title: The lognormal handwriter: learning, performing, and declining.
abstract: The generation of handwriting is a complex neuromotor skill requiring the interaction of many cognitive processes. It aims at producing a message to be imprinted as an ink trace left on a writing medium. The generated trajectory of the pen tip is made up of strokes superimposed over time. The Kinematic Theory of rapid human movements and its family of lognormal models provide analytical representations of these strokes, often considered as the basic unit of handwriting. This paradigm has not only been experimentally confirmed in numerous predictive and physiologically significant tests but it has also been shown to be the ideal mathematical description for the impulse response of a neuromuscular system. This latter demonstration suggests that the lognormality of the velocity patterns can be interpreted as reflecting the behavior of subjects who are in perfect control of their movements. To illustrate this interpretation, we present a short overview of the main concepts behind the Kinematic Theory and briefly describe how its models can be exploited, using various software tools, to investigate these ideal lognormal behaviors. We emphasize that the parameters extracted during various tasks can be used to analyze some underlying processes associated with their realization. To investigate the operational convergence hypothesis, we report on two original studies. First, we focus on the early steps of the motor learning process as seen as a converging behavior toward the production of more precise lognormal patterns as young children practicing handwriting start to become more fluent writers. Second, we illustrate how aging affects handwriting by pointing out the increasing departure from the ideal lognormal behavior as the control of the fine motricity begins to decline. Overall, the paper highlights this developmental process of merging toward a lognormal behavior with learning, mastering this behavior to succeed in performing a given task, and then gradually deviating from it with aging.
authors.affiliation: Laboratoire Scribens, Département de Génie Électrique, École Polytechnique de Montréal Montréal, QC, Canada.
authors.forename: Réjean
authors.initials: R
authors.lastname: Plamondon
authors.affiliation: Laboratoire Scribens, Département de Génie Électrique, École Polytechnique de Montréal Montréal, QC, Canada ; Département de psychiatrie, Université de Montréal Montréal, QC, Canada.
authors.forename: Christian
authors.initials: C
authors.lastname: O'Reilly
authors.affiliation: Département de Mathématiques et Informatique, LAMIA, Université des Antilles et de la Guyanne, Campus de Fouillole Pointe-à-Pitre, Guadeloupe, France.
authors.forename: Céline
authors.initials: C
authors.lastname: Rémi
authors.affiliation: Département de Mathématiques et Informatique, LAMIA, Université des Antilles et de la Guyanne, Campus de Fouillole Pointe-à-Pitre, Guadeloupe, France.
authors.forename: Thérésa
authors.initials: T
authors.lastname: Duval
keywords: aging; handwriting analysis and generation; kinematic theory; learning; lognormal models; lognormality; neuromuscular systems