On the automatic activation of attitudes: A quarter century of evaluative priming research.
Bibliographic record
Abstract
Evaluation is a fundamental concept in psychological science. Limitations of self-report measures of evaluation led to an explosion of research on implicit measures of evaluation. One of the oldest and most frequently used implicit measurement paradigms is the evaluative priming paradigm developed by Fazio, Sanbonmatsu, Powell, and Kardes (1986). This paradigm has received extensive attention in psychology and is used to investigate numerous phenomena ranging from prejudice to depression. The current review provides a meta-analysis of a quarter century of evaluative priming research: 73 studies yielding 125 independent effect sizes from 5,367 participants. Because judgments people make in evaluative priming paradigms can be used to tease apart underlying processes, this meta-analysis examined the impact of different judgments to test the classic encoding and response perspectives of evaluative priming. As expected, evidence for automatic evaluation was found, but the results did not exclusively support either of the classic perspectives. Results suggest that both encoding and response processes likely contribute to evaluative priming but are more nuanced than initially conceptualized by the classic perspectives. Additionally, there were a number of unexpected findings that influenced evaluative priming such as segmenting trials into discrete blocks. We argue that many of the findings of this meta-analysis can be explained with 2 recent evaluative priming perspectives: the attentional sensitization/feature-specific attention allocation and evaluation window perspectives.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".