Technical Program

Paper Detail

Paper: PS-2A.59
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Human learning and decision-making in the bandit task: Three wrongs make a right
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Dalin Guo, Angela Yu, UC San Diego, United States
Abstract: Humans and animals frequently need to make choices among options with imperfectly known reward outcomes. In neuroscience, this is often studied using the multi-armed bandit task, in which subjects repeatedly choose among “bandit” arms with fixed but unknown reward rates, thus negotiating a tension between exploitation and exploration. Here, using a modified version of the bandit task in which we query subjects’ reward expectations of unchosen arms, we investigate how general reward availability in the environment affects human prior beliefs. Based on self-report data and computational modeling of behavioral data, we obtain converging evidence that human subjects systematically under-estimate reward availability. Additional computational analyses reveal that this under-estimation compensates for two other apparent suboptimalities in human behavior, namely a default assumption of environmental non-stationarity, and the use of a simplistic decision policy. This result represents a concrete instance in which multiple sub-optimalities in brain computations synergistically interact to achieve much better-than-expected behavioral outcome. This work raises the intriguing possibility that many apparently isolated limitations in brain computation and representation may actually work together to achieve highly “intelligent” behavior in a broader context, and also sheds light on computationally efficient algorithms that could be adopted by artificial intelligence systems.