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Biological Learning and ControlHow the Brain Builds Representations, Predicts Events, and Makes Decisions$
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Reza Shadmehr and Sandro Mussa-Ivaldi

Print publication date: 2012

Print ISBN-13: 9780262016964

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262016964.001.0001

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Building Generative Models: Structural Learning and Identification of the Learner

Building Generative Models: Structural Learning and Identification of the Learner

Chapter:
(p.251) 9 Building Generative Models: Structural Learning and Identification of the Learner
Source:
Biological Learning and Control
Author(s):

Reza Shadmehr

Sandro Mussa-Ivaldi

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262016964.003.0010

This chapter presents a discussion on structural learning and identification of the structure of the learner. It reveals that the prior exposure to a rotation perturbation, despite being random and unlearnable, seemed to significantly enhance learning rates for a member of the same perturbation class. It shows that the problem of structural learning is that of describing a dynamical system that in principle can accurately predict the sensory consequences of motor commands, that is, learn the structure of a forward model. This chapter suggests that Expectation Maximization is an alternate approach to estimating the structure of a linear dynamical system.

Keywords:   structural learning, learning rates, rotation perturbation, linear dynamical system, motor commands, Expectation Maximization, sensory consequences

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