Kristin A. Bartlett, Almudena Palacios-Ibáñez, Jorge Dorribo Camba
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引用次数: 0
Abstract
Mental rotation, a common measure of spatial ability, has traditionally been assessed through paper-based instruments like the Mental Rotation Test (MRT) or the Purdue Spatial Visualization Test: Rotations (PSVT:R). The fact that these instruments present 3D shapes in a 2D format devoid of natural cues like shading and perspective likely limits their ability to accurately assess the fundamental skill of mentally rotating 3D shapes. In this paper, we describe the Virtual Reality Mental Rotation Assessment (VRMRA), a virtual reality-based mental rotation assessment derived from the Revised PSVT:R and MRT. The VRMRA reimagines traditional mental rotation assessments in a room-scale virtual environment and uses hand-tracking and elements of gamification in attempts to create an intuitive, engaging experience for test-takers. To validate the instrument, we compared response patterns in the VRMRA with patterns observed on the MRT and Revised PSVT:R. For the PSVT:R-type questions, items requiring a rotation around two axes were significantly harder than items requiring rotations around a single axis in the VRMRA, which is not the case in the Revised PSVT:R. For the MRT-type questions in the VRMRA, a moderate negative correlation was found between the degree of rotation in the X direction and item difficulty. While the problem of occlusion was reduced, features of the shapes and distractors accounted for 50.6% of the variance in item difficulty. Results suggest that the VRMRA is likely a more accurate tool to assess mental rotation ability in comparison to traditional instruments which present the stimuli through 2D media. Our findings also point to potential problems with the fundamental designs of the Revised PSVT:R and MRT question formats.
期刊介绍:
ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields.
The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.