There and back again: putting the vectorial movement planning hypothesis to a critical test.

Based on psychophysical evidence about how learning of visuomotor transformation generalizes, it has been suggested that movements are planned on the basis of movement direction and magnitude, i.e., the vector connecting movement origin and targets. This notion is also known under the term "vectorial planning hypothesis". Previous psychophysical studies, however, have included separate areas of the workspace for training movements and testing the learning. This study eliminates this confounding factor by investigating the transfer of learning from forward to backward movements in a center-out-and-back task, in which the workspace for both movements is completely identical. Visual feedback allowed for learning only during movements towards the target (forward movements) and not while moving back to the origin (backward movements). When subjects learned the visuomotor rotation in forward movements, initial directional errors in backward movements also decreased to some degree. This learning effect in backward movements occurred predominantly when backward movements featured the same movement directions as the ones trained in forward movements (i.e., when opposite targets were presented). This suggests that learning was transferred in a direction specific way, supporting the notion that movement direction is the most prominent parameter used for motor planning.

Visual feedback reduces bimanual coupling of movement amplitudes, but not of directions.

To what extent does visual feedback shape the coordination between our arms? As a first step towards answering this question, this study compares bimanual coupling in simultaneous bimanual reversal movements that control cursor movements on a vertical screen. While both cursors were visible in the control condition, visual feedback was prevented in the experimental condition by deleting one or both cursors from the screen. Absence of visual feedback for one or both arms significantly increased the reaction times of both arms and the movement amplitude of the occluded arm. Temporal coupling between the arms remained unchanged in all feedback conditions. The same was true for spatial coupling of movement directions. Amplitude coupling, however, was significantly affected by visual feedback. When no feedback for either arm was available, amplitude correlations were significantly higher than when feedback for one or both arms was present. This finding suggests that online visual feedback decreases bimanual amplitude coupling, presumably through independent movement corrections for the two arms. The difference between movement amplitudes and movement directions in their susceptibility to visual feedback supports the idea that they are subserved by different control mechanisms. Analysis of eye movements during task performance revealed no major differences between the different feedback conditions. The eye movements of all subjects followed a stereotypical pattern, with generally only one saccade after target onset, directed towards the average position of all possible targets, irrespective of feedback condition and target direction.

Control is good; prediction is better?

Franz Mechsner (2004) suggests that movements are exclusively controlled with respect to the effects that they cause in the external world and that motor control can be reduced to prediction of movement effects. Although predictive mechanisms certainly deserve a lot more attention than they have received in the past, the author argues here that prediction and control must necessarily work together to build a flexible and effective motor system.

Visuomotor transformations affect bimanual coupling.

Interactions between bimanual movements may occur at two different levels: at a visually based level, where movement trajectories are programmed within the visually perceived external space, and at the executional level, through crosstalk of sensorimotor signals arising during movement execution. In order to distinguish between these sources of interactions, we investigated bimanual reversal movements under different conditions of visual feedback. A visuomotor transformation dissociated movement execution from visual appearance on a computer screen. The transformation we used made movements of the same amplitude evoke different excursions, and made movements of different amplitudes entail matched excursions on the screen. The transformed conditions allowed us to study which parameters of bimanual coupling were related to the way movements were executed and which correlated with the visual movement display. We found a clear dissociation between execution-related and visually related bimanual interactions. The assimilation of movement amplitudes was completely execution-related. Whenever movements of different amplitudes were generated, the shorter movement was lengthened, irrespective of how the movements appeared on the feedback screen. In contrast, temporal coordination at the point of movement reversal, as well as trial-by-trial correlations of movement amplitudes, also showed significant effects of the visuomotor transformation, suggesting that these parameters are influenced by visually perceived effects of movements. This dissociation confirms the idea of separate pathways for bimanual interactions and shows that a specific set of bimanual interactions occur at least partly within a visually based external reference frame.

The neuronal basis of bimanual coordination: recent neurophysiological evidence and functional models.

Recent physiological studies of the neuronal processes underlying bimanual movements provide new tests for earlier functional models of bimanual coordination. The recently acquired data address three conceptual areas: the generalized motor program (GMP), intermanual crosstalk and dynamic systems models. To varying degrees, each of these concepts has aspects that can be reconciled with experimental evidence. The idea of a GMP is supported by the demonstration of abstract neuronal motor codes, e.g. bimanual-specific activity in motor cortex. The crosstalk model is consistent with the facts that hand-specific coding also exists and that interactions occur between the motor commands for each arm. Uncrossed efferent projections may underlie crosstalk on an executional level. Dynamic interhemispheric interactions through the corpus callosum may provide a high-level link at the parametric programming level, allowing flexible coupling and de-coupling. Flexible neuronal interactions could also underlie adaptive large-scale systems dynamics that can be formalized within the dynamic systems theory approach. The correspondence of identified neuronal processes with functions of abstract models encourages the development of realistic computational models that can predict bimanual behavior on the basis of neuronal activity.