How efficient are the recognition of dynamic and static facial expressions?
Bibliographic record
Abstract
Recently, Ambadar, Schooler and Cohn (2005) compared facial expression recognition performance with static and dynamic stimuli. To control for task difficulty, the researchers equated the information content in their dynamic and in their so-called “multi-static” condition, in which the frames of the dynamic stimuli were separated by noise masks and were played very slowly. Observers were better at discriminating dynamic than multi-static stimuli but only when the facial expressions were subtle. This result, however, might be due to low-level masking or to some high-level memory decay rather than to observer's sensitivity to facial expression movement per se. Here, we factored out task difficulty by measuring the calculation efficiency for the static vs. dynamic recognition of eight facial expressions (happiness, fear, sadness, disgust, surprise, anger, and pain). Contrary to sensitivity measures, such as the d', efficiency measures are directly comparable across tasks. Twenty naïve observers will participate to the experiment. We will extract their energy thresholds for the recognition of static and dynamic facial expressions (drawn from sets of 80 static and 80 dynamic stimuli) in five levels of external noise using the method of constant stimuli (5–10 levels of energy per noise level). Calculation efficiencies will be computed by dividing the slopes of the lines that best fit the energy thresholds of ideal and human observers. Preliminary results obtained on three observers yield efficiencies of about 20.43%, 11.46%, 11.95% and 10.4%, 8.76%, 7.36%, respectively, for the recognition of static and dynamic facial expressions.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".