FEELing (key)Pressed: Implicit Touch Pressure Bests Brain Activity for Modeling Emotion Dynamics in the Space Between Stressed & Relaxed
Why this work is in the frame
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Bibliographic record
Abstract
In-body lived emotional experiences can be complex, with time-varying and dissonant emotions evolving simultaneously; devices responding in real-time to estimate personal human emotion should evolve accordingly. Models assuming generalized emotions exist as discrete states fail to operationalize valuable information inherent in the dynamic and individualistic nature of human emotions. Our multi-resolution emotion self-reporting procedure allows the construction of emotion labels along the Stressed-Relaxed scale, differentiating not only what the emotions are, but how they are transitioning - e.g., "hopeful but getting stressed" vs. "hopeful and starting to relax". We trained participant-dependent hierarchical models of contextualized individual experience to compare emotion classification by modality (brain activity and keypress force from a physical keyboard), then benchmarked classification performance at F1-scores = [0.44, 0.82] (chance F1=0.22, σ = 0.01) and examined high-performing features. Notably, when classifying emotion evolution in the context of an experience that realistically varies in stress, pressure-based features from keypress force proved to be the more informative modality, and more convenient when considering intrusiveness and ease of collection and processing. Finally, we present our FEEL (Force, EEG and Emotion-Labelled) dataset, a collection of brain activity and keypress force data, labelled with self-reported emotion collected during tense videogame play (N = 16) and open-sourced for community exploration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it