Effects of Explicit Knowledge and Attentional-Perceptual Processing on the Ability to Recognize Fear and Surprise
Bibliographic record
Abstract
When participants are asked to identify expressed emotions from pictures, fear is often confused with surprise. The present study explored this confusion by utilizing one prototype of surprise and three prototypes of fear varying as a function of distinctive cues in the fear prototype (cue in the eyebrows, in the mouth or both zones). Participants were presented with equal numbers of pictures expressing surprise and fear. Eye movements were monitored when they were deciding if the picture was fear or surprise. Following each trial, explicit knowledge was assessed by asking the importance (yes vs. no) of five regions (mouth, nose, eyebrows, eyes, cheeks) in recognizing the expression. Results revealed that fear with both distinctive cues was recognized more accurately, followed by the prototype of surprise and fear with a distinctive cue in the mouth at a similar level. Finally, fear with a distinctive cue in the eyebrows was the least accurately recognized. Explicit knowledge discriminability results revealed that participants were aware of the relevant areas for each prototype but not equally so for all prototypes. Specifically, participants judged the eyebrow area as more important when the distinctive cue was in the eyebrows (fear-eyebrow) than when the cue was in the mouth (fear-mouth) or when both cues were present (fear-both). Results are discussed considering the attentional-perceptual and explicit knowledge limitation hypothesis.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".