Learning of Visuomotor Transformations for Vectorial Planning of Reaching Trajectories

By John W. Krakauer, Z. Pine, Maria Felice Ghilardi, C. Ghez. Journal of Neuroscience, 2000

896 citations recorded by the source corpus.

The planning of visually guided reaches is accomplished by independent specification of extent and direction. We investigated whether this separation of extent and direction planning for well practiced movements could be explained by differences in the adaptation to extent and directional errors during motor learning. We compared the time course and generalization of adaptation with two types of screen cursor transformation that altered the relationship between hand space and screen space. The first was a gain change that induced extent errors and required subjects to learn a new scaling factor. The second was a screen cursor rotation that induced directional errors and required subjects to learn new reference axes. Subjects learned a new scaling factor at the same rate when training with one or multiple target distances, whereas learning new reference axes took longer and was less complete when training with multiple compared with one target direction. After training to a single target, subjects were able to transfer learning of a new scaling factor to previously unvisited distances and directions. In contrast, generalization of rotation adaptation was incomplete; there was transfer across distances and arm configurations but not across directions. Learning a rotated reference frame only occurred after multiple target directions were sampled during training. These results suggest the separate processing of extent and directional errors by the brain and support the idea that reaching movements are planned as a hand-centered vector whose extent and direction are established via learning a scaling factor and reference axes.

Subfields: Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/5aa9e570c908c0d36d7b71a71c54201d0fb7ae26

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Citations in the corpus

Flexible Cognitive Strategies during Motor Learning (2011, background): “The benefits of an explicit strategy may be illusory with adaptive processes arising from automatic and incremental updating of a motor system that is impenetrable to conscious intervention [12–16].”
An Implicit Plan Overrides an Explicit Strategy during Visuomotor Adaptation (2006, background): “Adaptation to visuomotor rotation is a form of implicit motor learning: it proceeds incrementally (Krakauer et al., 2000), shows limited generalization (Krakauer et al.”
Bayesian integration in sensorimotor learning (2004, background): “However, most models of the sensorimotor system consider a cascade of mappings from sensory inputs to the motor outpu”
Evidence for a dynamic-dominance hypothesis of handedness (2001, background): “…adaptations to the rotated condition and have been demonstrated in a number of previous reports (Ghilardi et al. 1995; Imamizu et al. 1995; Ghahramani et al. 1996; Ghahramani and Wolpert 1997; Goodbody and Wolpert 1998; Ghilardi et al. 2000; Krakauer et al. 2000; Rosenbaum and Chaiken 2001).”
Autonomous Online Learning of Reaching Behavior in a humanoid Robot (2012, background): “This adaptation mechanism a®ects internal kinematic models of the body by exploiting the perceived errors in the movement(35); for example, they can learn how to control hand movements in a rotated reference frame.(36) In addition, neurophysiological evidence shows that a representation of what is r”
Principles of sensorimotor learning (2011, background): “Error-based learning is the driving force behind many well-studied adaptation paradigms, including prism adaptation 41 , saccade adaptation 42 , reaching in force fields 43,44 , visuomotor adaptation 45 and grip force adaptation 46 .”
Sensory prediction errors drive cerebellum-dependent adaptation of reaching. (2007, background): “Adaptation has been demonstrated across many different tasks (Krakauer et al. 2000; Martin et al. 1996; Morton and Bastian 2004; Reisman et al. 2005; Shadmehr and Mussa-Ivaldi 1994) and the cerebellum appears to be necessary for this form of learning (Chen et al.”
A computational neuroanatomy for motor control (2008, background): “It is possible that adaptation to a kinematic perturbation is driven primarily by visual errors (Krakauer et al. 2000) and adaptation to changes in inertial dynamics is driven primarily by proprioceptive errors (Hwang and Shadmehr 2005).”
Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns. (2011, positive): “Although previous studies have applied exponential models (Krakauer et al. 2000; Ghilardi et al. 2000; Galea and Miall 2006), here we found that individual data varied between a single and double exponential fit and so neither could be reliably fit to all individual subject data.”
Motor Learning and Consolidation: The Case of Visuomotor Rotation (2009, background): “Visuomotor rotation is a screen-cursor transformation that introduces a systematic directional bias around the hand and thus can be used to probe the adaptive processes underlying planning of reaching direction (Fig.1, see caption for details) (Krakauer et al. 2000).”
Eye–Hand Coordination during Learning of a Novel Visuomotor Task (2005, background): “Most studies of learning of visually guided manual skills have examined goal-directed reaching under novel load or visuomotor conditions (Newell and Rosenbloom, 1981; Cunningham, 1989; Lackner and Dizio, 1994; Shadmehr and Mussa-Ivaldi, 1994; Krakauer et al., 1999, 2000; Flanagan et al., 2003).”
Explicit and Implicit Processes Constitute the Fast and Slow Processes of Sensorimotor Learning (2015, background): “…one-target conditions is not just a reflection of the different components of implicit learning: adaptation in general shows narrow generalization (Krakauer et al., 2000; Taylor and Ivry, 2013) and participants in the Full condition had a fewer number of reaches toward each general reach…”