72. Behavioral and Neurobiological Correlates of Attention in Schizophrenia in a Virtual Environment
Bibliographic record
Abstract
Background: Deficits in attention are an enduring feature of schizophrenia that deters cognitive capacity and ultimately functional outcome. Existing tasks of attention are typically abstract, with limited relevance to everyday life. We evaluated attention in a virtual factory setting, where participants act as quality inspectors to identify defective objects on moving conveyor belts. Further, to examine the neurobiological underpinnings of attention in this simulated real-world setting, we evaluated associations between task performance and structural brain connectivity using diffusion tensor imaging (DTI). Methods: The virtual tasks consist of trials evaluating Selective Attention (SA, focusing on relevant stimuli amidst distracters), Divided Attention (DA, responding simultaneously to multiple demands), and Attentional Shift (ASh, shifting attention while avoiding distractions). In a behavioural validation phase, 50 schizophrenia patients (SZ) and 55 healthy controls (HC) completed the tasks and underwent clinical and cognitive characterization. A subsample of 20 SZ and 19 HC participants underwent DTI. Fractional anisotropy (FA) and mean diffusivity (MD) were computed using probabilistic white matter tractography of frontostriatal circuits implicated in attention and cognition. Results: SZ participants performed worse than HCs on DA (Mann-Whitney U = 662.5, Z = −4.58, P < .001) and ASh (U = 560.0, Z = −5.24, P < .001), but did not differ in SA. Within the SZ group, performance on all tasks correlated with the Trail Making Test (TMT) A (Spearman’s |ρ| = 0.33–0.48, P ≤ .02), DA and ASh performance correlated with the TMT B (|ρ| = 0.33–0.46, P ≤ .02), and ASh performance correlated with cognition (ρ = 0.32, P = .03) and motivation (ρ = 0.36, P = .01). In the DTI subsample, performance on all tasks correlated with reduced MD from right caudate to orbitofrontal cortex (OFC) (|ρ| = 0.34–0.40, P ≤ .04), and ASh performance correlated with MD from right caudate to dorsolateral prefrontal cortex (DLPFC) (ρ = −0.53, P = .001) and from left putamen to OFC (ρ = −0.36, P = .03). MD of the right caudate-DLPFC tract was also associated with ASh performance in SZ (ρ = −0.49, P = .04). Conclusion: The virtual tasks appear to be a valid means of evaluating attention, and may assess unique aspects of attention not captured by standard measures. Deficits in DA and ASh were particularly evident in SZ participants, and the latter seems associated with deficiencies in cognition and motivation that are central to the illness. The DTI findings suggest that frontostriatal circuitry may be shared across aspects of attention, but not entirely, and that deficits in ASh especially may be prominently linked with specific white matter tracts in SZ.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".