Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference
نویسندگان
چکیده
منابع مشابه
Win-Stay, Lose-Sample: a simple sequential algorithm for approximating Bayesian inference.
People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian infere...
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People can apparently make surprisingly sophisticated inductive inferences, despite the fact that there are constraints on cognitive resources that would make performing exact Bayesian inference computationally intractable. What algorithms could they be using to make this possible? We show that a simple sequential algorithm, Win-Stay, Lose-Shift (WSLS), can be used to approximate Bayesian infer...
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Hungry rats were trained in a two-lever conditioning chamber to earn food reinforcement according to either a win-shift/lose-stay or a win-stay/lose-shift contingency. Performance on the two contingencies was similar when there was little delay between the initial, information part of the trial (i.e., win or lose) and the choice portion of the trial (i.e., stay or shift with respect to the leve...
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Traditional language learning theory explores an idealized interaction between a teacher and a learner. The teacher provides sentences from a language, while the learner has to infer the underlying grammar. Here, we study a new approach by considering a population of individuals that learn from each other. There is no designated teacher. We are inspired by the observation that children grow up ...
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ژورنال
عنوان ژورنال: Cognitive Psychology
سال: 2014
ISSN: 0010-0285
DOI: 10.1016/j.cogpsych.2014.06.003