Spatial frequencies for the visual processing of the facial expression of pain
Bibliographic record
Abstract
Recent studies suggest that low spatial frequencies (SFs) are particularly important for the visual processing of the facial expression of pain (Wang et al., 2015; 2017). However, these studies used arbitrary cut-off to isolate the impact of low (under 8 cycles per faces (cpf)) and high (over 32 cpf) SFs, thus removing any contribution of the mid-SFs. Here we compared the utilization of SFs for pain and other basic emotions in three tasks (20 participants per task), that is 1) a facial expression recognition task with all basic emotions and pain, 2) a facial expression discrimination task where one target expression needed to be discriminated from the others and 3) a facial expression discrimination task with only two choices (i.e. fear vs. pain, pain vs. happy). SF Bubbles were used (Willenbockel et al., 2010), a method which randomly samples SFs on a trial-by-trial basis, enabling us to pinpoint the SFs that are correlated with accuracy. In the first task, accurate categorization of pain was correlated with the presence of a large band of SFs ranging from 4.3 to 52 cpf peaking at 14 cpf (Zcrit=3.45, p< 0.05 for all analysis). In the second task, the correct discrimination of pain was correlated with the presence of a band of SFs ranging from 5 to 20 cpf peaking at 11 cpf. In the third task, we computed the classification vectors for pain-happiness and pain-fear conditions and revealed the overlapping SFs. In this task, SFs ranging from 2.7 to 13 cpf peaking at 7.3 cpf are significantly correlated with pain discrimination. Our results highlight the importance of the mid-SFs in the visual processing of the facial expression of pain and suggest that any method removing these SFs offers an incomplete account of SFs diagnosticity. 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".