Laboratory for Computational Motor Control
Identification of multiple timescales of adaptation
using Expectation Maximization (EM)
EM is a
statistical algorithm that can estimate model parameters for a data set
containing latent variables.
Here we provide
code that uses EM to fit two state models of learning to sensorimotor data. This
tool uncovers the hidden fast and slow states of learning from observed
behavior.
We provide two packages,
one that can be used to fit sensorimotor data with set breaks (Version 2.1),
and one which assumes no set breaks occurred (Version 1.1).
To get started
with the package, download the zip file and refer to the README file.