Bibliographic record
Abstract
The monitoring of human affective state is a key part of developing responsive and naturally behaving human-robot interaction systems. However, evaluation and calibration of physiologically monitored affective state data is typically done using offline questionnaires and user reports. This paper investigates the potential to use an on-line device to collect user self reports that can be then used to calibrate physiologically generated affective state data. The collection of on-line calibration data is particularly germane to human-robot interaction where the physiological responses of interest include those related to more high frequency affective state events related to arousal (surprise, fear, alarm) as well as the more low frequency events (contentment, boredom, pleasure). In this context, this paper describes the development of an experimental device, and a preliminary study, to answer the question: Can people report, on-line, two degree of freedom continuous affective states using a hand held device suitable for calibration of physiologically obtained signals? In the following paper, we report on both the device design and user trials. Further work, using the device to calibrate existing models of the user’s affective state during human-robot interaction, is ongoing and will be reported at the time of the conference.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".