Scherer Jonas, Müller Martin M, Unterbrink Patrick, Meier Sina, Egelhaaf Martin, Bertrand Olivier J N, Boeddeker Norbert
Department of Neurobiology, Bielefeld University, Bielefeld, Germany.
Department of Cognitive Neuroscience, Bielefeld University, Bielefeld, Germany.
Front Behav Neurosci. 2024 May 21;18:1399716. doi: 10.3389/fnbeh.2024.1399716. eCollection 2024.
In 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.
To 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.
Overall 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.
It 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.
为了成功地从一个地方移动到另一个地方,我们的大脑常常通过根据空间线索的可靠性和与给定任务的相关性对其进行动态加权,来整合来自各种来源的感官输入。导航中两个最重要的线索是环境中地标的空间布局,以及行进距离和方向变化的连续路径整合。多项研究表明,线索的贝叶斯整合为在由少量易于识别的地标主导的环境中的导航提供了很好的解释。然而,在更复杂的环境中线索是如何组合的,在很大程度上仍不清楚。
为了研究人类在复杂环境中如何处理和组合地标与路径整合,我们在虚拟现实中进行了一系列三角形完成实验,在实验中我们将地标数量从开阔的草原变化到茂密的森林,从而超越了过去所研究的空间简单的环境。我们使用线性回归模型在群体和个体层面分析空间行为,并基于最大似然估计(MLE)开发了一个计算模型,以推断线索的潜在组合。
在目标位置周围布置三个地标的环境中,总体归巢性能最佳。当地标超过三个时,参与者在使用线索方面的个体差异非常显著。对于一些人来说,增加地标不会降低他们的表现,而对于另一些人来说,这似乎会损害他们对地标信息的使用。
在复杂环境中的导航成功似乎取决于识别目标位置周围正确空旷区域的能力,这表明一些参与者可能只见树木不见森林。