Directions in Research into Response Selection Slowing in Schizophrenia
Bibliographic record
Abstract
People with schizophrenia experience many types of symptoms and a particularly difficult type are cognitive. Cognitive deficits are relevant to prognosis Response selection slowing is perhaps the most straightforward way to show a cognitive deficit in schizophrenia and has a long history in schizophrenia research. Response selection plays a role in virtually any task that requires a motor response; thus it is important to understand it fully in the context of schizophrenia to allow appropriate and valid interpretation of other cognitive tasks. Adding to its importance is that response selection may also be an endophenotype of schizophrenia. This paper will summarize recent results in neuroimaging of response selection in schizophrenia. As well, the best test of our understanding of the deficit in response selection slowing in schizophrenia will be through simulation, so progress in computational models using neural networks will also be examined. This chapter will begin by summarizing the long history of research in response selection slowing in schizophrenia. It will then proceed with a description of various neuroimaging techniques that are used in the studies that will be discussed. Following that, landmark studies in response selection in healthy people will be summarized followed by the discussion of studies in response selection in schizophrenia. The next-to-last section will highlight some research in simulation that show promise in modeling the differences in response selection in people with schizophrenia before ending with a concluding paragraph.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".