Cerebral lateralization during handwritten and typed word generation: A functional transcranial Doppler ultrasound study in left- and right-handers.
Bibliographic record
Abstract
The neural underpinnings of written language, similarly to the neural underpinnings of oral language, are left-lateralized. However, cerebral lateralization for written language has only been studied using handwriting tasks; the cerebral lateralization of keyboard typing, a popular alternative means of writing, has not been explored. Therefore, it remains unanswered whether handwriting and keyboard typing follow similar cerebral laterality patterns. The aim of the present preregistered study was to investigate cerebral laterality during typing versus handwriting and to further examine the presence of handedness differences. We hypothesized that: i) cerebral lateralization will not differ between the two methods of writing after movement correction (i.e., after subtracting the control condition from the main task); and ii) cerebral lateralization of both handwriting and typing will be weaker in left-handers compared to right-handers. In order to assess cerebral laterality, we employed functional transcranial Doppler (fTCD) ultrasound, which allows for a reliable assessment of cerebral laterality during language production tasks and is unaffected by movement, such as the movement generated during typing and handwriting. Twenty-four left-handers and 30 right-handers underwent fTCD while performing written word generation either by handwriting or by typing on a computer keyboard. We found evidence of an absence of a difference between the two methods of writing after movement correction. However, we did not find conclusive evidence for either a difference or an absence of a difference in the cerebral lateralization during either method of writing between left-handers and right-handers.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".