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Bayesian inference was used to model continuously updating prior and posterior beliefs. Bayesian surprise was quantified as the Kullback-Leibler divergence between the prior and posterior distributions. The weights of past stimulus transition observations were discounted using a thresholded sigmoid function ( = 10).

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