A mixture of delta-rules approximation to bayesian inference in change-point problems.

ABSTRACT: 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 ta

SUBMITTER: Wilson RC 

PROVIDER: S-EPMC3723502 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature