Vladislav Myrov , Elena Gorina , Kristina Vodorezova , Maria Dvoeglazova , Ekaterina Koshmanova , Elena S. Gorbunova , Tadamasa Sawada
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引用次数: 0
Abstract
Within an Ames room, the perceived size of objects, such as people, changes dynamically when the objects move about within the room. The shape of the Ames room is not actually rectangular but it is perceived to be rectangular. Unfortunately, the geometrical properties of the Ames room have often been misunderstood, and rooms that have different shapes are also referred to as “Ames rooms” in many articles. In this study, the geometrical properties of the original Ames rooms constructed by Adelbert Ames, Jr. were analyzed and the generalization of the Ames room was discussed. We found that these original Ames rooms are 3D-to-3D perspective transformations of rectangular illusory rooms. Based on this analysis, we also developed a computational model that can construct a generalized Ames room that has a hexahedral shape with some free parameters that quantitatively control (i) the size and aspect-ratio of a rectangular illusory room, (ii) the amount of distortion of the Ames room from a rectangular room, and (iii) the viewpoint of an observer. This model was implemented as a computational program so that an Ames room can be constructed in a VR space. Note that the transformations of the Ames rooms can be applied to an arbitrary 3D scene and that they can be regarded as members of a subset of 3D-to-3D perspective transformations. Any perspective transformation in this subset distorts the 3D scene in such a way that the retinal image of the distorted scene, when seen from a specific viewpoint, is identical to the retinal image of the initial scene, when seen from a specific viewpoint. These generalizations allow us to control the conditions of an Ames room systematically with more flexibility when we study this illusion.
期刊介绍:
The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome.
Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation.
The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology.
Research Areas include:
• Models for sensation and perception, learning, memory and thinking
• Fundamental measurement and scaling
• Decision making
• Neural modeling and networks
• Psychophysics and signal detection
• Neuropsychological theories
• Psycholinguistics
• Motivational dynamics
• Animal behavior
• Psychometric theory