Probing the sources of suboptimality in human Bayesian inference

Luigi Acerbi, Daniel Wolpert, Sethu Vijayakumar

Research output: Contribution to conferencePosterpeer-review

Abstract / Description of output

When humans are presented with a simple Gaussian distribution of stimuli in an experimental setting, they are often able to combine this prior with noisy sensory information in agreement with the ‘optimal’ solution of Bayesian Decision Theory (BDT). However, in the presence of more complex experimental distributions (e.g. skewed or bimodal) performance appears suboptimal even after extensive training. Such suboptimality could arise from an inaccurate internal representation of the complex prior and/or from limitations in performing probabilistic inference on a veridical internal representation. We tested between these possibilities by developing a novel estimation task in which subjects were provided with explicit probabilistic information on each trial, thereby removing the need to learn the prior. The task consisted of 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 such as Gaussian, unimodal and bimodal. Subjects’ performance was in qualitative agreement with the predictions of BDT albeit generally suboptimal. However, the degree of suboptimality was largely independent of both the class of the prior and the level of noise in the cue, suggesting that learning or recalling the prior constitutes more of a challenge to decision making than manipulating the complex probabilistic information. We performed an extensive model comparison across a large set of suboptimal Bayesian observer models. Our analysis rejects many common models of variability in our task, 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
Original languageEnglish
Publication statusPublished - 2014
EventComputational and Systems Neuroscience (Cosyne) 2014 - Utah, Salt Lake City, United States
Duration: 27 Feb 20142 Mar 2014


ConferenceComputational and Systems Neuroscience (Cosyne) 2014
Country/TerritoryUnited States
CitySalt Lake City


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