Processing of Emotional Distraction Is Both Automatic and Modulated by Attention: Evidence from an Event-related fMRI Investigation
Bibliographic record
Abstract
Traditionally, emotional stimuli have been thought to be automatically processed via a bottom-up automatic "capture of attention" mechanism. Recently, this view has been challenged by evidence that emotion processing depends on the availability of attentional resources. Although these two views are not mutually exclusive, direct evidence reconciling them is lacking. One limitation of previous investigations supporting the traditional or competing views is that they have not systematically investigated the impact of emotional charge of task-irrelevant distraction in conjunction with manipulations of attentional demands. Using event-related fMRI, we investigated the nature of emotion-cognition interactions in a perceptual discrimination task with emotional distraction by manipulating both the emotional charge of the distracting information and the demands of the main task. Our findings show that emotion processing is both automatic and modulated by attention, but emotion and attention were only found to interact when finer assessments of emotional charge (comparison of most vs. least emotional conditions) were considered along with an effective manipulation of processing load (high vs. low). The study also identified brain regions reflecting the detrimental impact of emotional distraction on performance as well as regions involved in coping with such distraction. Activity in the dorsomedial pFC and ventrolateral pFC was linked to a detrimental impact of emotional distraction, whereas the dorsal ACC and lateral occipital cortex were involved in helping with emotional distraction. These findings demonstrate that task-irrelevant emotion processing is subjective to both the emotional content of distraction and the level of attentional demand.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".