Alexithymic traits impact the perception of implied motion in facial displays of affect.
Bibliographic record
Abstract
Something akin to motion perception occurs even when actual motion is not present but merely implied. However, it is not known if the experience of implied motion occurs during the perception of faces or even different affects. Moreover, it is not known if implied motion is moderated by individual differences in anxiety, depression or alexithymia. To examine this, participants were presented with picture pairs showing facial affect that implied either a forward or backward motion, i.e., depicting an increasing intensity in affect, or its diminution. Participants indicated whether or not the second face in the pair was the same as, or different from, the first face shown. To measure general affect recognition ability the Ekman 60 faces test was administered, as were the Toronto Alexithymia scale (TAS-20) and the Hospital Anxiety and Depression scale (HADS). Analysis of error rates revealed significant main effects for direction and emotion. There was no significant correlation with overall HADS score for any of the six emotions; there was also no effect of depression, anxiety or general face recognition abilities. Interestingly, the number of errors in the forward condition was negatively related to scores on the difficulty identifying feelings subscale of the TAS-20, which suggests that individuals who have problems identifying their own and others’ feelings had experienced a reduction in the experience of implied motion. Results suggest that implied motion may influence the experience of affect recognition and can be applied to clinical groups, specifically those demonstrating deficits in correctly recognising salient social cues.
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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 | 0.002 |
| 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".