On the automatic activation of attitudes: A quarter century of evaluative priming research.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it