Individual differences in reading skill and language lateralisation: A cluster analysis
Bibliographic record
Abstract
Individual differences in reading and cerebral lateralisation were investigated in 200 college students who completed reading assessments and divided visual field word recognition tasks, and received a structural MRI scan. Prior studies on this data set indicated that little variance in brain-behaviour correlations could be attributed to the effects of sex and handedness variables (Chiarello, Welcome, Halderman, & Leonard, 2009; Chiarello, Welcome, Halderman, Towler, et al., 2009; Welcome et al., 2009). Here a more bottom-up approach to behavioural classification (cluster analysis) was used to explore individual differences that need not depend on a priori decisions about relevant subgroups. The cluster solution identified four subgroups of college age readers with differing reading skill and visual field lateralisation profiles. These findings generalised to measures that were not included in the cluster analysis. Poorer reading skill was associated with somewhat reduced VF asymmetry, while average readers demonstrated exaggerated RVF/left hemisphere advantages. Skilled readers had either reduced asymmetries, or asymmetries that varied by task. The clusters did not differ by sex or handedness, suggesting that there are identifiable sources of variance among individuals that are not captured by these standard participant variables. All clusters had typical leftward asymmetry of the planum temporale. However, the size of areas in the posterior corpus callosum distinguished the two subgroups with high reading skill. A total of 17 participants, identified as multivariate outliers, had unusual behavioural profiles and differed from the remainder of the sample in not having significant leftward asymmetry of the planum temporale. A less buffered type of neurodevelopment that is more open to the effects of random genetic and environmental influences may characterise such individuals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".