Exploring emotional and cognitive conflict using speeded voluntary facial expressions.
Bibliographic record
Abstract
Affective conflict and control may have important parallels to cognitive conflict and control, but these processes have been difficult to quantitatively study with emotionally naturalistic laboratory paradigms. The current study examines a modification of the AX-Continuous Performance Task (AX-CPT), a well-validated probe of cognitive conflict and control, for the study of emotional conflict. In the Emotional AX-CPT, speeded emotional facial expressions measured with electromyography (EMG) were used as the primary response modality, and index of emotional conflict. Bottom-up emotional conflict occurred on trials in which precued facial expressions were incongruent with the valence of an emotionally evocative picture probe (e.g., smiling to a negative picture). A second form of top-down conflict occurred in which the facial expression and picture probe were congruent, but the opposite expression was expected based on the precue. A matched version of the task was also performed (in a separate group of participants) with affectively neutral probe stimuli. Behavioral interference was observed, in terms of response latencies and errors, on all conflict trials. However, bottom-up conflict was stronger in the emotional version of the task compared to the neutral version; top-down conflict was similar across the two versions. The results suggest that voluntary facial expressions may be more sensitive to indexing emotional than nonemotional conflict, and importantly, may provide an ecologically valid method of examining how emotional conflict may manifest in behavior and brain activity.
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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 | 0.000 |
| 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".