Bayesian models of human navigation behaviour in an augmented reality audiomaze.
Bayesian models of human navigation behaviour in an augmented reality audiomaze.
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DOI：
10.1111/ejn.15061
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发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Iversen JR
中科院分区：
文献类型：
--
作者：
Shikauchi Y;Miyakoshi M;Makeig S;Iversen JR
我们研究了在“Audiomaze”环境中探索性真实空间导航任务中记录的人体全身动作捕捉数据的贝叶斯建模（见Miyakoshi等人在同一卷中的合著论文），以研究地图学习对导航行为的影响。有三种模型，一种是仅反馈模型（没有地图学习），一种是地图重置模型（单次试验有限的地图学习），一种是地图更新模型（三次试验累积的地图学习）。估计的行为变量包括步长和转弯角度。结果表明，使用地图学习模型估计的步长始终比仅反馈模型更准确。同样的效果被证实为转角估计，但只是来自第三次试验的数据。我们将这些结果解释为人类地图学习对导航行为的贝叶斯证据。此外，将参与者分为自我中心和非中心导航员组，表明地图更新模型在估计步长方面具有优势，但仅适用于非中心导航员。这种相互作用表明，非中心导航员可能比自我中心导航员更能利用地图学习。我们讨论了这些结果与同时定位和映射（SLAM）问题的关系。将参与者分成两组，一组为非中心导航员，另一组为自我中心导航员，结果显示，在两组中，基于地图的模型都比仅反馈的模型具有类似的优势。仅在非中心导航器中，地图更新模型比地图重置模型有进一步的优势。地图学习（地图更新）模型最适合异心导航员行为的观察结果与异心导航员可能在基于他们构建的心理地图探索迷宫时表现更好的观点是一致的，这种优势可能会通过重复导航而进一步加强。
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. Splitting the participants into two subgroups, one for allocentric navigators and the other for egocentric navigators, showed a similar advantage of the map‐based models over the feedback‐only model in both groups. In the allocentric navigators only, there was a further advantage of the map updating model over the map resetting model. The observation that the map learning (map‐updating) model best fit the allocentric navigator behavior is consistent with the idea that allocentric navigators may have been better in exploring the maze based on the mental maps they built, and this advantage may be further reinforced by repeating the navigation.
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