To robustly measure spatial presence in an experiment where participants experience flying through a visible tunnel of varying extensions (length/width) and at different traversal speeds, it is essential to deploy a measurement framework that distinguishes the subjective feeling of being located within the tunnel from general perceptual processing or task performance. Below, I outline evidence-based recommendations anchored in the development and theoretical advancements in spatial presence research.
1. Theoretical Grounding: What is Spatial Presence?
Spatial presence, according to the most contemporary and theoretically coherent models, is the cognitive feeling that arises when unconscious spatial-cognitive processes present feedback to conscious experience, resulting in the sense of being “there” within a mediated space rather than merely observing it[1][2]. This feeling is distinct from spatial perception (such as distance or extension estimation) or immersion, underscoring the necessity for measurement tools that align closely with this cognitive feedback loop[1][2][3].
2. Self-Report Measurement: Direct Assessment of Spatial Presence
Self-report questionnaires are the gold standard for capturing the subjective and phenomenological quality of presence, a practice well-justified by both theory and empirical validation[1][4][2][5][3][6]. They are particularly well-suited for experimental comparisons (e.g., tunnel extension, speed) within a single type of mediated environment.
A. Main Instruments
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Presence Questionnaire (PQ): Widely validated for virtual environments, the PQ directly measures the user’s subjective sense of presence[4][5]. Key subscales include Involvement, Sensory Fidelity, and notably, Adaptation/Immersion—relevant for assessing how users adapt to the felt location within varying tunnel contexts.
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Spatial Presence Experience Scale (SPES): Specifically constructed to tap the sense of being spatially located (“self-location”) in a mediated environment[6]. Items focus directly on the psychological state of feeling surrounded by the environment.
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Pictorial Presence Self-Assessment Manikin (SAM): This nonverbal, intuitive instrument measures self-location and agency/action possibilities, and has been shown to be sensitive and efficient in assessing spatial presence, even when language could introduce ambiguity or fatigue[3].
B. Implementation in the Tunnel Paradigm
After each traversal condition (combination of tunnel extension and speed), instruct participants using language that cues their experience of “being there,” e.g.:
“Please indicate how strongly you felt as though you were truly inside the tunnel during the journey, rather than just observing it on a screen.”
(Use a 7-point scale: 1 = not at all, 7 = completely.)
Supplement this with several items from validated scales, such as:
- “It seemed to me that I was really flying through the tunnel.”
- “I felt present in the tunnel environment.”
- “I felt like I could reach out and touch the tunnel walls.”
Utilize the PQ, SPES, or pictorial SAM for repeated, fast administration across conditions[4][3][6].
3. Behavioral and Cognitive Measures: Indirect Indices of Presence
While self-report captures subjective presence, behavioral measures can provide convergent evidence for spatial engagement, as theorized by Wirth et al.'s process model in which spatial situation models facilitate presence[2].
A. Distance and Direction Estimation
After each tunnel traversal, ask participants to estimate:
- How far they traveled (match to actual tunnel length to assess veridicality).
- The direction or location of their starting point (utilizing a joystick or touchscreen).
Enhanced accuracy or confidence in these tasks may correlate with elevated presence, though these are indirect proxies, as they measure embodied action potential and spatial updating[7][2].
B. Navigation Memory or Tunnel Reproduction
Request that participants “draw” or recreate the tunnel’s path or dimensions, as richer, presence-linked “spatial situation models” should improve performance[2].
4. Brief Manipulation Checks and Control Measures
To confirm that participants are attending to the manipulations (tunnel length, speed), include manipulation check items post-trial:
- “Was the tunnel long or short?”
- “Did you feel you were traveling quickly or slowly?”
This ensures that self-reported presence is being assessed against perceived, not merely programmed, differences in stimulus parameters[2].
5. Potential for Complementary Physiological or Affective Measures
While self-report remains primary, greater presence often—though not always—tracks with emotional or physiological arousal[8][9]. Heart rate variability or skin conductance responses during traversal (especially in high-speed or narrow tunnels) could provide secondary indices, but must be interpreted cautiously due to possible confounds with anxiety or cybersickness[8][9]. This is particularly relevant if variations in speed and extension increase or decrease user engagement or tension.
6. Experimental Design and Data Analysis
Adopt a within-subjects factorial design, with each participant experiencing all combinations of tunnel extension and speed, counterbalancing order to control for fatigue or adaptation[2]. Analyze whether subjective presence scores differ across these conditions using repeated-measures ANOVA or mixed-effects models, and where relevant, test correlations between self-report presence scores and behavioral performance or physiological indices.
Summary Table: Measurement Strategy
In summary, measuring spatial presence in a tunnel-flight paradigm with variations in extension and speed is most decisively achieved through theoretically grounded self-report questionnaires (PQ, SPES, Pictorial SAM), tailored item wording to your spatial context, and supported by behavioral tasks that probe spatial updating and memory. Manipulation checks ensure experimental validity, and physiological measures offer convergent but secondary insight. This multi-method approach is both justified by—and advances—the current science of spatial presence measurement[1][4][2][5][3][6][7][8][9].