Old and Young use the same visual information to identify basic facial expressions
Bibliographic record
Abstract
Previous studies have shown that aging is associated with difficulties at recognizing facial expressions of fear, anger and sadness (Calder et al., 2003; West et al., 2012). Along with this alteration in performance, differences are observed between the visual scanpaths of older and younger adults during facial emotion recognition, whereby older adults fixate less on the eye area (Circelli et al., 2013). However, in emotion recognition, there is not a perfect overlap between the areas fixated on a face, and the utilization of the information contained in those areas (e.g. Blais et al., 2012 vs. Vaidya, et al., 2014). The present study therefore compared the visual information utilization of older adults (N=31; 65+ years old; Mage=71.8) to that of younger adults (N=31; 18-30 years old; Mage=22.6) during the recognition of facial emotions. The Bubbles method (Gosselin & Schyns, 2001) was used in a facial expression categorization task including four basic emotions (happy, fear, disgust and anger), displayed by either young (5 identities) or old faces (5 identities; Lindenberger, Ebner & Riediger, 2005). Classification images representing the visual information positively correlated with accuracy were separately obtained for each facial expression, facial age, and participants' age group. The results showed that older and younger participants use the same facial features in the same spatial frequency bands to accurately categorize the four basic facial expressions. Moreover, the visual information utilization was not modulated by the age of the face stimuli. Further investigation will be needed to clarify the apparent disparity between the present results, indicating no difference between old adults' and young adults' visual information utilization, and previous studies showing an altered visual scanpath in older adults. Meeting abstract presented at VSS 2016
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".