Authors
Adnane Ez-Zizi, Simon Farrell, David Leslie, Gaurav Malhotra, Casimir JH Ludwig
Publication date
2023/12
Journal
Computational Brain & Behavior
Volume
6
Issue
4
Pages
626-650
Publisher
Springer International Publishing
Description
Two prominent types of uncertainty that have been studied extensively are expected and unexpected uncertainty. Studies suggest that humans are capable of learning from reward under both expected and unexpected uncertainty when the source of variability is the reward. How do people learn when the source of uncertainty is the environment’s state and the rewards themselves are deterministic? How does their learning compare with the case of reward uncertainty? The present study addressed these questions using behavioural experimentation and computational modelling. Experiment 1 showed that human subjects were generally able to use reward feedback to successfully learn the task rules under state uncertainty, and were able to detect a non-signalled reversal of stimulus-response contingencies. Experiment 2, which combined all four types of uncertainties—expected versus unexpected uncertainty …
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