A neural model of cortico-cerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements

Benefits of a Light-Painting Technique for Learning to Write New Characters: A Proof of Concept With Adults

Learning to write relies on the effective integration of sensory feedback and a transition from motor control based on written tracings to motor control based on expert writing movements. This study aimed to test whether the photographic technique of light painting (LP) would facilitate this transition. To achieve this, we conducted two experiments using different LP setups. LP involves moving a light source in a dark environment while taking a long-exposure photograph. LP entails both a real-time reduction of product-related visual information and a post-trial addition of process-related visual information. In the first experiment, we conducted a pre-test, training, and post-test in which 16 adults wrote four new characters with the non-dominant hand. During the training sessions, participants stood and wrote in a vertical frame (1 × 1.2 m) two characters in the control condition (with a marker on the vertical support) and two characters in the LP condition (with a flashlight in the air). In the test phases, participants were seated at a table and copied the four characters into a square (4 cm * 4 cm) on a fixed sheet of graphics paper. As in-air writing strongly differs from classical handwriting situations, we performed a second LP experiment. The aim was to implement LP training in a condition closer to writing. Sixteen new participants followed the same protocol but sat at a table and wrote in a horizontal square (20 cm * 20 cm). In both experiments, participants who trained with the LP method wrote faster and with less pressure than those trained in the control condition. We also observed an improvement in spatial accuracy in Experiment 2, whatever the training condition. LP seemed to have led participants to focus on the writing process, probably because it modified the nature and timing of the visual information used for writing. LP may be a promising technique for remediating writing difficulties.

Writing letters in two graphic systems: Behavioral and neural correlates in Latin-Arabic biscripters

Biscriptuality is the ability to read and write using two scripts. Despite the increasing number of biscripters, this phenomenon remains poorly understood. Here, we focused on investigating graphomotor processing in French-Arabic biscripters. We chose the French and Arabic alphabets because they have comparable visuospatial complexity and linguistic features, but differ dramatically in their graphomotor characteristics. In a first experiment we describe the graphomotor features of the two alphabets and showed that while Arabic and Latin letters are produced with the same velocity and fluency, Arabic letters require more pen lifts, contain more right-to-left strokes and clockwise curves, and take longer to write than Latin letters. These results suggest that Arabic and Latin letters are produced via different motor patterns. In a second experiment we used functional magnetic resonance imaging to ask whether writing the two scripts relies upon partially distinct or fully overlapping neural networks, and whether the elements of the previously described handwriting network are recruited to the same extent by the two scripts. We found that both scripts engaged the so-called "writing network", but that within the network, Arabic letters recruited the left superior parietal lobule (SPL) and the left primary motor cortex (M1) more strongly than Latin letters. Both regions have previously been identified as holding scale-invariant representations of letter trajectories. Arabic and Latin letters also activated distinct regions that do not belong to the writing network. Complementary analyses indicate that the differences observed between scripts at the neural level could be driven by the specific graphomotor features of each script. Overall, our results indicate that particular features of the practiced scripts can lead to different motor organization at both the behavioral and brain levels in biscripters.

Teaching and Rehabilitation of Handwriting for Children in the Digital Age: Issues and Challenges

Handwriting is a determining factor for academic success and autonomy for all children. Making knowledge accessible to all is a challenge in the context of inclusive education. Given the neurodevelopmental diversity within a classroom of children, ensuring that the handwriting of all pupils progresses is very demanding for education professionals. The development of tools that can take into account the variability of the profiles and learning abilities of children with handwriting difficulties offers a new potential for the development of specific and adapted remediation strategies. This narrative review aims to present and discuss the challenges of handwriting learning and the opportunities offered by new technologies involving AI for school and health professionals to successfully improve the handwriting skills of all children.

