Deficits in automatically detecting changes in conjunction of auditory features in patients with schizophrenia
Bibliographic record
Abstract
Disturbances in processing simple acoustic changes in a stream of stimuli have been widely reported in patients with schizophrenia, but little is know about auditory feature conjunction in these individuals. This study was designed to examine the extent to which patients with schizophrenia automatically process changes in conjunction of auditory features by using event-related brain potentials. Seventeen patients and 17 age-matched controls were presented with frequent low pitch tones at 45 degrees to the left of center and frequent high pitch tones at 45 degrees to the right of center while performing a continuous visual serial-choice reaction time task. The sequence of auditory stimuli included rare conjunction-deviants comprised of a different combination of features (e.g., low pitch tone at 45 degrees right) and double-deviant tones that differed from the standard tones in both pitch and location (i.e., middle pitch at 0 degrees azimuth). Conjunction-deviant stimuli elicited an MMN wave that was maximum at frontocentral sites. Compared with controls, the MMN to conjunction-deviant was reduced in patients and was more centrally distributed. Double-deviant sounds generated a biphasic MMN followed by a P3a wave at central sites. Both MMN and P3a were reduced in patients compared with controls. These results show that patients with schizophrenia have difficulty in automatically detecting changes in a combination of auditory features as well as orienting to what "normally" would be considered salient by healthy individuals.
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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 | 0.000 | 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".