Dissociable Neural Systems Underwrite Logical Reasoning in the Context of Induced Emotions with Positive and Negative Valence
Bibliographic record
Abstract
How emotions influence syllogistic reasoning is not well understood. fMRI was employed to investigate the effects of induced positive or negative emotion on syllogistic reasoning. Specifically, on a trial-by-trial basis participants were exposed to a positive, negative, or neutral picture, immediately prior to engagement in a reasoning task. After viewing and rating the valence and intensity of each picture, participants indicated by keypress whether or not the conclusion of the syllogism followed logically from the premises. The content of all syllogisms was neutral, and the influence of belief-bias was controlled for in the study design. Emotion did not affect reasoning performance, although there was a trend in the expected direction based on accuracy rates for the positive (63%) and negative (64%) versus neutral (70%) condition. Nevertheless, exposure to positive and negative pictures led to dissociable patterns of neural activation during reasoning. Therefore, the neural basis of deductive reasoning differs as a function of the valence of the context.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".