Does emotion help or hinder reasoning? The moderating role of relevance.
Bibliographic record
Abstract
Some prior research has shown that emotion impairs logicality in deductive reasoning tasks, while other research suggests improved performance with emotional contents. We suggest that relevance, whether the affective state is associated with the semantic contents of the reasoning task, may be crucial in explaining these apparently inconsistent findings. This hypothesis is based on a framework distinguishing between integral emotions, where affective responses are evoked by the semantic contents of the target task, and incidental emotions, where affective responses are not related to the task. In 4 experiments we examined the effect of emotion on conditional reasoning when affective responses were relevant and irrelevant. We used images presented simultaneously with the reasoning stimuli (Experiments 1, 2, and 3) or videos presented prior to the reasoning stimuli (Experiment 4) that were either emotional or neutral and semantically related or not to the conditional statements. Results showed that emotion decreased the proportion of normatively correct responses only in the irrelevant condition. In the relevant condition, emotion did not produce reliable deleterious effects. We used reaction time and skin conductance measures to investigate the physiological and cognitive correlates of these effects. Results are discussed in terms of the distinction between incidental and integral emotions.
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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 | 0.002 | 0.019 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".