The bilateral field advantage in a sequential face–name matching task with famous and nonfamous faces.
Bibliographic record
Abstract
The authors examined the bilateral field advantage (BFA) using a sequential name-face (Experiment 1) or face-name (Experiment 2) matching task with both famous and nonfamous stimuli. In both experiments, the first stimulus (name in Experiment 1, face in experiment 2) was followed by a delay of 1,500 ms. The second stimulus (face in Experiment 1, name in Experiment 2) was then presented during a speeded response interval. Experiment 1 indicated a BFA for famous faces, but not for recently familiarized nonfamous faces. Experiment 2 indicated a BFA for names regardless of familiarity level. These results are compatible with prior findings of a BFA for meaningful stimuli as opposed to nonmeaningful ones, but only if previously unknown personal names are considered meaningful while previously unknown faces are not. These findings suggest that personal names are processed by bilateral neural networks whether they are previously known or not, whereas this is not the case for faces. Our findings have additional implications for working memory-based theories of why the BFA arises, and for the time course of meaningful association formation.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".