Naming emotions in motion: Alexithymic traits impact the perception of implied motion in facial displays of affect.
Bibliographic record
Abstract
Something akin to motion perception occurs when actual motion is not present but implied. However, it is not known if the experience of implied motion occurs during the perception of static faces nor if the effect would vary for different facial expressions. To examine this, participants were presented with pairs of faces where successive expressions depicted either increasing emotional intensity or its diminution. Participants indicated if the second face in the pair was the same as, or different from, the first face shown. To measure general facial emotion recognition ability, the Ekman 60 faces test was administered. As individual differences in depression, anxiety, and alexithymia have been shown to influence face processing, we measured these factors using the Hospital Anxiety and Depression scale (HADS) and the Toronto Alexithymia scale (TAS-20). As expected, participants were more likely to endorse the second face as being a match to the first when its expression implied forward motion compared to backward motion. This effect was larger for happiness and fear and positively related to accuracy on the Ekman 60 faces task. The effect was not related to depression or anxiety but it was negatively related to scores on the difficulty identifying feelings subscale of the TAS-20, suggesting that individuals who have problems identifying their own and others' feelings experienced a reduction in implied motion. Observers process implied motion from some facial expressions of emotion but the experience is modified by the ability to recognize one's own feelings and those of others. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".