Learning to write letters: transfer in automated movements indicates modularity of motor programs in human subjects

• Published in: Neuroscience letters Vol. 282; no. 1; pp. 33 - 36
• Main Authors: Kharraz-Tavakol, Oliver D., Eggert, Thomas, Mai, Norbert, Straube, Andreas
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier Ireland Ltd 17.03.2000; Elsevier
• Subjects: Automatization; Biological and medical sciences; Biomechanical Phenomena; Fundamental and applied biological sciences. Psychology; Handwriting; Humans; Language; Learning; Learning - physiology; Motor program; Movement - physiology; Production and perception of written language; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychomotor Performance - physiology; Transfer; Writing; Learning; Transfer; Writing; Automatization; Motor program; Human; Automation; Hand writing; Acquisition process; Language; Cognition; Module; Motricity; Motor learning
• ISSN: 0304-3940; 1872-7972
• DOI: 10.1016/S0304-3940(00)00851-X

Many automatic movements are open-loop, feed-forward motor programs (MP) that are kinematically well characterized by smooth speed and acceleration curves. However, it is unclear whether their internal representation consists of monolithic blocks or subroutines. This question was investigated using a learning paradigm of a writing task. Fifty-nine normal subjects were presented with two similar, but different new letters. Every subject practiced each letter in a series of 60 trials, with the order of letter series randomized. Every session was continuously recorded by a digitizing tablet. Using kinematic analysis, we measured the number of vertical acceleration peaks as an indication of the number of corrective movements (COM). Since COM declined as automatization was approached, we could quantitatively infer progress in motor learning under natural learning conditions. In the case of modular storage of MP, transfer in-between letters was expected due to the re-use of pre-learned motor subroutines. Statistical analysis showed that the exponential model described the data much better than the linear model (residual error: P<0.88 and P<0.00001, respectively), as expected for a learning paradigm. There was no difference between letters per se ( P<0.77). Motor improvement differed significantly ( P<0.02) between the first and the second series; there was a much greater reduction of COM in the second series (50.1 vs. 41.1%). This difference can be logically ascribed to transfer, indicating that automated movements are stored in motor subroutines.