Composition and Decomposition of Internal Models in Motor Learning under Altered Kinematic and Dynamic Environments

Flanagan, J. Randall; Nakano, Eri; Imamizu, Hiroshi; Osu, Rieko; Yoshioka, Toshinori; Kawato, Mitsuo

doi:10.1523/jneurosci.19-20-j0005.1999

Cited by 169 publications

(106 citation statements)

References 20 publications

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“…They showed that learning a visuomotor rotation is not affected by adaptation to an inertial load presented either simultaneously or 5 min later. Similarly, a recent study shows lack of anterograde interference between a visuomotor rotation and a viscous force field (Flanagan et al, 1999).In summary, these studies of motor interference have shown interference between two position-dependent visuomotor mappings and interference between two velocity-dependent force fields but no interference between a position-dependent visuomotor rotation and either an acceleration-dependent or velocitydependent force field. Based on these results, Krakauer et al (1999) concluded that internal models of kinematic and dynamic transformations are stored in distinct systems of working memory.…”

mentioning

confidence: 51%

Section: Abstract: Motor Learning; Internal Models; Arm Movement; VImentioning

confidence: 69%

“…Thus, our results suggest that separate motor working-memory resources may be used when learning transformations that depend on different kinematic parameters. Note that this hypothesis is not jeopardized by the results of Flanagan et al (1999), who found that there is no transfer of learning when subjects successively adapt to a position-dependent visuomotor rotation and a velocity-dependent rotary force field. Although this lack of transfer supports the idea that kinematic and dynamic learning are independent, the transformations involved depended on different kinematic parameters.…”

Section: Discussionmentioning

confidence: 95%

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Kinematics and Dynamics Are Not Represented Independently in Motor Working Memory: Evidence from an Interference Study

2002

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Our capacity to learn multiple dynamic and visuomotor tasks is limited by the time between the presentations of the tasks. When subjects are required to adapt to equal and opposite position-dependent visuomotor rotations (Krakauer et al., 1999) or velocity-dependent force fields (Brashers-Krug et al., 1996) in quick succession, interference occurs that prevents the first task from being consolidated in memory. In contrast, such interference is not observed between learning a positiondependent visuomotor rotation and an acceleration-dependent force field. On the basis of this finding, it has been argued that internal models of kinematic and dynamic sensorimotor transformations are learned independently (Krakauer et al., 1999). However, these findings are also consistent with the perturbations interfering only if they depend on the same kinematic variable. We evaluated this hypothesis using kinematic and dynamic transformations matched in terms of the kinematic variable on which they depend. Subjects adapted to a positiondependent visuomotor rotation followed 5 min later by a position-dependent rotary force field either with or without visual feedback of arm position. The force field tended to rotate the hand in the direction opposite to the visuomotor rotation. To assess learning, all subjects were retested 24 hr later on the visuomotor rotation, and their performance was compared with a control group exposed only to the visuomotor rotation on both days. Adapting to the position-dependent force field, both with and without visual feedback, impaired learning of the visuomotor rotation. Thus, interference between our kinematic and dynamic transformations was observed, suggesting that the key determinant of interference is the kinematic variable on which the transformation depends. Key words: motor learning; internal models; arm movement; visuomotor rotation; force field; motor memoryThe problem of motor learning is one of mastering novel sensorimotor transformations that relate motor commands to sensory outcomes. Such learning involves the acquisition of internal models that capture these sensorimotor transformations and enable the CNS to accurately estimate the motor commands required to achieve desired outcomes and to predict the consequences of actions (Johansson and Cole, 1992;Miall et al., 1993;Shadmehr and Mussa-Ivaldi, 1994;Wolpert et al., 1995;Conditt et al., 1997;Flanagan and Wing, 1997;Kawato, 1999;Wolpert and Ghahramani, 2000). Two classes of sensorimotor transformations have been widely used in motor control research: kinematic and dynamic. Kinematic transformations are mappings between different geometric variables (and their derivatives) and, importantly, do not depend on the dynamic properties of the system. In contrast, dynamic transformations relate motor commands to the motion of the system; therefore, they do depend on dynamic properties such as inertia and viscosity. Thus, for example, to control a computer mouse, we must learn the kinematic transformation that relates mouse motion to cursor mot...

