A laboratory-based study methodology to investigate attraction power of large public interactive displays
Bibliographic record
Abstract
A known challenge of designing large public interactive displays is to create an interface that attracts a passerby's attention and communicates its interactivity. However, typical "in-the-wild" field study methods of assessing public display design solutions require costly system implementation and deployment, creating challenges for assessing early stage design concepts. Such studies also limit the amount of experimental control researchers have over the environment, limiting the precision of results. To address these issues, we developed a complementary laboratory-based study methodology that employs experimental deception to assess the ability of an interface design solution to attract a passerby's attention. Our methodology enables more rigorous control of confounding factors, study of early-stage prototypes, and requires minimal setup. We used this methodology to assess existing visual design solutions for drawing attention and enticing interaction, compare our results to previous studies, and reflect on the benefits and limitations of this assessment approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".