Bibliographic record
Abstract
Transient affective states have been shown to affect cognition, including category learning, but less is known about the role of stable temperament traits and categorization. We examined affective temperament traits to see whether the tendency to experience negative and positive affect is predictive of category learning performance and strategy use. Working memory and attentional control were measured as covariates. Participants first completed the Adult Temperament Questionnaire (Evans & Rothbart, 2007) including two affective temperament factors and an attentional control factor. Then they completed a memory task followed by either a conjunctive rule-based (CR) or an information integration (II) category learning task. Results showed that people who tend to experience more positive affect and less negative affect achieved higher accuracy and were more likely to find the optimal strategy in the II task compared to people who tend to experience more negative affect and less positive affect. However, no performance or strategy use difference was seen in the CR task across different temperament profiles. These results extend prior literature and provide additional insights on the effects of stable traits on category learning.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".