Spatial frequencies for accurate categorization and discrimination of facial expressions
Bibliographic record
Abstract
Many studies have examined the role of spatial frequencies (SFs) in facial expression perception. However, most of these studies used arbitrary cut-off to isolate the impact of low and high SFs (De Cesarei & Codispoti, 2012) thus removing possible contribution of mid-SFs. This present study aims to reveal the diagnostic SFs for each basic emotion as well as neutral using SFs Bubbles (Willenbockel et al., 2010). Forty participants were tested (20 in a categorization task, 20 in a discrimination task; 4200 trials per participant). In the categorization task, subjects were asked to identify the perceived emotion among all the alternatives. In the discrimination task, subjects were asked, in a block-design setting (block order was counterbalanced across participants), to discriminate between a target emotion (e.g fear) and all other emotions. Mean accuracy was maintained halfway between chance (i.e. 12.5% and 50% correct for each task, respectively) and perfect accuracy. In both tasks, accuracy for happiness and surprise is associated with low-SFs (peaking at around 5 cycles per face (cpf); Zcrit=3.45, p< 0.05 for all analysis) whereas accuracy for sadness and neutrality is associated with mid-SFs (peaking between 11.5 and 15 cpf for both tasks). Interestingly, the facial expressions of fear and anger reveal significantly different patterns of use across task. Whereas their correct categorization is correlated with the presence of mid-to-high SFs (peaking at 14 and 20 cpf for angry and fear, respectively) their accurate discrimination is correlated with the utilization of lower SFs (peaking at 4 and 3.7 cpf). These results suggest that the visual system is able to use low-SF information to detect and discriminate social threatening cues. However, higher-SFs are probably necessary in a multiple-choices categorization task to allow fine-grained discrimination. 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".