Coarse information drives confusion of perceived emotion in schizophrenia
Bibliographic record
Abstract
It is widely accepted that emotion processing is impaired in schizophrenic patients (SPs). It is also believed —although evidence to support this claim remain scarce— that SPs confuse (i.e. mis-categorize) some emotions more than healthy individuals. While previous work using the Bubbles technique (e.g. Lee et al., 2011) has revealed aberrant use of facial information that agrees with emotion processing deficit in SPs, their use of only two facial expressions made it difficult to study the sources of the confusions. Here, we examined this question using Bubbles in a four-facial-expression identification task (happy, fearful, angry, or neutral). We first mapped which parts of the face at different spatial scales were used by SPs, and confirmed and extended previous findings. Second, we computed a confusion matrix over the ~13,000 trials completed by all SPs (N=13). Four mis-categorizations had a proportion of responses significantly higher than what is expected by chance (all chi2>60, Bonferroni-corrected ps< .001), including angry faces confused for neutral faces and fearful faces confused for angry faces. Finally, we revealed the specific facial information that drove SPs to commit these two confusions. We discovered that low-spatial frequency (LSF, i.e. coarse information < 10 cycles per faces) from the mouth area and nose area (i.e. philtrum, nose and nasolabial folds), respectively, led to angry faces being mistaken for neutral faces and to fearful faces being mistaken for angry faces. Previous studies revealed magnocellular system abnormalities in the form of below average LSF sensitivity in SPs (Butler et al., 2009). Our results indicate that the inability to extract useful coarse spatial information from expressive faces underlies the mis-labeling of perceived emotions in SPs. 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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".