Emotion Processing in Late Adulthood: The Effect of Emotional Valence and Face Age on Behavior and Scanning Patterns
Bibliographic record
Abstract
Age-related differences in emotion recognition are well-documented in older adults aged 65 and above, with stimulus valence and the age of the model being key influencing factors. This study examined these variables across three experiments using a novel set of images depicting younger and older models expressing positive and negative emotions (e.g., happy vs. sad; interested vs. bored). Experiment 1 focused on valence-arousal dimensions, Experiment 2 on emotion recognition accuracy, and Experiment 3 on visual fixation patterns. Age-related differences were found in emotion recognition. No significant age-related differences in gaze behavior were found; both age groups looked more at the eye region. The positivity effect-older adults' tendency to prioritize positive over negative information-did not consistently manifest in recognition performance or scanning patterns. However, older adults evaluated positive emotions differently than negative emotions, rating negative facial expressions as less negative and positive emotions as more arousing compared to younger adults. Finally, emotions portrayed by younger models were rated as more intense and more positive than those portrayed by older adults by both older and younger adults. We conclude that the positivity effect and own-age bias may be more complex and nuanced than previously thought.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".