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.

Automated summary

The study found that using map learning models led to more accurate estimation of step sizes and turning angles, with differences in the extent of advantage between egocentric and allocentric navigators. This suggests a Bayesian evidence of human map learning on navigation behavior and its implications for different types of navigators.