Attentional influences on affective priming: Does categorisation influence spontaneous evaluations of multiply categorisable objects?
Bibliographic record
Abstract
Previous research suggests that spontaneous evaluative responses to a stimulus depend on how that stimulus is categorised. The present research indicates that such categorisation effects depend on task-specific aspects of the measure, thereby concealing or overriding effects of unattended category cues. Results showed that affective priming effects in a paradigm based on response interference depended on participants' attention to the category membership of the primes. These effects were reflected in: (a) reduced effect sizes; (b) reduced internal consistencies; and (c) reduced correlations to corresponding self-reports when attention was directed toward alternative categories. Such attention-related decrements were not obtained for a priming paradigm based on affect misattribution, which showed reliable priming effects irrespective of participants' attention to the relevant categories. These results challenge the ubiquity of categorisation effects on spontaneous evaluations, suggesting that the impact of unattended category cues depends on conditions inherent in specific tasks.
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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 | 0.006 |
| 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) | 0.001 | 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".