Dual-task interference on left eye utilization during facial emotion perception.
Bibliographic record
Abstract
There is an ongoing debate in the literature about whether facial emotion perception is carried automatically-that is, without effort or attentional resources. While it is generally accepted that spatial attention is necessary for the perception of emotional facial expressions, the picture is less clear for central attention. Using the bubbles method, we provide results that were obtained by measuring the effect of the psychological refractory period on diagnostic information for the basic facial expressions. Based on previous findings that linked spatial attention with processing of the eyes and of high spatial frequencies in the visual periphery, we hypothesized that reliance on the eyes might decrease when central resources were monopolized by a difficult prioritized auditory task. Central load led to a marked decrease in left eye utilization that was generalized across emotions; on the contrary, utilization of the mouth was unaffected by central load. Thus, processing of the left eye might be nonautomatic, and processing of the mouth might be automatic. Interestingly, we also observed a reduction in reliance on the left side of the face under central load that was accompanied by a commensurate increase in reliance on the right side of the face. We end with a discussion of how hemispheric asymmetries might account for these peculiar findings. (PsycINFO Database Record (c) 2019 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".