A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems: e1003150

Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies...

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Bibliographic Details
Published in:PLoS computational biology 2013-07, Vol.9 (7)
Main Authors: Wilson, Robert C, Nassar, Matthew R, Gold, Joshua I
Format: Article
Language:English
Subjects:
Approximation
Behavior
Brain
Computation
Deltas
Dynamics
Human performance
Learning
Learning curves
Mathematical models
Optimization
Studies
Tax rates
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Summary:Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.
ISSN:1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003150