Biology subjects

Boeddeker, N.

Publications and source records attributed to Boeddeker, N..

4 recordsLinked to original sources

bioRxiv2025

Embodied Navigation: whole-body movement drives path integration in large-scale free-walking virtual reality

Human navigation relies on combining body cues (vestibular and proprioceptive signals) with visual cues such as optic flow. The weighting and integration of these signals during the continuous tracking of walked distances and angles, known as path integration, remain poorly understood. Previous path integration studies have been limited to small spaces (< 150 m2) and the influence of complex environments on cue weighting of body and vision cues is still unclear. Here, we conducted the largest-environment free-walking virtual reality navigation study to date in a 45 x 25 m facility (1, 215 m2) using triangle completion tasks in naturalistic environments. We systematically manipulated sensory input across three conditions: natural active walking (full sensory integration), active joystick control (visual cues only), and blindfolded active walking (body cues only). Participants navigated through both sparse fallow land without trees and more complex forest environments with 400 trees. We embedded performance data in a Bayesian cue combination model to analyse the underlying combination mechanism. Our results provide evidence that most participants substantially favour body cues over visual cues in a non-Bayesian combination process, with considerable inter-individual variance in cue dominance strength and side biases. While transitioning from fallow land to forests reduced directional variance, weighting of body and visual information remained constant. These findings advance our understanding of human spatial navigation by demonstrating that body-based cues dominate path integration even in visually rich, large-scale environments, challenging assumptions about optimal Bayesian cue integration in human navigation.

neuroscience↗

bioRxiv2025

Human performance in the Traveling Salesman Problem is influenced by spatial scale

Like many other animals, we humans frequently face complex route optimization problems when planning journeys between multiple locations. Several strategies can be employed to approach such Traveling Salesman Problems. However, these may be strongly constrained by spatial scales, for instance if the goal is to navigate in a supermarket, in a city, or across a continent. Here, we monitored the route optimization performances of human subjects collecting objects in a video game simulating 3D environments at various spatial scales. Unexpectedly, route optimization performances peaked at intermediate spatial scales, where participants could use global optimization strategies. At very large and very small spatial scales, however, participants predominantly employed less efficient local optimization strategies. At the very large scale, the considerable distances between objects prevented them from being seen all at once, making global planning difficult. At the very small scale, participants reported having chosen the shortest path, although they did not. This mismatch between perceived and actual performances suggests suboptimal alternatives were sufficiently short to be considered equivalent to the optimal route. Spatial scale thus strongly influences route planning in human navigators and may determine the spatial behaviors of a wide range of animals facing similar routing problems in their everyday lives.

animal behavior and cognition↗

bioRxiv2025

Uncovering persistent biases in human path integration by separating left and right trials

Navigation ability strongly varies between humans. Careful analysis of errors occurring in navigation, or indeed any cognitive process, offers insight into the underlying mechanisms at different levels. While analyses at the individual level allow nuanced identification of error persistences across conditions and time, the population level facilitates generalisation but precludes conclusions about lower-level phenomena. Regarding the critical navigation mechanism of path integration - the continuous tracking of navigated paths for self-localisation - previous studies have focused on populationlevel analyses, revealing systematic errors in estimating the travelled angles and distances. However, at the individual level, there are indications that people also possess left or right biases that are classically overlooked when pooling left and right trials. Therefore, we carefully investigate individual path integration errors in (1) a re-analysis of data from several influential human navigation studies, and (2) our own virtual reality path integration experiment. For both, we confirm time-persistent individual side biases, but find no evidence for consistent errors at the population level, suggesting that important aspects in human navigation performance might be overlooked by averaging across sides.

animal behavior and cognition↗

bioRxiv2023

Not seeing the forest for the trees: Combination of path integration and landmark cues in human virtual navigation

IntroductionIn order to successfully move from place to place, our brain often combines sensory inputs from various sources by dynamically weighting spatial cues according to their reliability and relevance for a given task. Two of the most important cues in navigation are the spatial arrangement of landmarks in the environment, and the continuous path integration of travelled distances and changes in direction. Several studies have shown that Bayesian integration of cues provides a good explanation for navigation in environments dominated by small numbers of easily identifiable landmarks. However, it remains largely unclear how cues are combined in more complex environments. MethodsTo investigate how humans process and combine landmarks and path integration in complex environments, we conducted a series of triangle completion experiments in virtual reality, in which we varied the number of landmarks from an open steppe to a dense forest, thus going beyond the spatially simple environments that have been studied in the past. We analysed spatial behaviour at both the population and individual level with linear regression models and developed a computational model, based on maximum likelihood estimation (MLE), to infer the underlying combination of cues. ResultsOverall homing performance was optimal in an environment containing three landmarks arranged around the goal location. With more than three landmarks, individual differences between participants in the use of cues are striking. For some, the addition of landmarks does not worsen their performance, whereas for others it seems to impair their use of landmark information. DiscussionIt appears that navigation success in complex environments depends on the ability to identify the correct clearing around the goal location, suggesting that some participants may not be able to see the forest for the trees.

neuroscience↗