Emotional modulation of inhibitory control in rumination from empirical and computational perspectives
Bibliographic record
Abstract
Rumination involves repetitive dwelling on negative thoughts, emotions, and memories and is a risk factor for depression. Cognitive theories suggest that rumination stems from heightened automatic, emotional stimuli-driven (i.e., "bottom-up") and/or deficits in effortful, goal-directed (i.e., "top-down") processes. It remains unclear whether rumination arises from bottom-up processes impacting top-down inhibitory control or from impaired inhibition alone. We used both experimental and computational approaches to address this. Participants (N = 151) first completed self-report measures of trait rumination, followed by the standard and emotional Stroop tasks, before and after a rumination induction. Brooding, a maladaptive component of rumination, was associated with slower reaction times for both tasks. A rumination induction, expected to heighten bottom-up emotional salience, increased the congruency effect in proportion to brooding severity. To study underlying computational mechanisms, we adapted an existing parallel distributed processing model of the Stroop task to include mechanisms for emotional cue processing and subsequently numerically fit the model parameters to individual participant Stroop data. Brooding was positively associated with bottom-up weights and steeper neural activation curves in the task control layer, representing a greater sensitivity to emotional cues and changes in task demands. Higher brooding also predicted faster temporal integration (i.e., activity decay) of top-down control signals and slower temporal integration (i.e., activity persistence) of emotional cues. We therefore propose that a greater sensitivity to changes in task demands and bottom-up emotional cues, along with a diminished capacity to sustain goal-relevant control signals, underlie inhibitory control deficits in trait rumination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".