Individual differences in affect and personality predict attentional and conceptual breadth
Bibliographic record
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) and approach/avoidance tendency (using the BIS/BAS Scale) can predict attentional breadth (using the global-local Navon letter task) and conceptual breadth (using the Remote Associates Test - RAT), with no mood manipulations or cues. Results showed that low arousal affect was positively associated with both measures of cognitive breadth. In contrast, approach motivation (BAS-Drive) was positively associated with a narrow attentional focus on the global-local task. Neither pleasant nor unpleasant valence (nor their difference) predicted any measure of cognitive breadth or focus. Overall, the results suggest the importance of arousal, motivational intensity and approach, as predictors of breadth of cognition. Finding that dispositional measures can also predict breadth of cognition suggests that affect and personality may underlie previously observed trait-like consistency in global-local bias. Meeting abstract presented at VSS 2014
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".