Failure of language lateralization in schizophrenia patients: an ERP study on early linguistic components
Bibliographic record
Abstract
OBJECTIVE: In line with Crow's hypothesis, altered hemispheric lateralization of language would cause the main symptoms of schizophrenia. The present experiment aimed to demonstrate the loss of the hemispheric specialization for linguistic processing in schizophrenia patients at the level of early automatic evoked potentials (N150). METHODS: A sample of 10 outpatients with schizophrenia treated with low levels of neuroleptics and 10 matched healthy control subjects were administered 3 linguistic tasks based on stimulus pair comparisons (phonological, semantic and word-picture matching tasks). Laterality scores of early evoked potentials were analyzed during 2 time windows corresponding to the N150- and N400-like components. RESULTS: The patients failed to develop the typical left hemispheric N150 component evoked by the first word (S1), which was consistently achieved by the healthy control group in posterior sites (p < 0.01). The effect was specific and stable for linguistic stimuli. As well, for the N150 elicited by the target stimulus (S2), the patients exhibited a lack of linguistic lateralization. In the control task (word-picture matching task), in which S2 was a picture, the 2 groups revealed very similar bilateral recognition potentials. CONCLUSION: The results point to a failure of language lateralization in patients with schizophrenia, a deficit involving those linguistic networks automatically activated in the earliest phase of word recognition (N150). Consistent with the current view of schizophrenia, this finding may be related to lack of integration among specific processes and reduced interconnection of underlying linguistic networks.
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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".