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mentioning

confidence: 51%

Section: Abstract: Motor Learning; Internal Models; Arm Movement; VImentioning

confidence: 69%

Section: Discussionmentioning

confidence: 95%

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Kinematics and Dynamics Are Not Represented Independently in Motor Working Memory: Evidence from an Interference Study

2002

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“…Finally, subjects have been studied performing reaching movements under novel environments while the kinematic and dynamic properties were altered [76,77]. The subjects were able to learn multiple internal kinematic and dynamic models that compensated for each transformation and, remarkably, were able to combine and decompose these models.…”

Section: Motor Learning and Adaptationmentioning

confidence: 99%

Computational approaches to motor control

Flash

Sejnowski²

2001

Current Opinion in Neurobiology

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New concepts and computational models that integrate behavioral and neurophysiological observations have addressed several of the most fundamental long-standing problems in motor control. These problems include the selection of particular trajectories among the large number of possibilities, the solution of inverse kinematics and dynamics problems, motor adaptation and the learning of sequential behaviors.

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“…Neural mechanisms that mimic the input-output properties of objects support prediction and control; these are termed internal models (Kawato et al, 1987;Wolpert et al, 1995). Considerable evidence from behavioral, neurophysiological, and functional imaging studies indicates that the CNS maintains a number of internal models for different objects and environments in a modular manner (Ghahramani and Wolpert, 1997;Flanagan et al, 1999;Krakauer et al, 1999;Gribble and Scott, 2002;Imamizu et al, 2003;Yamamoto et al, 2007). Behavioral studies have shown that humans can switch internal models based on contextual information.…”

Section: Introductionmentioning

confidence: 99%

Neural Correlates of Predictive and Postdictive Switching Mechanisms for Internal Models

Imamizu¹,

Kawato²

2008

J. Neurosci.

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Switching of sensorimotor tasks may be classified into predictive switching based on contextual information and postdictive switching based on the error between sensorimotor feedback and predictions. We used functional neuroimaging to study the brain regions involved in each type of switching of internal models for visuomotor rotations (clockwise and counterclockwise rotations of visual feedback). The color of a cue presented before movement initiation corresponded to direction of rotation of the feedback in an instructed condition, but not in a noninstructed condition. Switching-related activity was identified as activity that transiently increased after the direction of rotation was changed. The switching-related activity in cue periods in the instructed condition, when a predictive switch is possible, was observed in the superior parietal lobule (SPL). However, the switching-related activity in feedback periods in the noninstructed condition, when prediction error is crucial for the postdictive switch, was observed in the inferior parietal lobule (IPL) and prefrontal cortex. The functional influence of the SPL on the lateral cerebellum, namely, a possible neural correlate for internal models, increased in the instructed condition, but the influence of the IPL on the cerebellum was increased in the noninstructed condition. We observed a rapid activity increase in the instructed condition and a gradual activity increase in the noninstructed condition mainly in the lateral occipitotemporal cortices (LOTC) and supplementary motor cortex (SMA). These results are consistent with separate mechanisms for predictive and postdictive switches and suggest that the LOTC and SMA receive output signals from appropriate internal models.

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Composition and Decomposition of Internal Models in Motor Learning under Altered Kinematic and Dynamic Environments

Cited by 169 publications

References 20 publications

Kinematics and Dynamics Are Not Represented Independently in Motor Working Memory: Evidence from an Interference Study

Kinematics and Dynamics Are Not Represented Independently in Motor Working Memory: Evidence from an Interference Study

Computational approaches to motor control

Neural Correlates of Predictive and Postdictive Switching Mechanisms for Internal Models

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