Visual search irregularities in schizophrenia depend on display size switching
Bibliographic record
Abstract
INTRODUCTION: In past research it has been demonstrated that when performing a visual search task with either one or multiple (4, 7 or 10) stimuli displayed, patients with schizophrenia demonstrate slow response times (RTs) in the display size of one, target-absent (one-absent) condition. The goals of the present investigation were to replicate this effect, and to gain an understanding of the underlying cognitive operations by comparing display-size switch to display-size repeat trials. METHODS: In two experiments, patients and controls performed a visual search task with either one or four stimuli displayed. In Experiment 1 (one block with mixed switch and repeat trials), RT for display-size switch trials was compared to RT from display-size repeat trials. In Experiment 2, the display-size one and display-size four conditions were run in separate, homogeneous blocks. RESULTS: The results demonstrate that the one-absent slowing effect was eliminated on repeat trials, regardless of whether the switch and repeat trials were mixed or presented in separate blocks. CONCLUSIONS: This set of results suggests that a combination of cueing and switching effects may underlie the one-absent slowing observed in patients, such that switching to the one-absent condition is difficult due to insufficient cueing of the relevant cognitive operations. This visual search paradigm is an excellent candidate for inclusion in the development of a neurocognitive profile specific to 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 | 0.003 |
| 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".