Sliders Rate Valence but not Arousal: Psychometrics of Self-Reported Emotion Assessment
Bibliographic record
Abstract
Emotional reactions are increasingly recognized as an important part of experiences with technology, and there is a need for rigorous investigation into the collection of self-reported emotional data. We examine the capture of continuous, quantitative, affective self-reports as a complement to existing methods of evaluating human-system or product interaction. This experiment investigated 12 participants' use of a single slider (for valence, from very negative to very positive) and two sliders (for valence and arousal) in response to approximately 45 minutes of a nature video. Individual differences and physiological data (heart rate variability and skin conductance) were recorded. Emotion ratings were significantly related to skin conductance, which both differed significantly across chapters with different video content. We observed a learning effect, where participants' response times to probe questions decreased across blocks. Cognitive load appeared higher in the two-slider condition, with a possibly larger learning effect, and significantly longer dwell times, when compared to one slider. Arousal self-ratings were contradicted by skin conductance measures. We conclude with recommendations concerning the use of sliders for assessment of emotional user experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".