Reading impairments in schizophrenia relate to individual differences in phonological processing and oculomotor control: Evidence from a gaze-contingent moving window paradigm.
Bibliographic record
Abstract
Language and oculomotor disturbances are 2 of the best replicated findings in schizophrenia. However, few studies have examined skilled reading in schizophrenia (e.g., Arnott, Sali, Copland, 2011; Hayes & O'Grady, 2003; Revheim et al., 2006; E. O. Roberts et al., 2012), and none have examined the contribution of cognitive and motor processes that underlie reading performance. Thus, to evaluate the relationship of linguistic processes and oculomotor control to skilled reading in schizophrenia, 20 individuals with schizophrenia and 16 demographically matched controls were tested using a moving window paradigm (McConkie & Rayner, 1975). Linguistic skills supporting reading (phonological awareness) were assessed with the Comprehensive Test of Phonological Processing (R. K. Wagner, Torgesen, & Rashotte, 1999). Eye movements were assessed during reading tasks and during nonlinguistic tasks tapping basic oculomotor control (prosaccades, smooth pursuit) and executive functions (predictive saccades, antisaccades). Compared with controls, schizophrenia patients exhibited robust oculomotor markers of reading difficulty (e.g., reduced forward saccade amplitude) and were less affected by reductions in window size, indicative of reduced perceptual span. Reduced perceptual span in schizophrenia was associated with deficits in phonological processing and reduced saccade amplitudes. Executive functioning (antisaccade errors) was not related to perceptual span but was related to reading comprehension. These findings suggest that deficits in language, oculomotor control, and cognitive control contribute to skilled reading deficits in schizophrenia. Given that both language and oculomotor dysfunction precede illness onset, reading may provide a sensitive window onto cognitive dysfunction in schizophrenia vulnerability and be an important target for cognitive remediation.
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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 | 0.001 | 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".