Procedural Learning in Schizophrenia Can Reflect the Pharmacologic Properties of the Antipsychotic Treatments
Bibliographic record
Abstract
BACKGROUND: Conventional and atypical antipsychotics have different affinities for D2 receptors, and these receptors are principally located in the striatum. Given that this cerebral structure was previously found to play a major role in procedural learning, the antipsychotic treatment in schizophrenia may be determinant for the procedural learning profile of these patients. OBJECTIVE: The current study was aimed at verifying whether procedural learning differs in patients with schizophrenia treated with conventional antipsychotics and patients treated with atypical antipsychotics. METHOD: Forty-five patients with schizophrenia were divided into 3 different groups according to their pharmacologic treatment: (1) haloperidol, a classical neuroleptic with high D2 receptor affinity; (2) clozapine, an atypical neuroleptic with practically no D2 receptor affinity; and (3) risperidone, an atypical neuroleptic that nevertheless shows high D2 receptor affinity. Patients were compared to 35 control subjects on a visuomotor procedural learning task (mirror drawing). RESULTS: All patients were able to learn the task. However, those treated with haloperidol showed some degree of learning impairment, while those treated with clozapine or risperidone did not show this impairment. In addition, performance per se, regardless of the learning, was found to be affected in the haloperidol and risperidone, but not in the clozapine groups. CONCLUSION: Procedural learning in schizophrenia may be differentially affected, depending on the pharmacologic profiles of the antipsychotics used for the treatment of this illness.
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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) | 0.000 | 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".