Neural Responses to 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 affective objects influence the underlying neural mechanisms related to how facial expressions are recognized. To explore this question, event related potential (ERP) responses to emotionally primed expressions were recorded. Participants viewed a person with a neutral expression being presented with a positive emotional object (money, birthday cake), or a negative emotional object (spider, gun). The objects appeared at one of two stimulus onset asynchronies (SOA) (0 ms and 500 ms). Following the SOA interval, the neutral expression of the person changed to a happy or a fearful expression. After 1000 ms delay, participants categorized the expression as either "happy" or "fear". The main finding was that "happy" faces elicited a greater positive amplitude around 300 to 350 ms when primed with a positive object (e.g. birthday cake) than when primed with a negative object (e.g. spider). Interestingly, this congruency effect was not found for "fear" faces. Taken together, these results suggest that single objects with strong emotional associations can influence how the brain processes positive facial emotions. Meeting abstract presented at VSS 2014
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".