Studying Attraction Power in Proxemics-Based Visual Concepts for Large Public Interactive Displays
Bibliographic record
Abstract
A key challenge in designing interfaces for large interactive displays deployed in public settings is to draw (and keep) a passerby's attention. Proxemic interactions--a design approach that applies human spatial behavior to guide system behavior in response to a user's proximity to a display--has been proposed for attracting and engaging potential users. Yet, the effectiveness of this approach has not been evaluated. Moreover, little research exists in the broader literature on the relative efficacy of possible visual design strategies to attract and engage large display users. We conducted a study to the effectiveness of promising visual concepts applied in a proxemic interactions framework: content motion and user shadows. While both visual concepts were more effective than a control condition at capturing attention, the inclusion of user's shadow was found to have stronger attraction power than content motion alone. In contrast, they were found to be ineffective for communicating possible user interactions in the display, limiting their potential to facilitate further system use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".