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- W1518418447 abstract "In the simple case of a Gaussian distribution of stimuli, humans are often able to combine noisy sensory information with the statistics of the experiment (priors) in agreement with the ‘optimal’ solution of Bayesian Decision Theory (BDT). However, in the presence of a more complex experimental distribution (e.g. skewed or bimodal), such that the normatively optimal behavior is nonlinear, performance may appear suboptimal even after a long training. It is unclear whether such observed suboptimality arises from a mismatched internal representation of the complex prior, or from limitations in performing probabilistic inference on a correct internal representation of the complex distributions. We tested between these possibilities with a novel estimation task in which subjects were provided explicit probabilistic information in each trial, removing thereby the need for remembering a prior. The task consisted in estimating the location of a hidden target given a noisy cue and a visual representation of the prior probability density over locations, which changed from trial to trial. Priors belonged to different classes of distributions (Gaussian, unimodal, bimodal). Subjects’ performance was in qualitative agreement with the predictions of BDT albeit generally suboptimal. However, the degree of suboptimality was mostly independent of the class of the prior and level of noise in the cue, suggesting that remembering the patterns of past events constitutes more of a challenge to decision making than manipulating the complex probabilistic information at hand. We performed an extensive model comparison across a large set of suboptimal Bayesian observer models. Our analysis rejects for our task many common models of variability, such as probability matching and a sample-averaging strategy. Instead we found that subjects’ suboptimality was driven both by a miscalibrated internal representation of the parameters of the likelihood, and by decision noise that can be interpreted as a noisy representation of the posterior." @default.
- W1518418447 created "2016-06-24" @default.
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- W1518418447 date "2014-01-01" @default.
- W1518418447 modified "2023-09-25" @default.
- W1518418447 title "Probing the sources of suboptimality in human Bayesian inference" @default.
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