Academic journal article Attention, Perception and Psychophysics

VR-MDS: Multidimensional Scaling for Classification Tasks of Virtual and Real Stimuli

Academic journal article Attention, Perception and Psychophysics

VR-MDS: Multidimensional Scaling for Classification Tasks of Virtual and Real Stimuli

Article excerpt

Published online: 9 January 2014

# Psychonomic Society, Inc. 2014

Abstract Evaluating the perceptual similarity between virtual and real sensory stimuli has been a serious problem for virtual reality interface researchers for a long time. One of the most commonly used evaluation methods is a classification task where assessors classify randomly presented stimuli into multiple candidate types. The results of this method are summarized using two types of confusion matrices, which have different stimulus sets. The present study developed a method that computes the locations of simulated and real stimuli in a perceptual space on the basis of the two confusion matrices. The spatial distribution of the stimuli allows us to visually interpret the perceptual relationships between stimuli and their perceptual dimensionality. This method is recommended when the guidance index based on the answer ratios of the confusion matrices is fairly high.

Keywords Math modeling . Similarity . Statistics

(ProQuest: ... denotes formulae omitted.)

The universal aim of virtual reality technology is to design simulated stimuli that resemble real stimuli. The perceptual quality of the stimuli is a primary concern for researchers and users of simulators. For example, considerable effort has been devoted to representing the qualities of real tumors in medical simulators used to train physicians during palpations (Jeon, Choi, & Harders, 2012; Karadogan & Williams, 2013; Ottermo, 2006). Neuroscientists and psychologists have used graphic simulators to obtain pictures of objects under various lighting and material conditions (Motoyoshi & Matoba, 2012; Nishio, Goda, & Komatsu, 2012). Several studies have investigated the perceptual qualities of stimuli in other types of simulators-for example, sounds (Giordano et al., 2012), 3-D pictures (Mai, Doutre, Nasiopoulos, & Ward, 2012), and haptic stimuli (Okamura, Cutkosky, & Dennerlein, 2001).

The methodologies used to assess the perceptual properties of these stimuli are of crucial importance. Various methods have been used for this purpose, such as comparing the similarities of physical quantities in virtual and real stimuli-for example, force or acceleration (Kuchenbecker &Niemeyer,2006; Okamura, Webster, Nolin, Johnson, & Jafry, 2003). However, physical similarities are not sufficient, and researchers often make use of evaluations based on perceptual similarity.

In some cases, assessors have evaluated the degree of the subjective quality of sensory feedback using numerical or graded scales (Dev, Harris, Gutierrez, Shah, & Senger, 2002; Hikichi, Yasuda, Fukuda, & Sezaki, 2006). Trained assessors rate the integrated similarity between the percepts of real and virtual stimuli. Alternatively, they may experience only the virtual stimuli and rate them. The latter approach is used widely for audiovisual content (International Telecommunication Union, 2009).

In other cases, the similarities between virtual and real stimuli are evaluated using specific perceptual criteria (Fujita &Ohmori,2001; Konyo, Tadokoro, & Takamori, 2000; Watanabe & Fukui, 1995). For example, the assessors may compare real and virtual tumors only in terms of their compliances or locations beneath tissues.

For virtual reality simulators used for manufacturing or medical tasks, as well as discussions of the quality of specific stimuli such as tumors or reaction forces delivered via a tool grip, we need to consider the comprehensive qualities of the tasks experienced by trainees during trials. Questionnaires or behavioral indices are preferred in these cases. Questionnaire-based reality evaluations (e.g., Hendrix & Barfield, 1996;van Baren & IJsselstejin, 2004; Witmer & Singer, 1998)typically involve questions related to the naturalness of the environment, the difficulties of tasks, and so forth.

Behavioral approaches use task performance indices such as the time required to finish a given task or the accuracy of task performance (Fukumoto & Sugimura, 2001; Gomoll, Pappas, Forsythe, & Warner, 2008; Howe, Peine, Kontarinis, &Son,1995; Kontarinis & Howe, 1995; Pedowitz, Esch, & Snyder, 2002). …

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