Alexithymia and the processing of emotional scenes depicting implied motion
Bibliographic record
Abstract
Cognitive alexithymia is associated with impairments in identifying, verbalizing, and analyzing (particularly negative) emotions, which may be rooted in atypical hemispheric laterality and/or stimulus properties. We investigated how alexithymic traits affected processing of emotional scenes using a laterality task in which participants judged the pleasantness of 120 images presented in the presence or absence of a peripheral distractor. Half of the scenes depicted implied motion. Right-handed adults (N = 106) were classified as exhibiting low, moderate, or high levels of cognitive alexithymia using scores from the Toronto Alexithymia Scale – 20 (Parker et al., 2003). Contrary to expectations, no laterality effects were observed. Participants made correct judgments more quickly when a peripheral distractor was presented with the scene, F(2, 103) = 90.43, p < .001, ηp2 = .468. Participants were also slower at correctly judging negatively valenced scenes, F(1, 103) = 6.50, p = .012, ηp2 = .059, and those that contained implied motion, F(1, 103) = 6.04, p = .016, ηp2 = .055, suggesting that these scenes were difficult to process. Accuracy was higher for positive scenes with no implied motion than for other types of scenes, F(1, 103) = 32.93, p < .001, ηp2 = .242. Finally, participants with low levels of alexithymia showed better accuracy for positive scenes but no effect of implied motion, whereas those with moderate-to-high alexithymia showed the opposite pattern [Group X Motion: F(2, 103) = 4.03, p = .02, ηp2 = .073; Group X Valence: F(2, 103) = 3.03, p = .05, ηp2 = .056]. Overall, our results show that stimulus properties, such as implied motion and valence, influence how effectively people with varying levels of cognitive alexithymia process emotional information. These findings provide new insights into the nature of alexithymic deficits and into the functioning of the social brain more generally. Meeting abstract presented at VSS 2018
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".