Leveraging Pseudo-haptic Interaction to Enhance User Experience: A Case of Interactive Aerial Displays

1. Research Hypotheses:
This dataset was collected to test the hypothesis that pseudo-haptic visual feedback (specifically, Press, Hit, and Block effects) can effectively compensate for the lack of tactile feedback in interactive aerial displays, thereby significantly enhancing the subjective user experience and interaction performance.

2. Data Content and Collection:
The data originates from a within-subjects laboratory experiment (N=23). Participants interacted with eight different mid-air button designs, which were combinations of three binary pseudo-haptic features (Press: On/Off, Hit: On/Off, Block: On/Off). The dataset includes:

Demographics: Age and gender of participants.

Subjective Ratings: Participant responses on a 7-point Likert scale across four dimensions: Perception of Haptic Feedback, Naturalness of Interaction Logic, Visual-Spatial Immersion, and Spatio-Temporal Perception Accuracy.

User Experience Metrics: Scores from the short version of the User Experience Questionnaire (UEQ-S), measuring Pragmatic Quality and Hedonic Quality.

Preference Data: Participants' stated preferences for the different pseudo-haptic features and their most favored button design.

3. Key Findings and Interpretation:

The Press effect had the largest positive main effect, significantly improving all measured aspects of user experience. It is interpreted as the foundational element for simulating physicality.

The Hit effect significantly boosted immersion and hedonic quality, functioning as a strong operational confirmation signal.

The Block effect enhanced the haptic illusion but negatively impacted spatial perception accuracy, indicating a trade-off between realism and spatial clarity.

A significant three-way interaction confirmed that the combination of all three effects (Press+Hit+Block) yielded the optimal overall user experience.

4. Reuse and Interpretation:
This dataset is suitable for researchers working in human-computer interaction, particularly in areas of pseudo-haptics, mid-air interaction, and user experience evaluation. It can be used for meta-analysis, to validate computational models of user experience, or for educational purposes. The primary file (primary_data.csv) contains the raw scores, and the codebook.pdf provides a detailed description of all variables, their scales, and the experimental design for accurate interpretation. Statistical analysis should employ repeated-measures ANOVA to account for the within-subjects design.