Happy and angry facial expressions are processed independently of task demands and semantic context congruency in the first stages of vision – A mass univariate ERP analysis
Bibliographic record
Abstract
Neural decoding of others' facial expressions is critical in social interactions and has been investigated using scalp event related potentials (ERPs). However, the impact of task and emotional context congruency on this neural decoding is unclear. Previous ERP studies employed classic statistical analyses that only focused on specific electrodes and time points, which inflates type I and type II errors. The present study re-analyzed the study by Aguado et al. (2019) using robust data-driven Mass Univariate Statistics across every time point and electrode and rejected trials with early reaction times to rule out motor-related activity on neural recordings. Participants viewed neutral faces paired with negative or positive situational sentences (e.g. "She catches her partner cheating on her with her best friend"), followed by the same individuals' faces expressing happiness or anger, such that the facial expressions were congruent or incongruent with the situation. Participants engaged in two tasks: an emotion discrimination task, and a situation-expression congruency discrimination task. We found significant effects of expression largest during the N170-P2 interval, and effects of congruency and task around an LPP-like component. However, the effect of congruency was significant only in the congruency task, suggesting a limited and task-dependant influence of semantic context. Importantly, emotion did not interact with any factor neurally, suggesting facial expressions were decoded automatically during the first 400 ms of vision, regardless of context congruency or task demands. The results and their discrepancies with the original findings are discussed in the context of ERP statistics and the replication crisis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".