Exploring cerebral laterality of writing and the relationship to handedness: a functional transcranial Doppler ultrasound investigation
Bibliographic record
Abstract
Cerebral lateralization of oral language has been investigated in a plethora of studies and it is well established that the left hemisphere is dominant for production tasks in the majority of individuals. However, few studies have focused on written language and even fewer have sampled left-handers. Writing comprises language and motor components, both of which contribute to cerebral activation, yet previous research has not disentangled. The aim of this study was to disentangle the language and motor components of writing lateralization. This was achieved through the comparison of cerebral activation during (i) written word generation and (ii) letter copying, as assessed by functional Transcranial Doppler (fTCD) ultrasound. We further assessed cerebral laterality of oral language. The sample was balanced for handedness. We preregistered the hypotheses that (i) cerebral lateralization of the linguistic component of writing would be weaker in left-handers compared to right-handers and (ii) oral language and the linguistic component of written language would not be correlated in terms of cerebral lateralization. No compelling evidence for either of our hypotheses was found. Findings highlight the complexity of the processes subserving written and oral language as well as the methodological challenges to isolate the linguistic component of writing.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".