Dissociating error-based and reinforcement-based loss functions during sensorimotor learning.

ABSTRACT: It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reac

SUBMITTER: Cashaback JGA 

PROVIDER: S-EPMC5550011 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature