ACC Neuro-over-Connectivity Is Associated with Mathematically Modeled Additional Encoding Operations of Schizophrenia Stroop-Task Performance
Bibliographic record
Abstract
Functional magnetic resonance imaging at 7.0 Tesla was undertaken among Schizophrenia participants (Sz), and clinical (major mood disorder; MDD) and healthy controls (HC), during performance of the Stoop task. Stroop conditions included congruent and incongruent word color items, color-only items, and word-only items. Previous modeling results extended to this most widely used selective-attention task. All groups executed item-encoding operations (subprocesses of the item encoding process) at the same rate (performance accuracy being similarly high throughout), thus displaying like processing capacity; Sz participants, however, employed more subprocesses for item completions than did the MDD participants, who in turn used more subprocesses than the HC group. The reduced efficiency in deploying cognitive-workload capacity among the Sz participants was paralleled by more diffuse neuroconnectivity (Blood-Oxygen-Level-Dependent co-activation) with the anterior cingulate cortex (ACC) (Broadman Area 32), spreading away from this encoding-intensive region; and by less evidence of network dissociation across Stroop conditions. Estimates of cognitive work done to accomplish item completion were greater for the Sz participants, as were estimates of entropy in both the modeled trial-latency distribution, and its associated neuro-circuitry. Findings are held to be symptom and assessment significant, and to have potential implications for clinical intervention.
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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) | 0.001 | 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".