title: Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality.
abstract: Making decisions in uncertain environments often requires combining multiple pieces of ambiguous information from external cues. In such conditions, human choices resemble optimal Bayesian inference, but typically show a large suboptimal variability whose origin remains poorly understood. In particular, this choice suboptimality might arise from imperfections in mental inference rather than in peripheral stages, such as sensory processing and response selection. Here, we dissociate these three sources of suboptimality in human choices based on combining multiple ambiguous cues. Using a novel quantitative approach for identifying the origin and structure of choice variability, we show that imperfections in inference alone cause a dominant fraction of suboptimal choices. Furthermore, two-thirds of this suboptimality appear to derive from the limited precision of neural computations implementing inference rather than from systematic deviations from Bayes-optimal inference. These findings set an upper bound on the accuracy and ultimate predictability of human choices in uncertain environments.
authors.affiliation: Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France; Département des Neurosciences Fondamentales, Université de Genève, CH-1211 Geneva, Switzerland; Department of Neurobiology, Harvard Medical School, Boston, MA 24615, USA. Electronic address: jan_drugowitsch@hms.harvard.edu.
authors.forename: Jan
authors.initials: J
authors.lastname: Drugowitsch
authors.affiliation: Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France. Electronic address: valentin.wyart@ens.fr.
authors.forename: Valentin
authors.initials: V
authors.lastname: Wyart
authors.affiliation: Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France.
authors.forename: Anne-Dominique
authors.initials: AD
authors.lastname: Devauchelle
authors.affiliation: Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France.
authors.forename: Etienne
authors.initials: E
authors.lastname: Koechlin
mesh_terms: D001499:Bayes Theorem; D002755:Choice Behavior; D003071:Cognition; D003463:Cues; D003657:Decision Making; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D015233:Models, Statistical; D011336:Probability; D055815:Young Adult
keywords: computational models; decision-making; probabilistic inference; variability