Bayesian models of human navigation behaviour in an augmented reality audiomaze.

We investigated Bayesian modelling of human whole-body motion capture data recorded during an exploratory real-space navigation task in an "Audiomaze" environment (see the companion paper by Miyakoshi et al. in the same volume) to study the effect of map learning on navigation behaviour. There were three models, a feedback-only model (no map learning), a map resetting model (single-trial limited map learning), and a map updating model (map learning accumulated across three trials). The estimated behavioural variables included step sizes and turning angles. Results showed that the estimated step sizes were constantly more accurate using the map learning models than the feedback-only model. The same effect was confirmed for turning angle estimates, but only for data from the third trial. We interpreted these results as Bayesian evidence of human map learning on navigation behaviour. Furthermore, separating the participants into groups of egocentric and allocentric navigators revealed an advantage for the map updating model in estimating step sizes, but only for the allocentric navigators. This interaction indicated that the allocentric navigators may take more advantage of map learning than do egocentric navigators. We discuss relationships of these results to simultaneous localization and mapping (SLAM) problem.

Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging.

Functional magnetic resonance imaging (fMRI) acquisitions include a great deal of individual variability. This individuality often generates obstacles to the efficient use of databanks from multiple subjects. Although recent studies have suggested that inter-regional connectivity reflects individuality, conventional three-dimensional (3D) registration methods that calibrate inter-subject variability are based on anatomical information about the gray matter shape (e.g., T1-weighted). Here, we present a new registration method focusing more on the white matter structure, which is directly related to the connectivity in the brain, and apply it to subject-transfer brain decoding. Our registration method based on diffusion tensor imaging (DTI) transferred functional maps of each individual to a common anatomical space, where a decoding analysis of multi-voxel patterns was performed. The decoder trained on functional maps from other individuals in the common space showed a transfer decoding accuracy comparable to that of an individual decoder trained on single-subject functional maps. The DTI-based registration allowed more precise transformation of gray matter boundaries than a well-established T1-based method. These results suggest that the DTI-based registration is a promising tool for standardization of the brain functions, and moreover, will allow us to perform 'zero-shot' learning of decoders which is profitable in brain machine interface scenes.

Robust encoding of scene anticipation during human spatial navigation.

In a familiar city, people can recall scene views (e.g., a particular street corner scene) they could encounter again in the future. Complex objects with multiple features are represented by multiple neural units (channels) in the brain, but when anticipating a scene view, the kind of feature that is assigned to a specific channel is unknown. Here, we studied neural encoding of scene view anticipation during spatial navigation, using a novel data-driven analysis to evaluate encoding channels. Our encoding models, based on functional magnetic resonance imaging (fMRI) activity, provided channel error correction via redundant channel assignments that reflected the navigation environment. We also found that our encoding models strongly reflected brain activity in the inferior parietal gyrus and precuneus, and that details of future scenes were locally represented in the superior prefrontal gyrus and temporal pole. Furthermore, a decoder associated with the encoding models accurately predicted future scene views in both passive and active navigation. These results suggest that the human brain uses scene anticipation, mediated especially by parietal and medial prefrontal cortical areas, as a robust and effective navigation processing.

Decoding the view expectation during learned maze navigation from human fronto-parietal network.

Humans use external cues and prior knowledge about the environment to monitor their positions during spatial navigation. View expectation is essential for correlating scene views with a cognitive map. To determine how the brain performs view expectation during spatial navigation, we applied a multiple parallel decoding technique to functional magnetic resonance imaging (fMRI) when human participants performed scene choice tasks in learned maze navigation environments. We decoded participants' view expectation from fMRI signals in parietal and medial prefrontal cortices, whereas activity patterns in occipital cortex represented various types of external cues. The decoder's output reflected participants' expectations even when they were wrong, corresponding to subjective beliefs opposed to objective reality. Thus, view expectation is subjectively represented in human brain, and the fronto-parietal network is involved in integrating external cues and prior knowledge during spatial navigation.