Dissociating affective and perceptual effects of schematic faces on attentional scope.
Bibliographic record
Abstract
Attention allocation to positive and negative stimuli differs. For example, the flanker-interference asymmetry describes a pattern of results on flanker tasks using emotional stimuli, where a typical flanker-interference effect is observed for positive targets but not for negative targets. There are two dominant explanations for the flanker-interference asymmetry. According to the emotion-first explanation, negative targets are preferentially processed to facilitate the processing of potentially threatening stimuli. In contrast, feature-first explanations argue that the asymmetry results from differences in perceptual complexity between positive and negative stimuli. Three experiments used schematic emotional faces in a flanker task to directly compare these explanations. To manipulate the perceptual complexity of the stimuli, an enclosing circle was present on half of the trials. In all three experiments, reaction times showed the expected flanker-interference asymmetry, but the pattern was not influenced by the presence of the circle. However, event-related potentials showed that perceptual complexity influenced both the structural encoding and evaluative processing of the faces in the N170 and P3b time windows. These results suggest that both perceptual complexity and emotional valence play an important role in the processing of schematic emotional faces, but that emotional valence may have a stronger effect at evaluative stages of processing. Other findings show that the enclosing circle may alter the perceived emotional expression of neutral faces. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".