Eye movements and spatial frequency utilization during the recognition of static and dynamic facial expressions
Bibliographic record
Abstract
Previous studies have revealed that dynamic facial expressions are better recognized (e.g. Ambadar et al., 2005), and are processed in partially different brain areas (e.g. Schultz & Pilz, 2009), than static expressions. Still unknown is if the visual strategies underlying the recognition of dynamic and static expressions differ. Here, the ocular fixation pattern (Exp. 1) and spatial frequency (SF) utilization (Exp. 2) of 20 participants were measured with static and dynamic expressions. In both experiments, participants categorized pictures or videos (block design) of the six basic facial expressions and neutrality. In Exp. 1, the stimuli were presented unaltered and in Exp. 2, they were randomly filtered using SF Bubbles (Willenbockel et al., 2010). In both experiments, stimuli were presented for a duration of 500 ms. Fixation patterns were analyzed using iMap4 (Lao, et al., 2015). A repeated measures ANOVA revealed a main effect of condition (p< 0.05), indicating more fixations on the eye area with static than dynamic expressions, and more fixations on the nose area with dynamic than static expressions. SF tunings were obtained by conducting a multiple regression analysis on the random SF filters and accuracies across trials. Statistical thresholds were found with the Stat4Ci (Chauvin et al., 2005). A SF band peaking at 17.7 cycles per face (cpf) with a full-width-half-max (FWHM) of 30.3 cpf, and a SF band peaking at 16.0 cpf with a FWHM of 29.0 cpf, were found with static and dynamic facial expressions, respectively. SF between 3.7 and 5.7 cpf were more utilized with dynamic than with static expressions, and those between 18.7 and 27.3 cpf were more utilized with static than with dynamic expressions (p< 0.025). Together, these results suggest that the recognition of dynamic facial expressions can be performed using lower spatial frequencies, decreasing the need to directly fixate on facial features. Meeting abstract presented at VSS 2016
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".