000171791 001__ 171791
000171791 005__ 20181203022540.0
000171791 0247_ $$2doi$$a10.1371/journal.pcbi.1001048
000171791 02470 $$2ISI$$a000286652100007
000171791 037__ $$aARTICLE
000171791 245__ $$aRisk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings
000171791 269__ $$a2011
000171791 260__ $$c2011
000171791 336__ $$aJournal Articles
000171791 520__ $$aRecently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating.
000171791 6531_ $$aDecision-Making
000171791 6531_ $$aPrefrontal Cortex
000171791 6531_ $$aHumans
000171791 6531_ $$aReward
000171791 6531_ $$aActivation
000171791 6531_ $$aAmbiguity
000171791 6531_ $$aSystems
000171791 6531_ $$aExploitation
000171791 6531_ $$aInformation
000171791 6531_ $$aExploration
000171791 700__ $$uUniv New S Wales, Sydney, NSW, Australia$$aPayzan-LeNestour, Elise
000171791 700__ $$g181386$$uCALTECH, Pasadena, CA 91125 USA$$aBossaerts, Peter$$0241931
000171791 773__ $$j7$$tPlos Computational Biology$$q-
000171791 909C0 $$xU11813$$0252272$$pSFI-PB
000171791 909CO $$pCDM$$particle$$ooai:infoscience.tind.io:171791
000171791 937__ $$aEPFL-ARTICLE-171791
000171791 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000171791 980__ $$aARTICLE