Fast and slow object priming of fearful and happy facial expressions
Bibliographic record
Abstract
Facial expressions are not perceived in isolation, but are embedded in a complex perceptual and social environment. Contextual factors, such as body gesture and emotional scene, have been shown to influence the processes of expression recognition. However, it is not known how objects affect the speed at which a facial expression is recognized. In this experiment, a person presenting a neutral expression was shown with either a positive emotional object (money, birthday cake), a negative emotional object (spider, gun) or a neutral object (stapler, spoon). The objects appeared at one of four stimulus onset asynchronies (SOA) (0 ms, 100 ms, 300 ms, 500 ms). Following the SOA interval, the neutral expression of the person changed to a happy or a fearful expression. The participant’s task was to categorize the expression as "happy" or "fear" as quickly and accurately as possible. Overall, reaction time was faster when the expression was accompanied by congruent objects than when shown with incongruent or neutral objects. Congruency also interacted with emotion and SOA. At the shortest SOA (0 ms), participants were faster to categorize the "fear" expression when preceded by the congruent negative objects than when preceded by incongruent positive objects. At the longest SOA (500 ms), participants were faster to categorize the "happy" expression when preceded by congruent positive objects than when preceded by neutral or incongruent negative objects. The obtained results demonstrate that single objects with strong emotional associations can prime the recognition of facial expressions. Object priming for happy and fearful expressions seem to follow separate timing trajectories. Whereas fear is a "fast" emotion that is rapidly primed by a negative object, happy is a "slow" emotion that is gradually primed by a positive object. Meeting abstract presented at VSS 2013
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".