Bibliographic record
Abstract
A recent study about normal aging from our laboratory reported degraded face recognition across views but not with same views (Habak, Wilkinson, & Wilson, 2007, Vision Research). Therefore, we hypothesized that normal aging would affect face view adaptation. Younger (26 +/− 5.1 years) and older (67 +/− 5.2 years) subjects of 15 each with normal vision were recruited. They were required to make a two-alternative-forced choice of which direction a test face (200 ms) was facing after being adapted to an adapting face (5 s). Four adapting faces orientated at a side view (20°), an up view (20°), and their corresponding frontal views (for baseline measurement). Seven testing faces were orientated from left 6° to right 6° for side view and up 9° to down 9° for up or down view. The proportions of judging “right” or “down” view of 10 repetitions were calculated for each testing face at each condition. Point-of-subjective-equivalent (PSE) and sigma values were calculated from a psychophysical function. The older and younger groups showed similar baselines suggesting that thresholds of non-frontal view perceiving neurons are intact across aging. The older group showed a larger shift in PSEs and shallower slopes for the two adapting conditions suggesting that normal aging causes an increase in bandwidth of view-tuned neurons. These findings help to explain the degraded face perception in older population.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".