Representing Facial Expressions in Visual Working Memory: A Novel Adaptation of the Continuous Response Paradigm
Bibliographic record
Abstract
Most studies investigating emotion perception have used dichotomous response measures whereby each response is either correct or incorrect. We used a novel continuous response paradigm to investigate the precision of visual working memory for expressions of sadness, anger, and fear and to investigate whether biases in errors (e.g., incorrectly perceiving angry faces as fearful rather than sad) are evident early in visual processing. We created an "emotion wheel" by morphing three anchor expressions (anger/sad/fear) in 4% steps. On each trial (n = 750), a target face (an anchor or any randomly selected morph) appeared for 500ms. After a 900ms delay, participants (n=29) located the target face on the emotion wheel (comprised of 75 faces representing continuous variation in emotion). We measured the magnitude (degrees between target and response) and direction (e.g., towards or away from particular emotions) of response error. The magnitude of response error varied with proximity of the target to an anchor expression (smaller for unambiguous [target contained >75% of one emotion, m = 47°] vs. ambiguous [target contained < 75% of either emotion, m = 60°] targets, p < .001) and across expressions (smaller for unambiguous angry compared to sad or fearful expressions, ps < .001; smaller for ambiguous angry/fear and fear/sad blends than angry/sad blends, ps < .01). The direction of response biases for ambiguous targets favored threat-related expressions. Participants were biased towards anger and fear when viewing anger/sad and fear/sad blends, ps < .05; no bias was observed for angry/fearful blends. Collectively, our findings suggest prioritization of both direct (angry) and indirect (fearful) threat, as opposed to merely negative (sad) faces. Our results have important implications for emotion theory and for understanding threat-related biases in emotion processing. Meeting abstract presented at VSS 2018
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".