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Systems 2015, 3(4), 211-236;

Approaches to Learning to Control Dynamic Uncertainty

Biological and Experimental Psychology Centre, School of Biological and Chemical Sciences, Queen Mary, University of London, London E1 4NS, UK
Department of Computer Science, University College of London, London WC1E 6BT, UK
Author to whom correspondence should be addressed.
Academic Editors: Andreas Größler and Hendrik Stouten
Received: 1 July 2015 / Accepted: 24 September 2015 / Published: 10 October 2015
(This article belongs to the Special Issue Dynamic Decision Making in Controlled Experiments)
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In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains) or exploit (maximizing their short term gains)? More to the point, how does this choice of learning strategy influence one’s later ability to control the environment? In the present study, we explore whether people’s self-reported learning strategies and levels of arousal (i.e., surprise, stress) correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people’s ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty. View Full-Text
Keywords: dynamic; decision making; exploration; computational modeling dynamic; decision making; exploration; computational modeling

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Osman, M.; Glass, B.D.; Hola, Z. Approaches to Learning to Control Dynamic Uncertainty. Systems 2015, 3, 211-236.

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