[Prefrontal dysfunction in schizophrenia: implication is associative recognition].
Bibliographic record
Abstract
OBJECTIVE: We used an event related functional magnetic resonance imaging (fMRI) method to examine the neural basis of associative recognition memory deficit in schizophrenia. METHODS: Fifteen people with schizophrenia and 18 healthy control subjects were scanned with fMRI while performing a memory task (coding and recognition) of visual objects. During coding, the subjects studied items and pairs of items. During recovery, the subjects had to recognize items (old/new decisions) and recognize associations (intact/rearranged decisions). The study design was based on a random effect model and the fMRI analysis was restricted to correct items only. RESULTS: At the behavioral level, both groups performed equally well on item recognition, whereas people with schizophrenia demonstrated poorer performance on associative recognition. At the brain level, comparison between associative and item recognition tasks revealed greater left dorsolateral prefrontal and right inferior prefrontal activations in the control group relative to the schizophrenia group. CONCLUSIONS: The findings of this fMRI study suggest the prefrontal cortex as the basis for the selective memory deficit for associative recognition observed in schizophrenia.
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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) | 0.003 | 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".