Prime-mask interactions in unconscious priming and conscious perception of emotional faces
Bibliographic record
Abstract
Winkielman, Berridge, & Wilbarger (in press, PSPB) report that unseen happy and angry faces influence consumptive behaviors such as drinking and the rated value of a beverage. Central to their claim is the assumption that the emotionally expressive faces were not consciously perceived. Participants in their study were shown emotional faces for 16 ms, which were replaced by neutral faces for 400 ms. These display sequences influenced beverage consumption, even though participants were at chance in their attempts to identify the emotion in the prime faces. In the present work, we asked whether perception of either the prime or the mask face was influenced by interactions between features of both faces. In phase 1, participants made speeded classifications of happy and angry masks (450 ms) that were preceded by angry, happy, or neutral primes (22 ms) at one of three intervals (22 ms, 45 ms, or 67 ms). In phase 2, participants were instructed to classify these same prime faces as either happy or angry. Prime-mask congruence had different influences in the two tasks. In the mask classification task, emotionally congruent primes led to faster responses than incongruent primes and this effect increased with prime-mask interval. Featural similarity in the faces played no role in priming. In the prime classification task, the effect of emotional congruence interacted with featural similarity of the faces. For emotionally congruent faces, increased feature similarity improved accuracy whereas for incongruent faces it impaired accuracy. This underscores the importance of examining prime-mask interactions on unconscious influences on consumptive behavior. A second study examined the issue of task relevance in the masking of faces. We discuss the implications of these findings for unconscious action priming and conscious object recognition in the realm of rapid emotional processing.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".