Learning to Make Accurate Predictions
Learning to Make Accurate Predictions
This chapter considers some very simple learning problems to make accurate predictions. It reviews the least mean squared (LMS) algorithm. It shows that internal model is simply a link between motor commands and their sensory consequences. The driving force in learning an internal model is the sensory prediction error. This chapter also reveals that when motor commands are generated, perturbations like force fields or visuomotor rotations produce discrepancies between the predicted and observed sensory consequences. It illustrates that in some forms of biological learning, as in backward blocking, animals seem to learn in a way that resembles the Bayesian method and not LMS.
Keywords: least mean squared algorithm, internal model, biological learning, sensory prediction error, motor commands, sensory consequences, backward blocking, Bayesian method
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