伦敦的出租车司机利用邻里边界进行分层路线规划
Eva-Maria Griesbauer1, Pablo Fernandez Velasco2, Antoine Coutrot3
1Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, UK; Ordnance Survey Ltd, Southampton, UK.
Cognition
|December 7, 2024
概括
人类规划利用分层路线规划,将环境组织成集群以提高效率. 这项研究验证了在现实场景中使用伦敦出租车司机的等级规划.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 人类规划 人类规划
背景情况:
- 传统的树搜索算法在规划中与大型状态空间作斗争.
- 层次路线规划通过集群环境表示提供了一个计算效率高的替代方案.
- 对等级规划的现有证据仅限于简化,实验性环境.
研究的目的:
- 为了研究人类的现实世界层次规划.
- 用伦敦复杂的城市环境来测试层次规划.
- 测量人类在大规模空间规划方面的专业知识.
主要方法:
- 利用获得许可的伦敦出租车司机的导航记忆.
- 分析了伦敦街道规划的响应时间 (街道名称回忆) (>26,000条街道).
- 与伦敦的区域结构和导航决策 (例如方向变化) 相关联的响应时间.
主要成果:
- 响应时间受到伦敦地区的集群结构的影响,支持层次规划.
- 网络距离对响应时间的影响最小,与树搜索预测形成鲜明对比.
- 在规划过程中改变方向与响应时间的增加有关.
结论:
- 提供了在复杂环境中人类层次规划的现实世界的证据.
- 证明人类在大量状态中使用层次表示来构建规划.
- 提供了一种可量化的衡量人类在大规模空间规划方面的专业知识.
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