Haptic Processing of Facial Expressions of Emotion in 2D Raised-Line Drawings
Why this work is in the frame
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Bibliographic record
Abstract
Participants haptically (vs. visually) classified universal facial expressions of emotion (FEEs) depicted in simple 2D raised-line displays. Experiments 1 and 2 established that haptic classification was well above chance; face-inversion effects further indicated that the upright orientation was privileged. Experiment 2 added a third condition in which the normal configuration of the upright features was spatially scrambled. Results confirmed that configural processing played a critical role, since upright FEEs were classified more accurately and confidently than either scrambled or inverted FEEs, which did not differ. Because accuracy in both scrambled and inverted conditions was above chance, feature processing also played a role, as confirmed by commonalities across confusions for upright, inverted, and scrambled faces. Experiment 3 required participants to visually and haptically assign emotional valence (positive/negative) and magnitude to upright and inverted 2-D FEE displays. While emotional magnitude could be assigned using either modality, haptic presentation led to more variable valence judgments. We also documented a new face-inversion effect for emotional valence visually, but not haptically. These results suggest emotions can be interpreted from 2-D displays presented haptically as well as visually; however, emotional impact is judged more reliably by vision than by touch. Potential applications of this work are also considered.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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