability to seamlessly move between top-down and first-person views is important for navigation, memory formation, and other cognitive tasks. Evidence suggests that the medial temporal lobe and other cortical regions contribute to this function. To understand how a neural system might carry out these computations, we used variational autoencoders (VAEs) to reconstruct the first-person view from the top-down view of a robot simulation, and vice versa. Many latent variables in the VAEs had similar responses to those seen in neuron recordings, including location-specific activity, head direction tuning, and encoding of distance to local objects. Place-specific responses were prominent when reconstructing a first-person view from a top-down view, but head direction–specific responses were prominent when reconstructing a top-down view from a first-person view. In both cases, the model could recover from perturbations without retraining, but rather through remapping. These results could advance our understanding of how brain regions support viewpoint linkages and transformations. 

Space syntax is an influential framework for quantifying the relationship between environmental geometry and human behavior. Although many studies report high syntactic–behavioral correlations, previous pedestrian data were collected at low spatiotemporal resolutions, and data transformations and sampling strategies vary widely; here, we systematically test the robustness of space syntax’s predictive strength by examining how these factors impact correlations. We used virtual reality and motion tracking to correlate 30 syntactic measures with high resolution walking trajectories downsampled at 10 grid resolutions and subjected to various log transformations. Overall, correlations declined with increasing grid resolution and were sensitive to data transformations. Moreover, simulations revealed spuriously high correlations (e.g. R 2  = 1) with sparsely sampled data (<23 locations). These results strongly suggest that syntactic–behavioral correlations are not robust to changes in spatiotemporal resolution, and that high correlations obtained in previous studies could be inflated due to transformations, data resolution, or sampling strategies.