Toward Understanding the Brain Dynamics of Music: Learning and Conscious Performance of Lyrics and Melodies With Variable Rhythms and Beats

A neural network architecture models how humans learn and consciously perform musical lyrics and melodies with variable rhythms and beats, using brain design principles and mechanisms that evolved earlier than human musical capabilities, and that have explained and predicted many kinds of psychological and neurobiological data. One principle is called factorization of order and rhythm: Working memories store sequential information in a rate-invariant and speaker-invariant way to avoid using excessive memory and to support learning of language, spatial, and motor skills. Stored invariant representations can be flexibly performed in a rate-dependent and speaker-dependent way under volitional control. A canonical working memory design stores linguistic, spatial, motoric, and musical sequences, including sequences with repeated words in lyrics, or repeated pitches in songs. Stored sequences of individual word chunks and pitch chunks are categorized through learning into lyrics chunks and pitches chunks. Pitches chunks respond selectively to stored sequences of individual pitch chunks that categorize harmonics of each pitch, thereby supporting tonal music. Bottom-up and top-down learning between working memory and chunking networks dynamically stabilizes the memory of learned music. Songs are learned by associatively linking sequences of lyrics and pitches chunks. Performance begins when list chunks read word chunk and pitch chunk sequences into working memory. Learning and performance of regular rhythms exploits cortical modulation of beats that are generated in the basal ganglia. Arbitrary performance rhythms are learned by adaptive timing circuits in the cerebellum interacting with prefrontal cortex and basal ganglia. The same network design that controls walking, running, and finger tapping also generates beats and the urge to move with a beat.

The handwriting brain in middle childhood

While the brain network supporting handwriting has previously been defined in adults, its organization in children has never been investigated. We compared the handwriting network of 23 adults and 42 children (8- to 11-year-old). Participants were instructed to write the alphabet, the days of the week, and to draw loops while being scanned. The handwriting network previously described in adults (five key regions: left dorsal premotor cortex, superior parietal lobule (SPL), fusiform and inferior frontal gyri, and right cerebellum) was also strongly activated in children. The right precentral gyrus and the right anterior cerebellum were more strongly activated in adults than in children, while the left fusiform gyrus (FuG) was more strongly activated in children than in adults. Finally, we found that, contrary to adults, children recruited prefrontal regions to complete the writing task. This constitutes the first comparative investigation of the neural correlates of writing in children and adults. Our results suggest that the network supporting handwriting is already established in middle childhood. They also highlight the major role of prefrontal regions in learning this complex skill and the importance of right precentral regions and cerebellum in the performance of automated handwriting.

Investigating how children produce rotation and pointing movements when they learn to write letters

How do children learn to write letters? During writing acquisition, some letters may be more difficult to produce than others because certain movement sequences require more precise motor control (e.g., the rotation that produces curved lines like in letter O or the pointing movement to trace the horizontal bar of a T). Children of ages 6-10 (N = 108) wrote sequences of upper-case letters on a digitizer. They varied in the number of pointing and rotation movements. The data revealed that these movements required compensatory strategies in specific kinematic variables. For pointing movements there was a duration decrease that was compensated by an increase in in-air movement time. Rotation movements were produced with low maximal velocity but high minimal velocity. At all ages there was a global tendency to keep stability in the tempo of writing: pointing movements exhibited a duration trade-off whereas rotation movements required a trade-off on maximal and minimal velocity. The acquisition of letter writing took place between ages 6 and 7. At age 8 the children shifted focus to improving movement control. Writing automation was achieved around age 10 when the children controlled movement duration and fluency. This led to a significant increase in writing speed.

Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning

This article develops the iSTART neural model that proposes how specific imbalances in cognitive, emotional, timing, and motor processes that involve brain regions like prefrontal cortex, temporal cortex, amygdala, hypothalamus, hippocampus, and cerebellum may interact together to cause behavioral symptoms of autism. These imbalances include underaroused emotional depression in the amygdala/hypothalamus, learning of hyperspecific recognition categories that help to cause narrowly focused attention in temporal and prefrontal cortices, and breakdowns of adaptively timed motivated attention and motor circuits in the hippocampus and cerebellum. The article expands the model's explanatory range by, first, explaining recent data about Fragile X syndrome (FXS), mGluR, and trace conditioning; and, second, by explaining distinct causes of stereotyped behaviors in individuals with autism. Some of these stereotyped behaviors, such as an insistence on sameness and circumscribed interests, may result from imbalances in the cognitive and emotional circuits that iSTART models. These behaviors may be ameliorated by operant conditioning methods. Other stereotyped behaviors, such as repetitive motor behaviors, may result from imbalances in how the direct and indirect pathways of the basal ganglia open or close movement gates, respectively. These repetitive behaviors may be ameliorated by drugs that augment D2 dopamine receptor responses or reduce D1 dopamine receptor responses. The article also notes the ubiquitous role of gating by basal ganglia loops in regulating all the functions that iSTART models.

