Word Wins Over Face: Emotional Stroop Effect Activates the Frontal Cortical Network
Bibliographic record
Abstract
The prefrontal cortex (PFC) has been implicated in higher order cognitive control of behavior. Sometimes such control is executed through suppression of an unwanted response in order to avoid conflict. Conflict occurs when two simultaneously competing processes lead to different behavioral outcomes, as seen in tasks such as the anti-saccade, go/no-go, and the Stroop task. We set out to examine whether different types of stimuli in a modified emotional Stroop task would cause similar interference effects as the original Stroop-color/word, and whether the required suppression mechanism(s) would recruit similar regions of the medial PFC (mPFC). By using emotional words and emotional faces in this Stroop experiment, we examined the two well-learned automatic behaviors of word reading and recognition of face expressions. In our emotional Stroop paradigm, words were processed faster than face expressions with incongruent trials yielding longer reaction times and larger number of errors compared to the congruent trials. This novel Stroop effect activated the anterior and inferior regions of the mPFC, namely the anterior cingulate cortex, inferior frontal gyrus as well as the superior frontal gyrus. Our results suggest that prepotent behaviors such as reading and recognition of face expressions are stimulus-dependent and perhaps hierarchical, hence recruiting distinct regions of the mPFC. Moreover, the faster processing of word reading compared to reporting face expressions is indicative of the formation of stronger stimulus-response associations of an over-learned behavior compared to an instinctive one, which could alternatively be explained through the distinction between awareness and selective attention.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".