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(2024-07-05 17:12):
#paper doi:10.1016/j.cognition.2022.105126 Adaptive cognitive maps for curved surfaces in the 3D world 三维空间中,垂直信息大量存在,但之前大部分研究只聚焦于平面上的空间表征,构建2D认知地图。在该研究中, 研究者研究了人类是通过构建降维的扁平 2D 地图还是完整的 3D 地图来表示曲面。被试通过在表面的凹面上行驶(实验 1)、驾驶并垂直观察(实验 2)或飞行(实验 3)来了解虚拟环境中位于平面和曲面上的物体的位置。随后,他们被要求检索物体之间的路径距离或 3D 欧几里得距离。结果表明,被试明显低估曲线的路径距离,而接触3D结构更多的运动模式(飞行)改善了被试的估计表现。这些结果表明,被试在三维空间的认知地图不是2D的,也不是3D的,而是2D的流行表示,该认知地图可以根据经验和任务需求而调整。
3D世界中曲面的自适应认知地图
Abstract:
Terrains in a 3D world can be undulating. Yet, most prior research has exclusively investigated spatial representations on a flat surface, leaving a 2D cognitive map as the dominant model …
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Terrains in a 3D world can be undulating. Yet, most prior research has exclusively investigated spatial representations on a flat surface, leaving a 2D cognitive map as the dominant model in the field. Here, we investigated whether humans represent a curved surface by building a dimension-reduced flattened 2D map or a full 3D map. Participants learned the location of objects positioned on a flat and curved surface in a virtual environment by driving on the concave side of the surface (Experiment 1), driving and looking vertically (Experiment 2), or flying (Experiment 3). Subsequently, they were asked to retrieve either the path distance or the 3D Euclidean distance between the objects. Path distance estimation was good overall, but we found a significant underestimation bias for the path distance on the curve, suggesting an influence of potential 3D shortcuts, even though participants were only driving on the surface. Euclidean distance estimation was better when participants were exposed more to the global 3D structure of the environment by looking and flying. These results suggest that the representation of the 2D manifold, embedded in a 3D world, is neither purely 2D nor 3D. Rather, it is flexible and dependent on the behavioral experience and demand.
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翻译
3D 世界中的地形可能是起伏的。然而,大多数先前的研究都专门研究了平面上的空间表示,而 2D 认知地图是该领域的主要模型。在这里,我们研究了人类是否通过构建降维扁平化 2D 地图或完整的 3D 地图来表示曲面。参与者通过在表面的凹面驾驶(实验 1)、驾驶和垂直观察(实验 2)或飞行(实验 3)来学习在虚拟环境中放置在平面和曲面上的物体的位置。随后,他们被要求检索物体之间的路径距离或3D欧几里得距离。路径距离估计总体上是好的,但我们发现曲线上的路径距离存在明显的低估偏差,这表明即使参与者只是在表面上行驶,也可能存在潜在的3D捷径的影响。当参与者通过观察和飞行更多地暴露于环境的全球三维结构时,欧几里得距离估计更好。这些结果表明,嵌入在 3D 世界中的 2D 流形的表示既不是纯粹的 2D 也不是 3D。相反,它是灵活的,并依赖于行为经验和需求。