Hugo J. Spiers | Searcharxiv

arXiv2026

Human Navigation Behaviour and Brain Dynamics in Real-world Contexts

The study of navigation behaviour and the associated brain dynamics have been a focus increasing research over the last decades. Coinciding with this has been an increased focus on a more ecological understanding of cognition. Here we review recent research seeking to provide a more naturalistic, ecological understanding of human navigation behaviour and brain dynamics. Research in this area falls into four categories: testing navigation in real-world environments, analysis of data collected from tracking individuals during daily life, navigation in simulated or virtual environments mimicking the real-world, and mobile brain recording methods. Combining these different approaches to understand the neural basis of navigation shows excellent promise. We conclude with future directions for this research area.

q-bio.NC↗

arXiv2020

Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks

Spatial trajectories are ubiquitous and complex signals. Their analysis is crucial in many research fields, from urban planning to neuroscience. Several approaches have been proposed to cluster trajectories. They rely on hand-crafted features, which struggle to capture the spatio-temporal complexity of the signal, or on Artificial Neural Networks (ANNs) which can be more efficient but less interpretable. In this paper we present a novel ANN architecture designed to capture the spatio-temporal patterns characteristic of a set of trajectories, while taking into account the demographics of the navigators. Hence, our model extracts markers linked to both behaviour and demographics. We propose a composite signal analyser (CompSNN) combining three simple ANN modules. Each of these modules uses different signal representations of the trajectory while remaining interpretable. Our CompSNN performs significantly better than its modules taken in isolation and allows to visualise which parts of the signal were most useful to discriminate the trajectories.

cs.LG↗