Dispositional affect predicts attentional and conceptual breadth: Individual difference evidence for the importance of arousal and valence interactions
Bibliographic record
Abstract
Abstract Several studies have investigated the effect of induced mood state on attentional and cognitive breadth. Early studies concluded that inducing a positive mood state broadened attention and cognition, while inducing a negative mood state narrowed these. However, recent reports have suggested that when valence and motivational intensity are unconfounded, low motivational intensity promotes cognitive breadth, whereas high motivational intensity promotes cognitive narrowing. Here we examine whether self-reported dispositional affect (using the circumplex affect questionnaire) can predict attentional breadth (using both the global-local Navon letter task and the hierarchical shape task) and conceptual breadth (using both the Remote Associates Test and an object categorization task), with no mood manipulations or cues. For all four tasks, results showed a valence-activation interaction. Participants low in activation (arousal) and high in positive affect showed the greatest cognitive breadth, and participants high in activation and high in positive affect showed the least cognitive breadth. Participants low in positive affect showed intermediate breadth that was not influenced by activation. In contrast to existing theories of cognitive breadth that highlight the importance of valence, or motivational intensity or arousal alone, the present results suggest that the combination of activation and valence is key to predicting individual differences in both attentional and conceptual breadth such that cognitive breadth decreases with activation, but only for those with high positive affect. Meeting abstract presented at VSS 2017
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".