Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach
Bibliographic record
Abstract
We investigated age-related changes in visual processing speed in a face discrimination task using ERPs. Younger (n=13, mean age=22) and older (n=18, mean age=70) observers performed a spatial, two alternative forced choice task between 2 faces. Emphasis was on accuracy, not speed. Stimulus phase was manipulated in a parametric design, ranging from 0% (noise), to 100% (original stimulus). Behavioural 75% correct thresholds were on average lower, and maximum accuracy was higher, in younger than older observers. The earliest age-related ERP differences occurred in the time window of the N170: Older observers had a significantly stronger N170 in response to noise, but this age difference decreased with increasing phase information. These effects were not due to changes in brain signal variance. Overall, manipulating phase had a greater effect on ERPs from younger observers. This result was confirmed by a hierarchical modelling approach. ERPs from each subject were entered into a single-trial multiple linear regression model to identify variations in neural activity statistically associated with changes in image structure (Rousselet, Pernet, Bennett & Sekuler, BMC Neuroscience, 2008). The main model parameters were stimulus phase noise, kurtosis, and a measure of local phase coherence. The fit of the model, indexed by R2, was computed at multiple post-stimulus time points: peak R2 was similar in the two groups, but it occurred at a longer latency in older observers. Overall, our results suggest that older subjects accumulate face information more slowly than younger subjects. Despite the overall age-related group differences, within each age group chronological age did not predict any of the results.
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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 | 0.004 |
| 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 | 0.002 |
| 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".