This paper reports the results of three studies, each of which investigated the sense of presence within virtual environments as a function of visual display parameters. These factors included the presence or absence of head tracking, the presence or absence of stereoscopic cues, and the geometric field of view used to create the visual image projected on the visual display. In each study, subjects navigated a virtual environment and completed a questionnaire designed to ascertain the level of presence experienced by the participant within the virtual world. Specifically, two aspects of presence were evaluated: (1) the sense of “being there” and (2) the fidelity of the interaction between the virtual environment participant and the virtual world. Not surprisingly, the results of the first and second study indicated that the reported level of presence was significantly higher when head tracking and stereoscopic cues were provided. The results from the third study showed that the geometric field of view used to design the visual display highly influenced the reported level of presence, with more presence associated with a 50 and 90° geometric field of view when compared to a narrower 10° geometric field of view. The results also indicated a significant positive correlation between the reported level of presence and the fidelity of the interaction between the virtual environment participant and the virtual world. Finally, it was shown that the survey questions evaluating several aspects of presence produced reliable responses across questions and studies, indicating that the questionnaire is a useful tool when evaluating presence in virtual environments.
This paper proposes a model of interaction in virtual environments which we term the immersion, presence, performance (IPP) model. This model is based on previous models of immersion and presence proposed by Barfield and colleagues and Slater and colleagues. The IPP model describes the authors' current conceptualization of the effects of display technology, task demands, and attentional resource allocation on immersion, presence, and performance in virtual environments. The IPP model may be useful for developing a theoretical framework for research on presence and for interpreting the results of empirical studies on the sense of presence in virtual environments. The model may also be of interest to designers of virtual environments.
Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of R(adj)(2)), followed by position (28 ± 24% of R(adj)(2)) and speed (11 ± 19% of R(adj)(2)). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower R(adj)(2) values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics.
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