Cerebral Lateralization During Handwritten and Typed Word Generation: A Functional Transcranial Doppler Ultrasound Study in Left‐Handers and Right‐Handers
Bibliographic record
Abstract
Cerebral lateralization of written language, much like oral language, is predominantly left lateralized. However, handwriting has been the primary focus in lateralization studies. The cerebral lateralization of typing-a widely used method of writing-remains unexamined. This preregistered study aimed to explore the cerebral lateralization of typing versus handwriting and to investigate possible handedness-related differences. We hypothesized that (i) cerebral lateralization would not differ between the two writing methods after movement correction and (ii) both handwriting and typing would show weaker lateralization in left-handers compared to right-handers. To investigate this, we used functional transcranial Doppler (fTCD) ultrasound, a reliable method for assessing cerebral lateralization during language tasks that remains unaffected by movement artifacts, such as those generated by handwriting and typing. A total of 24 left-handers and 30 right-handers participated, performing written word generation through both handwriting and typing on a computer keyboard while undergoing fTCD assessment. We applied a Bayesian framework for our analysis, as it enables us to demonstrate the absence of a difference (i.e., no difference between two variables), which is not possible with the use of p values (estimated under the frequentist framework). Our results provided evidence supporting the absence of a difference in cerebral lateralization between handwriting and typing after movement correction. However, we found no conclusive evidence to either support or refute a difference in lateralization between left-handers and right-handers, suggesting that more research is needed to clarify the role of handedness in cerebral lateralization for different writing methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".