Absence of Interhemispheric Transfer of Unilateral Visuomotor Learning in Young Children and Individuals With Agenesis of the Corpus Callosum
Bibliographic record
Abstract
This study was undertaken to investigate the role of the corpus callosum in interhemispheric transfer of unilateral visuomotor learning. In the first experiment, the cross-manual performance of 4 callosal agenesis participants was compared to that of 4 age- and IQ-matched controls. In the second experiment, normal children of different ages (6-7 and 11-12 years) and adults were submitted to the same task to assess the impact of callosal maturation on interhemispheric transfer. Participants had to make aiming movements from a starting position toward either a central or a lateral target on the same side as the hand used, while maintaining central fixation. Prior to training, a pretest was performed with the hand contralateral to the hand used during learning. Participants were then submitted to a posttest with the untrained hand. All participants learned the unilateral aiming task in the learning phase, as evidenced by a reduction in spatial errors with an increasing number of practice trials. However, acallosal participants and children aged 6 to 7 years failed to transfer the acquired skill from the trained to the untrained hemisphere. These findings suggest that interhemispheric transfer of visuomotor skills cannot be assumed by other structures in the case of agenesis or morphological immaturity of the corpus callosum. The results further indicate that unilateral visuomotor learning leads to the formation of a single, unihemispheric engram in the absence, whether functional or anatomical, of the corpus callosum.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".