Valence, expression and identity effects in the affective priming paradigm
Bibliographic record
Abstract
Background: In the affective-priming paradigm, a briefly-presented facial expression (prime) facilitates reaction time to identify a subsequent facial expression (target) if the prime and target are congruent in emotional valence (i.e. both positive or both negative). However, some studies have found that facilitation is more specific, only occurring when the prime and target share the same emotion (e.g. both angry). Objective: We investigated first whether reaction times to identify expressions showed valence priming effects that were modulated by whether the specific emotion was congruent between the prime and target. Second, we examined whether effects differed when the prime and target were faces of different people, a situation which minimizes low-level image effects. Methods: 22 subjects were asked to indicate if the valence of a target facial expression was positive or negative. A prime (no prime, angry, fearful, happy) was shown for 85ms, preceded and followed by masks, and then a target expression (angry, fearful, or happy) was shown for 100ms. We examined whether priming by negative emotions (angry or fearful) differed from positive emotions (happy). Next we assessed whether there was an interaction between specific expressions of the prime and of the target. Results: We replicated the valence effect of priming, showing that subjects were faster to respond to positive emotions with a positive prime, and negative emotions with negative primes. Similar effects were obtained when the faces of the prime and target differed in identity. Examination of effects on negative targets showed no interaction between prime and target, in either same or different-identity blocks. Conclusion: Expression-priming effects are related to emotional valence rather than specific expressions. Along with the finding that the effects do not depend on facial identity, this indicates a high-level origin of the priming effect, rather than an effect generated by low-level image properties. Meeting abstract presented at VSS 2015
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 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".