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

Many automatic movements are open-loop, feed-forward motor prog ra ms (MP) that are kinematically well characterized by smooth speed and acceleration curves. However, it is unclear whether their internal representation consis ts of monolithic blocks or subroutines. This question was investigated usin g a learning paradigm of a writing task. Fifty-nine normal subjects were pr esented with two similar, but different new letters. Every subject practice d each letter in a series of 60 trials, with the order of letter series ran domized. Every session was continuously recorded by a digitizing tablet. Us ing kinematic analysis, we measured the number of vertical acceleration pea ks as an indication of the number of corrective movements (COM). Since COM declined as automatization was approached, we could quantitatively infer pr ogress 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 t he 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 an d the second series; there was a much greater reduction of COM in the secon d series (50.1 vs. 41.1%). This difference can be logically ascribed to tra nsfer, indicating that automated movements are stored in motor subroutines. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved.