Impaired distractor inhibition in patients with schizophrenia on a negative priming task
Bibliographic record
Abstract
BACKGROUND: Numerous studies have suggested, via the interpretation of negative priming effects, that subjects with schizophrenia are less able than controls to inhibit irrelevant distracting information. Further issues concerning impairment in inhibitory processes are investigated here. First, recent research has revealed that negative priming (NP) effects can be caused by different processes, distractor inhibition or perceptual review. Therefore, conclusions concerning reduced inhibition in patients with schizophrenia are not possible from previous NP research. Secondly, previous NP studies have required subjects to identify some feature of the target. This is the first study to examine NP that uses a spatial task in patients with schizophrenia. METHOD: Twenty-eight subjects with schizophrenia and 28 age and sex matched non-psychiatric control subjects completed a computerized NP task that eliminated the possible contribution of perceptual review. RESULTS: Subjects with schizophrenia had reduced levels of NP compared to control subjects on this spatial NP task (t = 2.46, P < 0.02). Current age, positive, negative or total PANNS scores did not correlate with negative priming scores, but post hoc analyses revealed that clozapine-treated patients had significantly greater levels of negative priming than patients receiving typical antipsychotic medications. CONCLUSIONS: The present experiment eliminated the contribution of perceptual review to negative priming and demonstrated that when a pure measure of inhibition is taken on a localization task, patients with schizophrenia were less able to inhibit irrelevant distracting stimuli. The fact that NP was reduced in a spatial task suggested a more diffuse reduction in inhibition than previous studies that examined only identification-based responses.
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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 | 0.000 | 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 | 0.000 |
| 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".