Measuring Emotional Responses to Interaction: Evaluation of Sliders and Physiological Reactions
Bibliographic record
Abstract
Recent work has proposed sliders as a useful way to measure self-reported emotion continuously. My dissertation extends this work to ask: what are relevant properties of affective self-report on sliders and variations? How reliable are affective self-reports? How do they relate to physiological data? What are individual and cultural differences? How can this method be applied to ehealth? Three emotion self-report tools (one-slider, two-slider, a touchscreen) were developed and evaluated in four experiments. The first experiment was within-subjects. Participants viewed short videos, with four self-report conditions (including no reporting) and physiological capture (heart rate variability and skin conductance). In a re-rating task, the sliders models were found to be more reliable than the touchscreen (Lottridge & Chignell, 2009a). The second and third experiments were between-subjects, and examined individual and cultural differences. Canadian and Japanese participants watched a nature video, while rating emotions and answering questions. Analyses were carried out within and across the datasets. Larger operation span displayed a minor benefit. Valence and arousal ratings were not strongly related to skin conductance. The Japanese performed on par with Canadians but reported worse performance. Based on the results, the recommendation was made that a single slider be used to rate valence, that arousal be estimated with skin conductance, and that slider psychometrics be used to assess cognitive load over time. In the fourth experiment, diabetic participants watched Diabetes-related videos. They clustered into usage patterns: some moved the slider very little during videos and more afterward, some hardly moved the slider, and some used it as expected. Two novel metrics facilitated these analyses: Emotional Bandwidth, an application of information entropy that characterizes the granularity of the self reports (Lottridge & Chignell, 2009b) and Emotional Majority Agreement, the amount of agreement relative to a sample’s self-reports (Lottridge & Chignell, 2009c). In summary, this dissertation contributes a method of measuring emotion through sliders and skin conductance that has been evaluated in a number of experimental studies. It contributes the empirical results, design recommendations, and two novel metrics of emotional response. Limitations and implications for future research and practice are also discussed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".