The effect of orientation and stimulus duration on older and younger adults' ability to identify facial expressions.
Bibliographic record
Abstract
In younger adults, certain facial expressions (e.g. happiness) are recognized easily, while others (e.g. fear and surprise) are not (Palermo and Coltheart, 2004). Previous results suggest that older adults show overall recognition deficits and qualitatively different patterns in the particular expressions that are most difficult to identify (Ruffman et al., 2008). In the current study, 23 younger (18-33 years old) and 23 older (60-80 years old) adults performed a 4AFC (angry, fearful, happy, sad) facial expression categorization task that varied face orientation (upright/inverted) and stimulus duration (100, 500, 1000ms). For both groups, happiness was the easiest expression to identify, and fear and sadness were the most difficult and most frequently confused. For upright faces, there was no age difference in response accuracy but response latency was longer in older subjects. For inverted faces, older adults showed lower accuracy and longer latencies for expressions of anger, fear, and sadness. Recognition of inverted happy faces was spared in older adults for accuracy, but not response latency. At all stimulus durations, older subjects were less accurate than younger subjects for angry and sad faces, but accuracy for happy faces was unaffected by age. The pattern of relative difficulties was the same in each age group at both orientations and all stimulus durations. Furthermore, there was no age difference in the pattern of response confusions. However, when subjects were asked to classify neutral faces, younger subjects were more likely to respond angry or fearful than sad, while the order was reversed for older subjects. Our results suggest that, in general, older individuals process expressive faces in a qualitatively similar way to their younger counterparts, but are less efficient at extracting the diagnostic information. Age-related deficits observed in previous studies may reflect a general decrease in processing efficiency, rather than facial expression identification per se. Meeting abstract presented at VSS 2012
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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 | 0.003 |
| 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) | 0.003 | 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".