Learning under uncertainty: a comparison between R-W and Bayesian approach

NeurIPS 2016 He HuangMartin Paulus

Accurately differentiating between what are truly unpredictably random and systematic changes that occur at random can have profound effect on affect and cognition. To examine the underlying computational principles that guide different learning behavior in an uncertain environment, we compared an R-W model and a Bayesian approach in a visual search task with different volatility levels... (read more)

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