Motor control of handwriting in the developing brain: A review

This review focuses on the acquisition of writing motor aspects in adults, and in 5-to 12-year-old children without learning disabilities. We first describe the behavioural aspects of adult writing and dominant models based on the notion of motor programs. We show that handwriting acquisition is characterized by the transition from reactive movements programmed stroke-by-stroke in younger children, to an automatic control of the whole trajectory when the motor programs are memorized at about 10 years old. Then, we describe the neural correlates of adult writing, and the changes that could occur with learning during childhood. The acquisition of a new skill is characterized by the involvement of a network more restricted in space and where neural specificity is increased in key regions. The cerebellum and the left dorsal premotor cortex are of fundamental importance in motor learning, and could be at the core of the acquisition of handwriting.

Neurodevelopmental Hypothesis about the Etiology of Autism Spectrum Disorders

Previous models or hypotheses of autism spectral disorder (ASD) failed to take into full consideration the chronological and causal developmental trajectory, leading to the emergence of diverse phenotypes through a complex interaction between individual etiologies and environmental factors. Those phenotypes include persistent deficits in social communication and social interaction (criteria A in DSM-5), and restricted, repetitive patterns of behavior, interests, or activities (criteria B in DSM-5). In this article, we proposed a domain-general model that can explain criteria in DSM-5 based on the assumption that the same etiological mechanism would trigger the various phenotypes observed in different individuals with ASD. In the model, we assumed the following joint causes as the etiology of autism: (1) Hypoplasia of the pons in the brainstem, occurring immediately following neural tube closure; and (2) Deficiency in the GABA (γ-aminobutyric acid) developmental switch during the perinatal period. Microstructural abnormalities of the pons directly affect both the structural and functional development of the brain areas strongly connected to it, especially amygdala. The impairment of GABA switch could not only lead to the deterioration of inhibitory processing in the neural network, but could also cause abnormal cytoarchitecture. We introduced a perspective that atypical development in both brain structure and function can give full explanation of diverse phenotypes and pathogenetic mechanism of ASD. Finally, we discussed about neural mechanisms underlying the phenotypic characteristics of ASD that are not described in DSM-5 but should be considered as important foundation: sleep, global precedence, categorical perception, intelligence, interoception and motor control.

A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness

How do the hippocampus and amygdala interact with thalamocortical systems to regulate cognitive and cognitive-emotional learning? Why do lesions of thalamus, amygdala, hippocampus, and cortex have differential effects depending on the phase of learning when they occur? In particular, why is the hippocampus typically needed for trace conditioning, but not delay conditioning, and what do the exceptions reveal? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later do not? Why do thalamic or sensory cortical lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions during trace conditioning experiments degrade recent but not temporally remote learning? Why do orbitofrontal cortical lesions degrade temporally remote but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of prefrontal cortex after memory consolidation? How are attention and consciousness linked during conditioning? How do neurotrophins, notably brain-derived neurotrophic factor (BDNF), influence memory formation and consolidation? Is there a common output path for learned performance? A neural model proposes a unified answer to these questions that overcome problems of alternative memory models.

What's Special about Human Imitation? A Comparison with Enculturated Apes

What, if anything, is special about human imitation? An evaluation of enculturated apes’ imitation skills, a “best case scenario” of non-human apes’ imitation performance, reveals important similarities and differences between this special population of apes and human children. Candidates for shared imitation mechanisms include the ability to imitate various familiar transitive responses and object–object actions that involve familiar tools. Candidates for uniquely derived imitation mechanisms include: imitating novel transitive actions and novel tool-using responses as well as imitating opaque or intransitive gestures, regardless of familiarity. While the evidence demonstrates that enculturated apes outperform non-enculturated apes and perform more like human children, all apes, regardless of rearing history, generally excel at imitating familiar, over-rehearsed responses and are poor, relative to human children, at imitating novel, opaque or intransitive responses. Given the similarities between the sensory and motor systems of preschool age human children and non-human apes, it is unlikely that differences in sensory input and/or motor-output alone explain the observed discontinuities in imitation performance. The special rearing history of enculturated apes—including imitation-specific training—further diminishes arguments suggesting that differences are experience-dependent. Here, it is argued that such differences are best explained by distinct, specialized mechanisms that have evolved for copying rules and responses in particular content domains. Uniquely derived social and imitation learning mechanisms may represent adaptations for learning novel communicative gestures and complex tool-use. Given our species’ dependence on both language and tools, mechanisms that accelerated learning in these domains are likely to have faced intense selective pressures, starting with the earliest of human ancestors.

