Deciding when and how to correct a movement: discrete submovements as a decision making process.
Rapid reaching movements of human and non-human primates are often characterized by irregular multi-peaked velocity profiles. How to interpret these irregularities is still under debate. While some reports assert that these irregularities are the result of a continuous controller interacting with the environment, we and others hold that the velocity irregularities are evidence for a controller that produces discrete movement corrections. Here we analyze rapid pronation/supination wrist movements in monkey during a 1D step-tracking task, where visual perturbations of the target were randomly introduced at movement onset. We use our recently introduced algorithm (Fishbach et al. in Exp Brain Res 164:442-457, 2005) to decompose an irregular movement into a primary movement and one or more discrete, corrective submovements. We first show that the visual perturbation has almost no effect on primary movements. In contrast, this perturbation influences the type and the extent of the corrective submovements that often follow primary movements. Secondly, we show that the highly variable timing of overlapping submovements does not depend directly on the visual perturbation but rather on an estimate of the movement error and on the movement's extent-to-go at the time of correction initiation. These results are consistent with a forward-model based intermittent controller with a non-linearity that depends both on a prediction of the magnitude and direction of the movement's error and on its variance. Corrections are initiated only when the predicted error is statistically significant. A simple abstract model that implements these principles accounts for the type and timing of the corrections observed in our data.
Fishbach
Alon
A
Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA. fishbach@northwestern.edu
Roy
Stephane A
SA
Bastianen
Christina
C
Miller
Lee E
LE
Houk
James C
JC
D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D003657:Decision Making / Q000502:physiology*; D005260:Female; D008252:Macaca fascicularis; D008253:Macaca mulatta; D008297:Male; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D017711:Nonlinear Dynamics; D011597:Psychomotor Performance / Q000502:physiology