OVER-RELIANCE ON THE MOUTH AREA IN THE VISUAL SCANPATHS ARE ALSO OBSERVED WITH OLDER EMOTIONAL FACE
Bibliographic record
Abstract
Previous studies have shown that aging is associated with a reduced accuracy at recognizing some facial expressions (Calder et al., 2003; West et al., 2012). It has been proposed that this deficit is linked with altered visual scanpaths: elderly fixate more the mouth area than younger participants (Wong et al., 2005). However, these results were obtained using pictures of young individuals’ face instead of individuals of the same age group as the participants. This study therefore compared the visual scanpaths of older (N=31; Mage=71.8) and younger adults (N=31; Mage=22.6) during the recognition of facial emotions displayed by young and older faces (five identities each). The task consisted in categorizing the six basic emotions, while eye movement were recorded. Accuracy scores were calculated for each expression and stimulus age. A repeated-measures ANOVA conducted on participants’ accuracy scores revealed an interaction between participant’s age, stimulus age and emotion [F(5,300)=7.13, p<.001]. Paired t-tests indicated that young adults were more accurate than older adults with fear, no matter the stimulus age[t(61)=8.57, p<.001, t(61)=-3.32, p<.01 with young and old faces respectively]. They were also more accurate with sadness[t(61)=6.89, p<.001], but only when they were displayed by young faces, as well as with disgust[t(61)=-4.49, p<.001], and neutral[t(61)=-3.10, p<.01] when they were displayed by older faces. Moreover, the ratio of fixations duration on the eye vs. the mouth was significantly higher for younger than older adults[t(57)=2.22, p=.03]. These results confirm that the visual scanpath of adults is altered, even when older face stimuli are used.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".