The sense of spatial presence in virtual environments (VEs) is typically measured using self-report methods after task completion. Recent years have seen efforts to measure presence via physiological markers, with mixed results. Behavioral indicators might better track variations in presence over time. We propose finger tapping as an indicator of changing attentional demands corresponding to low and high presence conditions in VEs. Participants completed “virtual plank” (raised vs lowered to reflect high or low presence) and VR video (90- vs 360-degree) conditions while attempting to tap their fingers at a specified rate. Participants wore a data glove to measure finger taps, and we analyzed variations in the inter-tap intervals. We trained a neural net classifier to distinguish between low and high presence conditions using features derived from the positional finger-tap timeseries (e.g., power spectral density, autocorrelation). The trained network classified test epochs with a success rate of 80% for both video and plank conditions.

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