Biases and Variability from Costly Bayesian Inference Arthur Prat-Carrabin1,2, Florent Meyniel3, Misha Tsodyks4,5, and Rava Azeredo da Silveira2,4,6,7,? 1Department of Economics, Columbia University, New York, USA 2Laboratoire de Physique de l’École Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, France 3Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale, Commissariat à l’Energie Atomique et aux énergies alternatives, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France 4Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel 5The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, USA 6Institute of Molecular and Clinical Ophthalmology Basel 7Faculty of Science, University of Basel ?email:
[email protected] Abstract When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of proba- bility. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and a cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities on average and a limited memory of past observations, and, consequently, a fluctuating bias.