Individual differences in naturally occurring affect predict conceptual breadth: evidence for the importance of arousal by valence interactions
Bibliographic record
Abstract
Several studies have investigated the effect of induced mood state on conceptual breadth (breadth and flexibility of thought). Early studies concluded that inducing a positive mood state broadened cognition, while inducing a negative mood state narrowed cognition. However, recent reports have suggested that valence and arousal can each influence conceptual breadth. Individual differences in affective dispositions may bias perceptions, thoughts, and behaviors and, in turn, may be biased by them. Here, we examine whether individual differences in valence and arousal dimensions of self-reported, naturally occurring affect relate to conceptual breadth (using the Remote Associates Test, the Object Categorization Task, and the Alternative Uses Task), with no mood manipulations or cues. The three conceptual breadth tasks loaded onto a latent conceptual breadth factor that was predicted significantly by the interaction of valence and arousal. For participants low in arousal, greater positive affect was associated with greater conceptual breadth. For participants high in arousal, greater positive affect was associated with reduced conceptual breadth. In contrast to most existing theories of conceptual breadth that highlight the importance of valence or arousal alone, the present results suggest that the interaction between arousal and valence is key to predicting individual differences in conceptual breadth. We posit that positive mood states predict greater conceptual breadth in the presence of low versus high arousal due to a relaxation of cognitive control under low arousal.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".