A quantitative evaluation of the AVITEWRITE model of handwriting learning

Paine, Rainer W.; Grossberg, Stephen; Gemmert, Arend W. A. Van

doi:10.1016/j.humov.2004.08.024

Cited by 16 publications

(11 citation statements)

References 47 publications

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“…One of these models is the Edelman and Flash (1987) minimum snap model. In this work they presented a model of trajectory formation based on dynamic minimization of jerk [8,10]. Their approach was a computational one.…”

Section: Discussionmentioning

confidence: 99%

“…So when the model learns to write a letter it can use only memory to generate the letter trajectories. The overall approach is based upon the hypothesis that complex human movements are made up of, and can be segmented into, basic and simple parts [1,2,6,8]. In other words, due to the properties of the neuromuscular system involved in a rapid writing task, there is a class of simple movements, called strokes, that are preferentially produced by such a system, once it is well-trained.…”

Section: Introductionmentioning

confidence: 99%

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A behavioral model of writing

Naghibolhosseini

Bahrami

2008

2008 International Conference on Electrical and Computer Engineering

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In this paper we propose a new model to generate handwriting based on behavioral patterns we believe is to be found in humans when imitating a written character. The proposed algorithm has a hierarchical structure. It is consisted of two main levels. At the first level the graphical features of the written letter to be imitated are extracted. These features are the directions of movement for each stroke. To extract the strokes that shape a letter, zero crossings of vertical and horizontal spatial velocity profiles are determined. At the second part a given letter is regenerated by using the extracted directions. At this level, the letter is linearly divided into several subdivisions. After that the excursion length (step) and the slope of line-segments producing the subdivisions are estimated. In each trial of learning, trajectory points are chosen with the estimated step and a random slope. The final trajectory is produced by successive arrangement of the strokes. In each trial the slope and step of the target point has a distance less than a specified threshold from the actual path is stored in memory and others are generated again. This process will continue during different trials until the trajectory can be generated using only memory. The resulted trajectories for different letters written by different subjects are qualitatively and quantitatively similar to the actual trajectories generated by human.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A behavioral model of writing

Naghibolhosseini

Bahrami

2008

2008 International Conference on Electrical and Computer Engineering

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“…Unfortunately, Hollerbach's (1981) work, aiming to computer generation of handwriting-like traces, offered no method of parameter extraction from real, cursive handwriting produced by humans. We compared our POMH model to the Edelman and Flash model (EFM, Edelman and Flash 1987;Edelman et al 1990), already used elsewhere for comparison purposes (Paine et al 2004). The interest of EFM undertaking lies in its completeness: The model was ultimately designed to reconstruct any real handwriting trace and Edelman and Flash proposed a quantitative method to assess its matching the real traces.…”

Section: Comparison With Edelman-flash Model (Efm)mentioning

confidence: 99%

A parsimonious oscillatory model of handwriting

et al. 2014

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We propose an oscillatory model that is theoretically parsimonious, empirically efficient and biologically plausible. Building on Hollerbach's (Biol Cybern 39:139-156, 1981) model, our Parsimonious Oscillatory Model of Handwriting (POMH) overcomes the latter's main shortcomings by making it possible to extract its parameters from the trace itself and by reinstating symmetry between the [Formula: see text] and [Formula: see text] coordinates. The benefit is a capacity to autonomously generate a smooth continuous trace that reproduces the dynamics of the handwriting movements through an extremely sparse model, whose efficiency matches that of other, more computationally expensive optimizing methods. Moreover, the model applies to 2D trajectories, irrespective of their shape, size, orientation and length. It is also independent of the endeffectors mobilized and of the writing direction.

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“…Language development also requires adaptively timed learning to enable children to learn culturally important sequential motor actions through imitation. For example, cerebellar adaptive timing mechanisms, together with neocortical working memory mechanisms, model how children may learn handwriting through imitation of script (Grossberg and Paine 2000;Paine et al 2005). In the adult, conversation requires sustaining attention and the ability to flexibly disengage it.…”

Section: Interactions Between Adaptive Timing and Hyperspecific Learningmentioning

confidence: 99%

Neural Dynamics of Autistic Behaviors: Learning, Recognition, Attention, Emotion, Timing, and Social Cognition

Grossberg

2014

Comprehensive Guide to Autism

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A quantitative evaluation of the AVITEWRITE model of handwriting learning

Cited by 16 publications

References 47 publications

A behavioral model of writing

A behavioral model of writing

A parsimonious oscillatory model of handwriting

Neural Dynamics of Autistic Behaviors: Learning, Recognition, Attention, Emotion, Timing, and Social Cognition

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