Discrimination, bias and focused attention in the composite face effect
Bibliographic record
Abstract
In the composite-face effect subjects attempt to recognize one face-half when it is aligned or misaligned with the other half. In same/different experiments, subjects are less likely to perceive that one half is the same in the aligned than the misaligned condition, which others argue represents interference from the whole facial configuration in aligned stimuli. However, it is unclear whether this is due to reduced discriminability or a criterion shift. Furthermore, the contribution of focusing attention on one face-half is unknown. We had 18 healthy subjects perform two composite face tests, each with one block of aligned faces and another of misaligned faces. Each trial consisted of a composite face viewed for 200 ms, followed by a second composite face shown at 50% larger scale, also for 200 ms. In the first test, subjects indicated if one half of the face was the same or different in the two images, while disregarding the other face-half. Half of the subjects responded to the upper face-half and half to the lower face-half. In the second test, subjects indicated if EITHER the top or bottom half of the face was the same. The same composite faces were used in both tests, with order counterbalanced. We calculated hit rate, false alarm rate, d' and c' (criterion bias). Face-alignment had a significant effect on c' but not on d', an effect that derived mainly from the condition of attending to one face-half. When subjects attended to both halves, hit rates decreased and false alarms increased, resulting in a decrease in d'; also, in this condition alignment did not have an effect on any variable. We conclude that the composite effect is due to a shift in criterion bias rather than discriminability, and that focused attention on one face-half is critical.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".