From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control

This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory.

Précis on The Cognitive-Emotional Brain

In The Cognitive-Emotional Brain (Pessoa 2013), I describe the many ways that emotion and cognition interact and are integrated in the brain. The book summarizes five areas of research that support this integrative view and makes four arguments to organize each area. (1) Based on rodent and human data, I propose that the amygdala's functions go beyond emotion as traditionally conceived. Furthermore, the processing of emotion-laden information is capacity limited, thus not independent of attention and awareness. (2) Cognitive-emotional interactions in the human prefrontal cortex (PFC) assume diverse forms and are not limited to mutual suppression. Particularly, the lateral PFC is a focal point for cognitive-emotional interactions. (3) Interactions between motivation and cognition can be seen across a range of perceptual and cognitive tasks. Motivation shapes behavior in specific ways – for example, by reducing response conflict or via selective effects on working memory. Traditional accounts, by contrast, typically describe motivation as a global activation independent of particular control demands. (4) Perception and cognition are directly influenced by information with affective or motivational content in powerful ways. A dual competition model outlines a framework for such interactions at the perceptual and executive levels. A specific neural architecture is proposed that embeds emotional and motivational signals into perception and cognition through multiple channels. (5) A network perspective should supplant the strategy of understanding the brain in terms of individual regions. More broadly, in a network view of brain architecture, “emotion” and “cognition” may be used as labels of certain behaviors, but will not map cleanly into compartmentalized pieces of the brain.

The minimum transition hypothesis for intermittent hierarchical motor control

In intermittent control, instead of continuously calculating the control signal, the controller occasionally changes this signal at certain sparse points in time. The control law may include feedback, adaptation, optimization, or any other control strategies. When, where, and how does the brain employ intermittency as it controls movement? These are open questions in motor neuroscience. Evidence for intermittency in human motor control has been repeatedly observed in the neural control of movement literature. Moreover, some researchers have provided theoretical models to address intermittency. Even so, the vast majority of current models, and I would dare to say the dogma in most of the current motor neuroscience literature involves continuous control. In this paper, I focus on an area in which intermittent control has not yet been thoroughly considered, the structure of muscle synergies. A synergy in the muscle space is a group of muscles activated together by a single neural command. Under the assumption that the motor control is intermittent, I present the minimum transition hypothesis (MTH) and its predictions with regards to the structure of muscle synergies. The MTH asserts that the purpose of synergies is to minimize the effort of the higher level in the hierarchy by minimizing the number of transitions in an intermittent control signal. The implications of the MTH are not only for the structure of the muscle synergies but also to the intermittent and hierarchical nature of the motor system, with various predictions as to the process of skill learning, and important implications to the design of brain machine interfaces and human robot interaction.

A modular neural model of motor synergies

Animals such as reptiles, amphibians and mammals (including humans) are mechanically extremely complex. It has been estimated that the human body has between 500 and 1400 degrees of freedom! And yet, these animals can generate an infinite variety of very precise, complicated and goal-directed movements in continuously changing and uncertain environments. Understanding how this is achieved is of great interest to both biologists and engineers. There are essentially two questions that must be addressed: (1) What type of control strategy is used to handle the large number of degrees of freedom involved? and (2) How is this strategy instantiated in the substrate of neural and musculoskeletal elements comprising the animal bodies? The first question has been studied intensively for several decades, providing strong indications that, rather than using standard feedback control based on continuous tracking of desired trajectories, animals' movements emerge from the controlled combination of pre-configured movement primitives or synergies. These synergies represent coordinated activity patterns over groups of muscles, and can be triggered as a whole with controlled amplitude and temporal offset. Complex movements can thus be constructed from the appropriate combination of a relatively small number of synergies, greatly simplifying the control problem. Although experimental studies on animal movements have confirmed the existence of motor synergies, and their utility has been demonstrated in the control of fairly complex robots, their neural basis remains poorly understood. In this paper, we introduce a simple but plausible and general neural model for motor synergies based on the principle that these functional modules reflect the structural modularity of the underlying physical system. Using this model, we show that a small set of synergies selected through a redundancy-reduction principle can generate a rich motor repertoire in a model two-jointed arm system. We investigate the synergies generated by this model systematically with respect to various parameters, and compare them to those observed in experiments.

