Boeddeker, N. | Search bioRxiv

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↗