scholarly journals Space-alternating generalized expectation-maximization algorithm

1994 ◽  
Vol 42 (10) ◽  
pp. 2664-2677 ◽  
Author(s):  
J.A. Fessler ◽  
A.O. Hero
Keyword(s):  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Generalized Expectation

scholarly journals Estimating properties of the fast and slow adaptive processes during sensorimotor adaptation

2018 ◽  
Vol 119 (4) ◽  
pp. 1367-1393 ◽  
Author(s):  
Scott T. Albert ◽  
Reza Shadmehr
Keyword(s):  
Motor Learning ◽  
Expectation Maximization ◽  
State Space Model ◽  
Expectation Maximization Algorithm ◽  
Measured Data ◽  
Sensorimotor Learning ◽  
Adaptive Processes ◽  
Time Courses ◽  
Hidden States ◽  
Generalized Expectation

Experience of a prediction error recruits multiple motor learning processes, some that learn strongly from error but have weak retention and some that learn weakly from error but exhibit strong retention. These processes are not generally observable but are inferred from their collective influence on behavior. Is there a robust way to uncover the hidden processes? A standard approach is to consider a state space model where the hidden states change following experience of error and then fit the model to the measured data by minimizing the squared error between measurement and model prediction. We found that this least-squares algorithm (LMSE) often yielded unrealistic predictions about the hidden states, possibly because of its neglect of the stochastic nature of error-based learning. We found that behavioral data during adaptation was better explained by a system in which both error-based learning and movement production were stochastic processes. To uncover the hidden states of learning, we developed a generalized expectation maximization (EM) algorithm. In simulation, we found that although LMSE tracked the measured data marginally better than EM, EM was far more accurate in unmasking the time courses and properties of the hidden states of learning. In a power analysis designed to measure the effect of an intervention on sensorimotor learning, EM significantly reduced the number of subjects that were required for effective hypothesis testing. In summary, we developed a new approach for analysis of data in sensorimotor experiments. The new algorithm improved the ability to uncover the multiple processes that contribute to learning from error. NEW & NOTEWORTHY Motor learning is supported by multiple adaptive processes, each with distinct error sensitivity and forgetting rates. We developed a generalized expectation maximization algorithm that uncovers these hidden processes in the context of modern sensorimotor learning experiments that include error-clamp trials and set breaks. The resulting toolbox may improve the ability to identify the properties of these hidden processes and reduce the number of subjects needed to test the effectiveness of interventions on sensorimotor learning.

Ordered subset expectation maximization algorithm for image reconstruction applied to zero-flow or low-flow mimicking brain phantom

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S678-S678
Author(s):  
Yasuhiro Akazawa ◽  
Yasuhiro Katsura ◽  
Ryohei Matsuura ◽  
Piao Rishu ◽  
Ansar M D Ashik ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Low Flow ◽  
Brain Phantom ◽  
Zero Flow
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