Age Differences in Face Processing: The Role of Perceptual Degradation and Holistic Processing
Bibliographic record
Abstract
OBJECTIVES: We simultaneously investigated the role of three hypotheses regarding age-related differences in face processing: perceptual degradation, impaired holistic processing, and an interaction between the two. METHODS: Young adults (YA) aged 20-33-year olds, middle-age adults (MA) aged 50-64-year olds, and older adults (OA) aged 65-82-year olds were tested on the context congruency paradigm, which allows measurement of face-specific holistic processing across the life span (Meinhardt-Injac, Persike & Meinhardt, 2014. Acta Psychologica, 151, 155-163). Perceptual degradation was examined by measuring performance with faces that were not filtered (FSF), with faces filtered to preserve low spatial frequencies (LSF), and with faces filtered to preserve high spatial frequencies (HSF). RESULTS: We found that reducing perceptual signal strength had a greater impact on MA and OA for HSF faces, but not LSF faces. Context congruency effects were significant and of comparable magnitude across ages for FSF, LSF, and HSF faces. By using watches as control objects, we show that these holistic effects reflect face-specific mechanisms in all age groups. DISCUSSION: Our results support the perceptual degradation hypothesis for faces containing only HSF and suggest that holistic processing is preserved in aging even under conditions of reduced signal strength.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".