Wormholes in virtual space: From cognitive maps to cognitive graphs
Wormholes in virtual space: From cognitive maps to cognitive graphs
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虚拟空间中的虫洞：从认知图到认知图
DOI：
10.1016/j.cognition.2017.05.020
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发表时间：
2017
期刊：
影响因子：
3.4
通讯作者：
Ericson, Jonathan D.
中科院分区：
文献类型：
--
作者：
Warren, William H.;Rothman, Daniel B.;Schnapp, Benjamin H.;Ericson, Jonathan D.
人类和其他动物在视觉信息和路径整合的基础上建立了环境的空间知识。我们比较了三个假设的几何形状的导航空间的知识：（一）“认知地图”与度量欧几里德结构和一致的坐标系，（B）“拓扑图”或网络的地方之间的路径，和（c）“标记图”纳入当地的度量信息的路径长度和交界角。在两个实验中，参与者走在一个非欧几里德的环境中，一个虚拟的树篱迷宫包含两个“虫洞”，视觉旋转和位置之间的心灵传输。在训练过程中，他们从一个“家”的位置学习了八个目标物体的度量位置，这些目标物体是单独可见的。在测试过程中，更短的虫洞路线的目标是首选，和新的捷径是方向性的，相反的拓扑假设。捷径受到虫洞的强烈影响，平均恒定误差为37°和41°（预期为45°），这表明违反了空间知识中的度量假设。此外，虫洞附近目标的捷径相对于侧翼目标发生了变化，揭示了空间知识中的“撕裂”（86%），“折叠”（91%）和顺序反转（66%）。此外，参与者完全没有意识到这些几何不一致性，反映了对欧几里得结构的惊人的不敏感性。欧氏映射模型和标号图模型下的捷径数据概率对后者（BFGM> 100）具有决定性的支持作用。我们的结论是导航空间的知识是最好的特点是由一个标记的图形，其中本地度量信息是近似的，几何不一致，而不是嵌入在一个共同的坐标系。此类“认知图”模型支持路线查找、新颖的绕道和粗略的捷径，并有可能统一空间导航的一系列数据。
Humans and other animals build up spatial knowledge of the environment on the basis of visual information and path integration. We compare three hypotheses about the geometry of this knowledge of navigation space: (a) ‘cognitive map’ with metric Euclidean structure and a consistent coordinate system, (b) ‘topological graph’ or network of paths between places, and (c) ‘labelled graph’ incorporating local metric information about path lengths and junction angles. In two experiments, participants walked in a non-Euclidean environment, a virtual hedge maze containing two ‘wormholes’ that visually rotated and teleported them between locations. During training, they learned the metric locations of eight target objects from a ‘home’ location, which were visible individually. During testing, shorter wormhole routes to a target were preferred, and novel shortcuts were directional, contrary to the topological hypothesis. Shortcuts were strongly biased by the wormholes, with mean constant errors of 37° and 41° (45° expected), revealing violations of the metric postulates in spatial knowledge. In addition, shortcuts to targets near wormholes shifted relative to flanking targets, revealing ‘rips’ (86% of cases), 'folds' (91%), and ordinal reversals (66%) in spatial knowledge. Moreover, participants were completely unaware of these geometric inconsistencies, reflecting a surprising insensitivity to Euclidean structure. The probability of the shortcut data under the Euclidean map model and labelled graph model indicated decisive support for the latter (BFGM> 100). We conclude that knowledge of navigation space is best characterized by a labelled graph, in which local metric information is approximate, geometrically inconsistent, and not embedded in a common coordinate system. This class of ‘cognitive graph’ models supports route finding, novel detours, and rough shortcuts, and has the potential to unify a range of data on spatial navigation.