https://orcid.org/0000-0001-6097-7946
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Abstract
Finding one’s way is a fundamental daily activity and has been widely studied in the field of geospatial cognition. Immersive virtual reality (iVR) techniques provide new approaches for investigating wayfinding behavior and spatial knowledge acquisition. It is currently unclear, however, how wayfinding behavior and spatial knowledge acquisition in iVR differ from those in real-world environments (REs). We conducted an RE wayfinding experiment with twenty-five participants who performed a series of tasks. We then conducted an iVR experiment using the same experimental design with forty participants who completed the same tasks. Participants’ eye movements were recorded in both experiments. In addition, verbal reports and postexperiment questionnaires were collected as supplementary data. The results revealed that individuals’ wayfinding performance is largely the same between the two environments, whereas their visual attention exhibited significant differences. Participants processed visual information more efficiently in RE but searched visual information more efficiently in iVR. For spatial knowledge acquisition, participants’ distance estimation was more accurate in iVR compared with RE. Participants’ direction estimation and sketch map results were not significantly different, however. This empirical evidence regarding the ecological validity of iVR might encourage further studies of the benefits of VR techniques in geospatial cognition research.
寻路是一项基本的日常活动, 在地理空间认知领域受到广泛的研究。沉浸式虚拟现实（iVR）技术, 为研究寻路行为和空间知识获取, 提供了新的途径。然而, 关于iVR和现实环境（RE）的寻路行为和空间知识获取的不同之处, 目前我们并不了解。我们对25名参与者进行了RE寻路实验, 参与者完成了一系列的任务。然后, 使用相同的实验设计, 我们进行了iVR实验, 40名参与者完成相同的任务。两个实验都记录了参与者眼睛的活动。此外, 作为补充数据, 本文还收集了口头报告和实验后问卷。结果显示, 在两种环境中, 个人的寻路表现基本相同, 而视觉关注有显著差异。在RE中, 实验参与者处理视觉信息的效率更高, 而在iVR中搜索视觉信息的效率更高。在空间知识获取方面, iVR中的距离估计比RE更准确。然而, 参与者的方向估计和示意图结果没有显著差异。本文提供了iVR生态有效性的经验证据, 可能促使我们进一步研究虚拟现实技术在地理空间认知研究中的作用。
Hallar la manera de orientarse es una actividad cotidiana fundamental, la cual ha sido ampliamente estudiada en el campo de la cognición geoespacial. Las técnicas de sumersión en la realidad virtual (iVR) proveen enfoques nuevos para investigar el comportamiento de orientarse y sobre adquisición de conocimiento espacial. Sin embargo, hasta el momento no es claro cómo difieren el comportamiento de orientación y la adquisición de conocimiento espacial en las iVR de lo que ocurre al respecto en entornos del mundo real (REs). Llevamos a cabo un experimento RE de orientación con veinticinco participantes que desempeñaban una serie de tareas. Después, condujimos un experimento iVR aplicando el mismo diseño experimental, con cuarenta participantes que completaron las mismas tareas. Los movimientos de los ojos de los participantes se registraron en ambos experimentos. Además, a manera de datos complementarios, se recogieron informes verbales y cuestionarios administrados después de los experimentos. Los resultados revelan que el desempeño de los individuos para orientarse en gran medida es el mismo en los dos experimentos, en tanto que su atención visual exhibió diferencias significativas. Los participantes procesaron información visual más eficientemente en RE, aunque la búsqueda de esa información visual se cumplió más eficientemente en iVR. En cuanto a la adquisición de conocimiento espacial, la estimación de la distancia por los participantes fue más exacta en iVR en comparación con RE. Sin embargo, el cálculo de la dirección por los participantes y los resultados del bosquejo del mapa no fueron significativamente diferentes. Esta evidencia empírica en relación con la validez ecológica de iVR podría estimular estudios adicionales de los beneficios de las técnicas VR en investigación sobre cognición geoespacial.
Supplemental Material
Supplemental material for this article, which includes ten tables that provide additional experimental information and data and one figure that shows the test of mental rotation of three-dimensional objects, is available on the publisher’s Web site at https://dx.doi.org/10.1080/24694452.2021.1894088.
Acknowledgments
The authors thank all participants in this experiment and all reviewers for their helpful comments and suggestions. We thank Benjamin Knight, MSc, from Edanz Group (https://en-author-services.edanzgroup.com/ac) for editing a draft of this article.
Additional information
Funding
Notes on contributors
Weihua Dong
WEIHUA DONG is a Professor in the Faculty of Geographical Science at Beijing Normal University, Beijing 100875, China. E-mail: [email protected]. His research interests include GIScience and spatial cognition.
Tong Qin
TONG QIN is a Master’s Candidate in the Faculty of Geographical Science at Beijing Normal University, Beijing 100875, China. E-mail: [email protected]. His research interests include spatial cognition and pedestrian wayfinding or navigation.
Tianyu Yang
TIANYU YANG is a Research Assistant in the Faculty of Geographical Science at Beijing Normal University, Beijing 100875, China. E-mail: [email protected]. His research interests include geospatial cognition and cartographic visualization.
Hua Liao
HUA LIAO is a Lecturer in the College of Resources and Environmental Sciences at Hunan Normal University, Changsha, Hunan Province 410081, China. E-mail: [email protected]. His research interests include geovisualization with a focus on cognitive issues, map interaction based on eye tracking, and pedestrian navigation in virtual and real environments.
Bing Liu
BING LIU is a PhD Candidate in the Department of Cartography at Technical University of Munich, Munich 80333, Germany. E-mail: [email protected]. Her research interests include spatial learning and user experience with extended reality (XR) and development of spatial ability.
Liqiu Meng
LIQIU MENG is a Professor of Cartography at the Technical University of Munich, Munich 80333, Germany. E-mail: [email protected]. Her research interests include geodata integration, mobile map services, multimodal navigation algorithms, geovisual analytics, and ethical concerns in social sensing.
Yu Liu
YU LIU is a Professor of GIScience in the Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China. E-mail: [email protected]. His research concentration is in GIScience and big geodata.
