Bibliographic record
Abstract
Adults' expertise in face recognition relies upon holistic processing. When a stimulus is detected as a face, its parts become integrated into a whole or Gestalt-like representation, making information about individual features less accessible. A compelling demonstration is the whole/part advantage: adults are much better at recognizing the features from an individual's face in the context of the whole face than in isolation (Tanaka & Farah, 1993). In the present study we investigated whether holistic processing is also engaged for another aspect of face processing, the recognition of emotional expressions. This is of interest because previous research has shown that processing facial identity and facial expressions are dissociable functions that involve separate systems. Adult participants were presented with a target face stimulus displaying either a congruent emotional expression (e.g., happy eyes and happy mouth) or an incongruent emotional expression (e.g., angry eyes and happy mouth). In the whole face condition, participants were then shown two faces, the target and a foil that differed from the target in the emotional expression of one feature (either the eyes or mouth). In the isolated-part condition, participants were presented with one feature from the target face and a foil feature displaying a different emotion. Participants were instructed to find the target face (whole face condition) or the target feature (isolated-part condition). We predicted that for congruent trials, participants would be better at identifying the features in the whole face than when presented as isolated parts (the whole/part advantage). In contrast, we predicted that for incongruent trials, participants would be better at identifying the features as isolated parts than when presented in the whole face. We discuss the results in terms of how holistic processing is engaged in normal adults and individuals with face processing deficits (i.e., prosopagnosia and autism).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".