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

An intimate link exists between the predictive and learning processes in the brain. Perceptual/cognitive and spatial/motor processes use complementary predictive mechanisms to learn, recognize, attend and plan about objects in the world, determine their current value, and act upon them. Recent neural models clarify these mechanisms and how they interact in cortical and subcortical brain regions. The present paper reviews and synthesizes data and models of these processes, and outlines a unified theory of predictive brain processing.

A phase dynamic model of systematic error in simple copying tasks

A crucial insight into handwriting dynamics is embodied in the idea that stable, robust handwriting movements correspond to attractors of an oscillatory dynamical system. We present a phase dynamic model of visuomotor performance involved in copying simple oriented lines. Our studies on human performance in copying oriented lines revealed a systematic error pattern in orientation of drawn lines, i.e., lines at certain orientation are drawn more accurately than at other values. Furthermore, human subjects exhibit "flips" in direction at certain characteristic orientations. It is argued that this flipping behavior has its roots in the fact that copying process is inherently ambiguous-a line of given orientation may be drawn in two different (mutually opposite) directions producing the same end result. The systematic error patterns seen in human copying performance is probably a result of the attempt of our visuomotor system to cope with this ambiguity and still be able to produce accurate copying movements. The proposed nonlinear phase-dynamic model explains the experimentally observed copying error pattern and also the flipping behavior with remarkable accuracy.

The contribution of the parietal lobes to speaking and writing

The left parietal lobe has been proposed as a major language area. However, parietal cortical function is more usually considered in terms of the control of actions, contributing both to attention and cross-modal integration of external and reafferent sensory cues. We used positron emission tomography to study normal subjects while they overtly generated narratives, both spoken and written. The purpose was to identify the parietal contribution to the modality-specific sensorimotor control of communication, separate from amodal linguistic and memory processes involved in generating a narrative. The majority of left and right parietal activity was associated with the execution of writing under visual and somatosensory control irrespective of whether the output was a narrative or repetitive reproduction of a single grapheme. In contrast, action-related parietal activity during speech production was confined to primary somatosensory cortex. The only parietal area with a pattern of activity compatible with an amodal central role in communication was the ventral part of the left angular gyrus (AG). The results of this study indicate that the cognitive processing of language within the parietal lobe is confined to the AG and that the major contribution of parietal cortex to communication is in the sensorimotor control of writing.

The cerebellum, cerebellar disorders, and cerebellar research--two centuries of discoveries

Research on the cerebellum is evolving rapidly. The exquisiteness of the cerebellar circuitry with a unique geometric arrangement has fascinated researchers from numerous disciplines. The painstaking works of pioneers of these last two centuries, such as Rolando, Flourens, Luciani, Babinski, Holmes, Cajal, Larsell, or Eccles, still exert a strong influence in the way we approach cerebellar functions. Advances in genetic studies, detailed molecular and cellular analyses, profusion of brain imaging techniques, emergence of behavioral assessments, and reshaping of models of cerebellar function are generating an immense amount of knowledge. Simultaneously, a better definition of cerebellar disorders encountered in the clinic is emerging. The essentials of a trans-disciplinary blending are expanding. The analysis of the literature published these last two decades indicates that the gaps between domains of research are vanishing. The launch of the society for research on the cerebellum (SRC) illustrates how cerebellar research is burgeoning. This special issue gathers the contributions of the inaugural conference of the SRC dedicated to the mechanisms of cerebellar function. Contributions were brought together around five themes: (1) cerebellar development, death, and regeneration; (2) cerebellar circuitry: processing and function; (3) mechanisms of cerebellar plasticity and learning; (4) cerebellar function: timing, prediction, and/or coordination?; (5) anatomical and disease perspectives on cerebellar function.

