A case-study of affect measurement tools for physical user interface design
Bibliographic record
Abstract
Designers of human-computer interfaces often overlook issues of affect. An example illustrating the importance of affective design is the frustration many of us feel when working with a poorly designed computing device. Redesigning such computing interfaces to induce more pleasant user emotional responses would improve the user’s health and productivity. Almost no research has been conducted to explore affective responses in rendered haptic interfaces. In this paper, we describe results and analysis from two user studies as a starting point for future systematic evaluation and design of rendered physical controls. Specifically, we compare and contrast self-report and biometric measurement techniques for two common types of haptic interactions. First, we explore the tactility of real textures such as silk, putty, and acrylic. Second, we explore the kinesthetics of physical control renderings such as friction and inertia. We focus on evaluation methodology, on the premise that good affect evaluation and analysis cycles can be a useful element of the interface designer’s tool palette.
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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 | 0.000 | 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 | 0.000 |
| 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".