Noise as a mechanism of anomalous face processing among persons with Schizophrenia
Bibliographic record
Abstract
There is substantial evidence that people with Schizophrenia (SCZ) have altered visual perception and cognition, including impaired face processing. However, the mechanism(s) underlying this observation are not yet known. Eye movement studies have found that people with SCZ do not direct their gaze to the most informative regions of the face (e.g., the eyes). This suggests that SCZ patients may be less able to extract the most relevant face information and therefore have decreased calculation efficiency. In addition, research with non-face stimuli indicates that SCZ is associated with increased levels of internal noise. Importantly, both calculation efficiency and internal noise have been shown to underpin face perception among healthy observers. Therefore, the current study applies noise masking to upright and inverted faces to determine if face processing deficits among those with SCZ are the result of changes in calculation efficiency, internal noise, or both. Consistent with previous results, SCZ participants exhibited higher contrast thresholds in order to identify masked target faces. However, higher thresholds were associated with increases in internal noise but unrelated to changes in calculation efficiency. These results suggest that SCZ-related face processing deficits are the result of a decreased noise-to-signal ratio. The source of increased processing noise among these patients is unclear, but may emanate from abnormal neural dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".