Constructing a valid measure of presence and discovering the factors that contribute to presence have been much sought after goals of presence researchers and at times have generated controversy among them. This paper describes the results of principal-components analyses of Presence Questionnaire (PQ) data from 325 participants following exposure to immersive virtual environments. The analyses suggest that a 4-factor model provides the best fit to our data. The factors are Involvement, Adaptation/Immersion, Sensory Fidelity, and Interface Quality. Except for the Adaptation/Immersion factor, these factors corresponded to those identified in a cluster analysis of data from an earlier version of the questionnaire. The existence of an Adaptation/Immersion factor leads us to postulate that immersion is greater for those individuals who rapidly and easily adapt to the virtual environment. The magnitudes of the correlations among the factors indicate moderately strong relationships among the 4 factors. Within these relationships, Sensory Fidelity items seem to be more closely related to Involvement, whereas Interface Quality items appear to be more closely related to Adaptation/Immersion, even though there is a moderately strong relationship between the Involvement and Adaptation/Immersion factors.

Virtual environments (VEs) have been used successfully to train wayfinders to navigate through buildings and learn their layout. However, at the same time, for many, the VE deficiencies have reduced the effectiveness of VEs for training spatial tasks. In an effort to improve VE effectiveness, we conducted research to determine if certain unique capabilities of VEs could compensate for its deficiencies. Research participants were required to learn the layout or configuration of one floor of an office building as portrayed in a VE. To improve spatial learning, we developed three VE navigation training aids: local and global orientation cues, aerial views, and a themed environment enhanced with sights and sounds and divided into four distinct sectors. The navigation aids were provided during the training but were not available during testing of survey knowledge. Of the three training aids investigated, only the aerial views were effective in improving performance on the survey knowledge tests. The effectiveness of the navigation aids seemed to depend on how they were used during training. A retention test given one week after training indicated that spatial knowledge acquired in a VE diminished little over the one-week retention interval.

In an article that discussed both the conceptual aspects of presence and the practical considerations of measuring the concept (Witmer & Singer, 1998), we argued that both involvement and immersion are necessary for experiencing presence. The article also presented two questionnaires we have developed, an Immersive Tendencies Questionnaire (ITQ) and a Presence Questionnaire (PQ). Our analyses showed that they are internally consistent with high reliability, there is a weak but consistent positive relation between presence and performance, the ITQ predicts presence as measured by the PQ, and individuals reporting more simulator sickness symptoms report less presence than those reporting fewer symptoms. In this issue, Slater (1999) critiques our approach to measuring presence as represented by the PQ. Dr. Slater finds our definitions of presence helpful and that our concept of immersion is part of his understanding of the meaning of presence. Dr. Slater then argues that both our approach to measuring presence and the PQ are conceptually flawed. In his critique, he raises statistical questions about our measure, concluding that the PQ is not a measure of presence at all. He concludes by arguing against the validity of the measure and stating that he would not use the PQ in his research. In this article, we argue that the PQ is based in the same conceptual structure that is accepted by Dr. Slater, and that the PQ represents a fundamentally sound approach—although not the only approach—to measuring presence. Dr. Slater's statistical arguments are shown to be incorrect simplifications, as he acknowledges in his critique. We also demonstrate that individual PQ items still correlate significantly with the PQ total score even when the PQ total is adjusted to remove the score on individual items from that total. Finally, we rebut Slater's argument concerning the validity of the measure, and suggest that researchers not be constrained by an equipment-oriented model of the presence experience.

The effectiveness of virtual environments (VEs) has often been linked to the sense of presence reported by users of those VEs. ( Presence is defined as the subjective experience of being in one place or environment, even when one is physically situated in another.) We believe that presence is a normal awareness phenomenon that requires directed attention and is based in the interaction between sensory stimulation, environmental factors that encourage involvement and enable immersion, and internal tendencies to become involved. Factors believed to underlie presence were described in the premier issue of Presence: Teleoperators and Virtual Environments. We used these factors and others as the basis for a presence questionnaire (PQ) to measure presence in VEs. In addition we developed an immersive tendencies questionnaire (ITQ) to measure differences in the tendencies of individuals to experience presence. These questionnaires are being used to evaluate relationships among reported presence and other research variables. Combined results from four experiments lead to the following conclusions: the PQ and ITQ are internally consistent measures with high reliability; there is a weak but consistent positive relation between presence and task performance in VEs; individual tendencies as measured by the ITQ predict presence as measured by the PQ; and individuals who report more simulator sickness symptoms in VE report less presence than those who report fewer symptoms.

The ability to accurately estimate distance is an essential component of navigating large-scale spaces. Although the factors that influence distance estimation have been a topic of research in real-world environments for decades and are well known, research on distance estimation in virtual environments (VEs) has only just begun. Initial investigations of distance estimation in VEs suggest that observers are less accurate in estimating distance in VEs than in the real world (Lampton et al., 1995). Factors influencing distance estimates may be divided into those affecting perceived distance (visual cues only) and those affecting traversed distance to include visual, cognitive, and proprioceptive cues. To assess the contribution of the various distance cues in VEs, two experiments were conducted. The first required a static observer to estimate the distance to a cylinder placed at various points along a 130-foot hallway. This experiment examined the effects of floor texture, floor pattern, and object size on distance estimates in a VE. The second experiment required a moving observer to estimate route segment distances and total route distances along four routes, each totaling 1210 feet. This experiment assessed the effects of movement method, movement speed, compensatory cues, and wall texture density. Results indicate that observers underestimate distances both in VEs and in the real world, but the underestimates are more extreme in VEs. Texture did not reliably affect the distance estimates, providing no compensation for the gross underestimates of distance in VE. Traversing a distance improves the ability to estimate that distance, but more natural means of moving via a treadmill do not necessarily improve distance estimates over traditional methods of moving in VE (e.g., using a joystick). The addition of compensatory cues (tone every 10 feet traversed on alternate route segments) improves VE distance estimation to almost perfect performance.