Real Emotions Don't Stand Still: Toward Ecologically Viable Representation of Affective Interaction
Bibliographic record
Abstract
To create emotionally expressive robots, designers of human-robot interaction routinely translate emotion theories into instruments through which we estimate, quantify and analyze human emotional responses to robot behaviour. Pragmatically, we often use straightforward models such as Russell's circumplex, treating emotion as a single point in a two-dimensional space. However, this simple metaphor and its consequent representations omit many aspects of real emotional experience, can lead to erroneous data and may undermine computational models that rely on them. Problems with emotion representations currently prevalent in human-robot interaction fall into three categories: (1)Representations are static and singular, whereas real emotions can be dynamic, multi-valued, uncertain or conflicting. (2)The framing of an interaction is unspecified (i.e., in an affective rating task: which part of an interaction involving multiple parties and perspectives the participant is meant to consider). (3) Participant responses captured with instruments and methods that are not well-understood by experimenters nor participants produce data that is hard to interpret. We propose alternative emotion representations to account for dynamic emotions inherent in interactive contexts; scrutinize framing ambiguities in study tasks and argue for mixed-methods approaches to achieve shared understanding of emotion representations between participants and researchers.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".