When emotions improve reasoning: The possible roles of relevance and utility
Bibliographic record
Abstract
New paradigms in the psychology of reasoning have included a consideration for general contextual factors that may impact on the reasoning process, including individuals’ goals and motivations. We suggest that emotions are one such important contextual factor that influences reasoning. The classic literature on thinking and reasoning has typically ignored the possible influence of emotion, except to consider it a source of disruption. We review findings from studies where participants were asked to reason about personally relevant emotional experiences such as sexual abuse, war, and terrorist attacks. While some findings are consistent with the view that incidental emotions have a deleterious effect on reasoning, a number of findings also suggest a beneficial impact of emotion. For instance, veterans reasoned more logically about combat-related syllogisms than structurally identical syllogisms with neutral contents; victims of sexual abuse reporting more negative emotions following the events also reasoned more logically on abuse-related contents, relative to neutral contents. This may be associated with integral emotions, when the affective reaction is relevant to the semantic contents reasoned about. We propose that the positive impact of integral emotions on reasoning can be explained by increased utility of problem content and increased utility of reasoning.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".