Haptic Processing of Facial Expressions of Emotion in 2D Raised-Line Drawings
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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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 | 0.000 | 0.005 |
| 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) | 0.003 | 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 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".