When practice leads to co-articulation: the evolution of geometrically defined movement primitives

Sosnik, Ronen; Hauptmann, Björn; Karni, Avi; Flash, Tamar

doi:10.1007/s00221-003-1799-4

Cited by 149 publications

(193 citation statements)

References 39 publications

Supporting

Mentioning

182

Contrasting

Order By: Relevance

“…This learning may and may not be effector-specific . Effector-specific sequence learning is indicated by performance reduction when another effector (finger or hand) is used than during practice, and that has been observed in many different motor tasks (Doya, 2000;MacNeilage, 1970;Mattys, 2004;Park & Shea, 2003;Shea & Wulf, 2005;Sosnik, Hauptmann, Karni, & Flash, 2004), including the serial RT task (Deroost, Zeeuws, & Soetens, 2005;Keele et al, 1995;Verwey & Clegg, 2005). In the serial RT task, effector-specific learning has even been demonstrated by the capacity to learn a sequence with the fingers of one hand while the other hand performs a random sequence (Berner & Hoffmann, 2009).…”

Section: External Sequence Control: Speeding Up Reactionsmentioning

confidence: 99%

A cognitive framework for explaining serial processing and sequence execution strategies

2014

View full text Add to dashboard Cite

Behavioral research has produced many taskspecific cognitive models that do not say much about the underlying information-processing architecture. Such an architecture is badly needed to better understand how cognitive neuroscience can benefit from existing cognitive models. This problem is especially pertinent in the domain of sequential behavior where behavioral research suggests a diversity of cognitive processes, processing modes and representations. Inspired by decades of reaction time (RT) research with the Additive Factors Method, the Psychological Refractory Period paradigm, and the Discrete Sequence Production task, we propose the Cognitive framework for Sequential Motor Behavior (C-SMB). We argue that C-SMB accounts for cognitive models developed for a range of sequential motor tasks (like those proposed by Keele et al., Psychological Review, 110(2), 316-339, 2003; Rosenbaum et al., Journal of Experimental Psychology: Human Perception and Performance, 9(1), 86-102, 1983, Journal of Memory and Language, 25, 710-725, 1986, Psychological Review, 102, 28-67, 1995 Schmidt, Psychological Review, 82(4), 225-260, 1975;Sternberg et al., 1978, Phonetica, 45, 177-197, 1988. C-SMB postulates that sequence execution can be controlled by a central processor using central-symbolic representations, and also by a motor processor using sequencespecific motor representations. On the basis of this framework, we present a classification of the sequence execution strategies that helps researchers to better understand the cognitive and neural underpinnings of serial movement behavior.

show abstract

Section: External Sequence Control: Speeding Up Reactionsmentioning

confidence: 99%

A cognitive framework for explaining serial processing and sequence execution strategies

2014

View full text Add to dashboard Cite

show abstract

“…It has been shown that geometrically defined movement features can be acquired by the human motor system (Sosnik et al, 2004), before the specification of kinematic attributes such as movement speed Zipser, 2002, 2004). It is thus conceivable that separate planning constraints may be imposed at the geometric and temporal levels.…”

Section: Introductionmentioning

confidence: 99%

A Computational Model for Redundant Human Three-Dimensional Pointing Movements: Integration of Independent Spatial and Temporal Motor Plans Simplifies Movement Dynamics

Biess¹,

Liebermann²,

Flash³

2007

J. Neurosci.

126

View full text Add to dashboard Cite

Few computational models have addressed the spatiotemporal features of unconstrained three-dimensional (3D) arm motion. Empirical observations made on hand paths, speed profiles, and arm postures during point-to-point movements led to the assumption that hand path and arm posture are independent of movement speed, suggesting that the geometric and temporal properties of movements are decoupled. In this study, we present a computational model of 3D movements for an arm with four degrees of freedom based on the assumption that optimization principles are separately applied at the geometric and temporal levels of control. Geometric properties (path and posture) are defined in terms of geodesic paths with respect to the kinetic energy metric in the Riemannian configuration space. Accordingly, a geodesic path can be generated with less muscular effort than on any other, nongeodesic path, because the sum of all configuration-speed-dependent torques vanishes. The temporal properties of the movement (speed) are determined in task space by minimizing the squared jerk along the selected end-effector path. The integration of both planning levels into a single spatiotemporal representation simplifies the control of arm dynamics along geodesic paths and results in movements with near minimal torque change and minimal peak value of kinetic energy. Thus, the application of Riemannian geometry allows for a reconciliation of computational models previously proposed for the description of arm movements. We suggest that geodesics are an emergent property of the motor system through the exploration of dynamical space. Our data validated the predictions for joint trajectories, hand paths, final postures, speed profiles, and driving torques.

show abstract

“…More specifically, based on several studies relating the neural basis of modularity to the development of skilled movements (15)(16)(17), we expected that functional brain networks derived from acquisition of a simple motor skill would display modular structure over the variety of temporal scales associated with learning (18). We also hypothesized that modular structure would change dynamically during learning (4,19), and that characteristics of such dynamics would be associated with learning success.…”

mentioning

confidence: 99%

Dynamic reconfiguration of human brain networks during learning

Bassett

Wymbs

Porter

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

1,531

1,765

View full text Add to dashboard Cite

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes-flexibility and selection-must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.complex network | time-dependent network | fMRI | motor learning | community structure T he brain is a complex system, composed of many interacting parts, which dynamically adapts to a continually changing environment over multiple temporal scales. Over relatively short temporal scales, rapid adaptation and continuous evolution of those interactions or connections form the neurophysiological basis for behavioral adaptation or learning. At small spatial scales, stable neurophysiological signatures of learning have been best demonstrated in animal systems at the level of individual synapses between neurons (1-3). At a larger spatial scale, it is also well-known that specific regional changes in brain activity and effective connectivity accompany many forms of learning in humans-including the acquisition of motor skills (4, 5).Learning-associated adaptability is thought to stem from the principle of cortical modularity (6). Modular, or nearly decomposable (7), structures are aggregates of small subsystems (modules) that can perform specific functions without perturbing the remainder of the system. Such structure provides a combination of compartmentalization and redundancy, which reduces the interdependence of components, enhances robustness, and facilitates behavioral adaptation (8, 9). Modular organization also confers evolvability on a system by reducing constraints on change (8,(10)(11)(12). Indeed, a putative relationship between modularity and adaptability in the context of human neuroscience has recently been posited (13,14). To date, however, the existence of modularity in large-scale cortical connectivity during learning has not been tested directly.Based on the aforementioned theoretical and empirical grounds, we hypothesized that the principle of modularity would characterize the fundamental organiz...

show abstract

When practice leads to co-articulation: the evolution of geometrically defined movement primitives

Cited by 149 publications

References 39 publications

A cognitive framework for explaining serial processing and sequence execution strategies

A cognitive framework for explaining serial processing and sequence execution strategies

A Computational Model for Redundant Human Three-Dimensional Pointing Movements: Integration of Independent Spatial and Temporal Motor Plans Simplifies Movement Dynamics

Dynamic reconfiguration of human brain networks during learning

Contact Info

Product

Resources

About