Poor penmanship in children correlates with abnormal rhythmic tapping: a broad functional temporal impairment

Timing is crucial for proficient motor tasks; temporal impairments may lead to dysfunctional motor activities. Although much research has been dedicated to the study of movement timing, clinical examination often overlooks temporal impairment of motor activity. The authors hypothesize that some children have a global temporal impairment leading to dysfunctional motor skills. This article checks whether temporal abnormalities detected on a simple tapping task correlate with temporal dysfunction during complex motor skills such as handwriting. Twenty-three school-aged children, 8-14 years (11.1 +/- 1.3 years), underwent tests to assess finger tapping and cursive handwriting. Handwriting samples were rated by experienced teachers. Children with abnormal tapping had lower handwriting rating scores. Temporal features were similar in both tasks; variability on the tapping test correlated with handwriting variability. Temporal variability was not significantly higher for children with poor penmanship as a whole but rather specific to the subgroup of children with a tapping abnormality. Poor penmanship could be attributed in certain children to global temporal impairment reflected as variable finger tapping and handwriting. Evaluation of dysfunctional motor performance should include temporal aspects, and further studies are needed to better delineate and address treatment of "dysrhythmia."

Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates

What brain mechanisms underlie autism, and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the Imbalanced Spectrally Timed Adaptive Resonance Theory (iSTART) model, that proposes how cognitive, emotional, timing, and motor processes that involve brain regions such as the prefrontal and temporal cortex, amygdala, hippocampus, and cerebellum may interact to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes, notably a combination of underaroused emotional depression in the amygdala and related affective brain regions, learning of hyperspecific recognition categories in the temporal and prefrontal cortices, and breakdowns of adaptively timed attentional and motor circuits in the hippocampal system and cerebellum. The model clarifies how malfunctions in a subset of these mechanisms can, through a systemwide vicious circle of environmentally mediated feedback, cause and maintain problems with them all.

A quantitative evaluation of the AVITEWRITE model of handwriting learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an inverse relation between curvature and speed. The adaptive vector integration to endpoint handwriting (AVITEWRITE) model of Grossberg and Paine (2000) [A neural model of corticocerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements. Neural Networks, 13, 999-1046] addressed how such complex movements may be learned through attentive imitation. The model suggested how parietal and motor cortical mechanisms, such as difference vector encoding, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psychophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a two-thirds power law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing the human trajectories. The results show that model performance was variable across the subjects, with an average correlation between the model and human data of 0.89+/-0.10. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and the learning of other complex sensory-motor skills would benefit from further research.

A via-point time optimization algorithm for complex sequential trajectory formation

In our previous research, we proposed a method for the reproduction of complex movement trajectories and robot arm control that could mimic fast, skilled human movements. That method is based on bi-directional theory and uses a representation of a set of via-points as boundary conditions or control variables to perform robot arm trajectory control. The via-points are extracted from human movement data and the resultant via-point representation is able to regenerate handwritten characters, control a Kendama toy, and perform a tennis serve. The via-point information contains both spatial and temporal information, that is, the position on the trajectory and the time of passing through the via-point position, respectively. Trajectory generation is performed using the trajectory formation model based on the optimal criterion, namely, the smoothness criterion, for which the boundary conditions are both the position and the timing of the via-point information. However, generating a smooth trajectory at different movement speeds is quite difficult if the time of passing through the via-point position is unknown or different from the extracted via-point time. In this paper, we therefore propose a new algorithm which can determine temporal via-point information. Our proposed algorithm can generate roughly the same trajectory as the measured human trajectory from only the spatial information of via-point locations. The optimality and the convergence of the new algorithm are investigated theoretically, and the trajectory generated by the algorithm is shown in numerical experiments. It is shown that starting from arbitrary temporal information the proposed algorithm can produce an appropriate trajectory.

Central mechanisms of motor skill learning

Recent studies have shown that frontoparietal cortices and interconnecting regions in the basal ganglia and the cerebellum are related to motor skill learning. We propose that motor skill learning occurs independently and in different coordinates in two sets of loop circuits: cortex-basal ganglia and cortex-cerebellum. This architecture accounts for the seemingly diverse features of motor learning.