Traditional test administration and proactive interference undermine visual-spatial working memory performance in schizophrenia-spectrum disorders
Bibliographic record
Abstract
Introduction Working-memory (WM) is a core cognitive deficit among individuals with Schizophrenia Spectrum Disorders (SSD). However, the underlying cognitive mechanisms of this deficit are less known. This study applies a modified version of the Corsi Block Test to investigate the role of proactive interference in visuospatial WM (VSWM) impairment in SSD. Methods Healthy and SSD participants completed a modified version of the Corsi Block Test involving both high (typical ascending set size from 4 to 7 items) and low (descending set size from 7 to 4 items) proactive interference conditions. Results The results confirmed that the SSD group performed worse overall relative to a healthy comparison group. More importantly, the SSD group demonstrated greater VSWM scores under low (Descending) versus high (Ascending) proactive interference; this pattern is opposite to that of healthy participants. Conclusions This differential pattern of performance supports that proactive interference associated with the traditional administration format contributes to VSWM impairment in SSD. Further research investigating associated neurocognitive mechanisms and the contribution of proactive interference across other domains of cognition in SSD is warranted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".