Dual-Task Interference on Early and Late Stages of Facial Emotion Detection Is Revealed by Human Electrophysiology
Bibliographic record
Abstract
Rapid and accurate processing of potential social threats is paramount to social thriving, and provides a clear evolutionary advantage. Though automatic processing of facial expressions has been assumed for some time, some researchers now question the extent to which this is the case. Here, we provide electrophysiological data from a psychological refractory period (PRP) dual-task paradigm in which participants had to decide whether a target face exhibited a neutral or fearful expression, as overlap with a concurrent auditory tone categorization task was experimentally manipulated. Specifically, we focused on four event-related potentials (ERP) linked to emotional face processing, covering distinct processing stages and topography: the early posterior negativity (EPN), early frontal positivity (EFP), late positive potential (LPP), and also the face-sensitive N170. As expected, there was an emotion modulation of each ERP. Most importantly, there was a significant attenuation of this emotional response proportional to the degree of task overlap for each component, except the N170. In fact, when the central overlap was greatest, this emotion-specific amplitude was statistically null for the EFP and LPP, and only marginally different from zero for the EPN. N170 emotion modulation was, on the other hand, unaffected by central overlap. Thus, our results show that emotion-specific ERPs for three out of four processing stages-i.e., perceptual encoding (EPN), emotion detection (EFP), or content evaluation (LPP)-are attenuated and even eliminated by central resource scarcity. Models assuming automatic processing should be revised to account for these results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".