Inhibiting reactions to emotional versus non‐emotional response cues in schizophrenia: Insights from a motor‐process paradigm
Bibliographic record
Abstract
Recent models of schizophrenia suggest deficient use of contextual response cues when confronted with countermanding emotional cues. It is important to clinically validate these models by testing patients diagnosed with schizophrenia on tasks with competing emotional and contextual response determinants. Control and schizophrenia groups completed a novel task that elicited motor responses consistent with, or in opposition to, pre-potent emotional actions (i.e., approach vs. avoidance). An analogous non-emotional task was also used to examine cue-conflict impairment more generally. The groups demonstrated statistically equivalent performance decrements on incongruent versus congruent trials on both tasks. However, within the schizophrenia group, the incongruency effect was significantly greater in the emotional versus non-emotional task. These data suggest that, while patients with schizophrenia were able to employ contextual response cues to override competing emotional responses, they were slower to resolve emotional versus non-emotional response conflict. When patients were subdivided according to the presence or absence of disorganized symptoms, this effect was confined to patients with disorganized symptoms.
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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 | 0.001 |
| 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 | 0.001 |
| 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".