Joint Modulation of Facial Expression Processing by Contextual Congruency and Task Demands
Bibliographic record
Abstract
Faces showing expressions of happiness or anger were presented together with sentences that described happiness-inducing or anger-inducing situations. Two main variables were manipulated: (i) congruency between contexts and expressions (congruent/incongruent) and (ii) the task assigned to the participant, discriminating the emotion shown by the target face (emotion task) or judging whether the expression shown by the face was congruent or not with the context (congruency task). Behavioral and electrophysiological results (event-related potentials (ERP)) showed that processing facial expressions was jointly influenced by congruency and task demands. ERP results revealed task effects at frontal sites, with larger positive amplitudes between 250-450 ms in the congruency task, reflecting the higher cognitive effort required by this task. Effects of congruency appeared at latencies and locations corresponding to the early posterior negativity (EPN) and late positive potential (LPP) components that have previously been found to be sensitive to emotion and affective congruency. The magnitude and spatial distribution of the congruency effects varied depending on the task and the target expression. These results are discussed in terms of the modulatory role of context on facial expression processing and the different mechanisms underlying the processing of expressions of positive and negative emotions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".