AGE-RELATED IMPACT OF ABILITY AT IDENTIFYING FACIAL EXPRESSION ON UTILIZATION OF VISUAL INFORMATION
Bibliographic record
Abstract
Previous studies have shown that aging is associated with difficulties at recognizing some facial expressions (Calder and al., 2003; West and al., 2012). Circelli et al., (2013) showed that this alteration in performance is linked to changes in the older adults’ visual scanpaths. We recently showed that despite displaying different visual scanpaths, older and younger participants use the same facial features on average to accurately categorize the basic facial expressions (Dion-Marcoux et al., 2016). However, we also observed some heterogeneity in the ability of our participant to categorize expressions, and these differences in the ability may be linked to the visual strategies used. This study compared the impact of ability at categorizing expression on the use of visual information of older (N=31; Mage=71.8) and younger adults (N=31; Mage=22.6). The Bubbles method (Gosselin & Schyns, 2001) was used to measure information utilization during a facial expression categorization task of basic emotions displayed by young and elderly faces (five identities each). A separate facial expression categorization task was used to measure ability. Classification images representing the visual information that was correlated with the ability at identifying facial expressions were separately obtained for each facial expression, facial age, and participants’ age group. The results showed that participants’ ability modulate the visual information utilized by older, but not younger, adults. Future analyses will allow verifying if the older participants with the highest performance alteration reveal visual strategies that differ from those of young participants, and if these differences can predict their alteration.
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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 | 0.005 |
| 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) | 0.004 | 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".