Finding Home: Landmark Ambiguity in Human Navigation

Abstract

Memories of places often include landmark cues, i.e., information provided by the spatial arrangement of distinct objects with respect to the target location. To study how humans combine landmark information for navigation, we conducted two experiments: To this end, participants were either provided with auditory landmarks while walking in a large sports hall or with visual landmarks while walking on a virtual-reality treadmill setup. We found that participants cannot reliably locate their home position due to ambiguities in the spatial arrangement when only one or two uniform landmarks provide cues with respect to the target. With three visual landmarks that look alike, the task is solved without ambiguity, while audio landmarks need to play three unique sounds for a similar performance. This reduction in ambiguity through integration of landmark information from 1, 2, and 3 landmarks is well modeled using a probabilistic approach based on maximum likelihood estimation. Unlike any deterministic model of human navigation (based e.g., on distance or angle information), this probabilistic model predicted both the precision and accuracy of the human homing performance. To further examine how landmark cues are integrated we introduced systematic conflicts in the visual landmark configuration between training of the home position and tests of the homing performance. The participants integrated the spatial information from each landmark near-optimally to reduce spatial variability. When the conflict becomes big, this integration breaks down and precision is sacrificed for accuracy. That is, participants return again closer to the home position, because they start ignoring the deviant third landmark. Relying on two instead of three landmarks, however, goes along with responses that are scattered over a larger area, thus leading to higher variability. To model the breakdown of integration with increasing conflict, the probabilistic model based on a simple Gaussian distribution used for Experiment 1 needed a slide extension in from of a mixture of Gaussians. All parameters for the Mixture Model were fixed based on the homing performance in the baseline condition which contained a single landmark. from the 1-Landmark Condition. This way we found that the Mixture Model could predict the integration performance and its breakdown with no additional free parameters. Overall these data suggest that humans use similar optimal probabilistic strategies in visual and auditory navigation, integrating landmark information to improve homing precision and balance homing precision with homing accuracy.

An Adaptive Cue Selection Model of Allocentric Spatial Reorientation

“…Experiment 2 was done to see if the difference in results between adults in Experiment 1 and a previous study (Jetzschke et al, 2017) was due to the use of disorientation in Experiment 1. Since results were like Experiment 1 (i.e.…”

“…To isolate the variable preventing cue combination, we can closely compare Experiment 1 and a previous study that did find cue combination (Jetzschke, Ernst, Froehlich, & Boeddeker, 2017). Both studies used adults.…”

Fractal dimension and the navigational information provided by natural scenes

“…Our results thus appear to corroborate in a quantitative way the conclusions reached by Juliani et al (2016) who found an inverse relationship between the fractal dimension of virtual scenes and the performance of human observers in localizing goals in virtual environments, the postulated reason being that low fractal dimension scenes offer more cues for recognizing individual landmarks, which is made intuitively clear by the views of Juliani et al’s environments in their Fig 3 [ 20 ]. When locating a goal location humans are indeed likely to make use of individual object recognition rather than of information provided by the unsegmented panorama [ 18 – 19 ].…”

“…In the following analysis, we will be using the rotIDF in outdoor environments as one of the ways in which navigational information can be quantified, but add the caveat that this may not be the most adequate way of doing so when considering human navigation, because humans are likely to make much more use of object recognition than insects [ 18 – 19 ]. However, our main interest here is to test whether fractal dimension as a measure of scene complexity is also a useful measure of the navigational information provided by natural scenes.…”

Where did I park my car? Influence of landmark permanency on navigation

“…15 participants (9 male, 6 female) aged between 19 and 28 (m = 22.2, sd = 3.1) took part in the first experiment. The sample size was determined based on an effect size from a similar experiment (Cohen's dz = 0.98; Jetzschke, Ernst, Froehlich, & Boeddeker, 2017), aiming for a power of 0.95 with alpha = 0.05 (two-tailed). All participants gave their written informed consent before participating and received course credits or money (7€/h).…”