The beneficial influence of inattention on visual interference in schizophrenia.
Bibliographic record
Abstract
OBJECTIVE: Schizophrenia is associated with poor spatial attention. However, although this deficit undermines the perception of target information, it may be helpful for ignoring irrelevant inputs. The present study examined whether event-related brain potential (ERP) indices of visual spatial attention predicted the magnitude of the brain response to interference in schizophrenia. METHOD: ERPs were recorded in 16 schizophrenia patients and 20 healthy control participants who had to indicate whether the target E was global or local in compound letter stimuli. The nontarget could be either highly similar to the target (i.e., a global E composed of local Ss and vice versa) and thus produce more interference, or it could be dissimilar (i.e., a global E composed of local Hs and vice versa) and generate less interference. RESULTS: Both groups' responses were slowed by interference. Voltage amplitudes of the P1, and of ERP interference effects from 300-500 ms after stimulus onset, were significantly smaller in schizophrenia patients than in healthy participants when the target was global. In patients, larger P1 amplitudes were correlated with larger interference effects and with more severe symptoms of attentional deficits and conceptual disorganization. Schizophrenia participants thus exhibited abnormal ERPs to interference despite normal behavioral performance. CONCLUSIONS: Schizophrenia patients likely pay less attention to stimuli in general; however, the impact of this impairment on target detection is compensated by relatively greater inattention to irrelevant components of the stimuli, and this explains why they are not more influenced by interference than healthy participants at the behavioral level. (PsycINFO Database Record
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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 | 0.001 |
| 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 | 0.000 |
| 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".