Bayesian Inference Causes Incoherence in Human Probability Judgments

AbstractHuman probability judgements appear systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. The Bayesian sampling viewpoint provides a simple rational model of probability judgements, which generates “biases” such as conservatism. The Bayesian sampler provides a single framework for explaining phenomena associated with diverse biases and heuristics, including availability and representativeness. The approach turns out to provide a rational reinterpretation of “noise” in an important recent model of probability judgement, the probability theory plus noise model (Costello & Watts, 2014; 2016; 2017; Costello, Watts, & Fisher, 2018), and captures the empirical data supporting this model.


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