The influence of peripheral emotions on inhibitory control among children
Bibliographic record
Abstract
In this study, we investigated the cognitive-emotional interplay by measuring the effects of executive competition (Pessoa, 2013), i.e., how inhibitory control is influenced when emotional information is encountered. Sixty-three children (8 to 9 years of age) participated in an inhibition task (central task) accompanied by happy, sad, or neutral emoticons (displayed in the periphery). Typical interference effects were found in the main task for speed and accuracy, but in general, these effects were not additionally modulated by the peripheral emoticons indicating that processing of the main task exhausted the limited capacity such that interference from the task-irrelevant, peripheral information did not show (Pessoa, 2013). Further analyses revealed that the magnitude of interference effects depended on the order of congruency conditions: when incongruent conditions preceded congruent ones, there was greater interference. This effect was smaller in sad conditions, and particularly so at the beginning of the experiment. These findings suggest that the bottom-up perception of task-irrelevant emotional information influenced the top-down process of inhibitory control among children in the sad condition when processing demands were particularly high. We discuss if the salience and valence of the emotional stimuli as well as task demands are the decisive characteristics that modulate the strength of this relation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".