Relativized hierarchical decomposition of Markov decision processes

Ravindran, Balaraman

doi:10.1016/b978-0-444-62604-2.00023-x

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…In the instructive feedback condition, on the other hand, participants might have felt less motivation to use a computationally demanding Bayesian updating strategy (or fewer participants might have consistently done so), because they could only rely on intrinsic reward to execute the task correctly, leading to relatively weaker performance. This notion is specifically in line with reinforcement learning theory where individuals, as biological agents, respond to environmental stimuli in ways that will result in the maximization of reward and minimization of loss (O'Hara, Hall, van Rijsbergen, & Shadbolt, 2006;Ravindran, 2013). However, we note that there are a number of different ways in which participants' behavior may have deviated from Bayes optimality, and the results of this study do not serve to fully disambiguate between these.…”

Section: T a B L E 2 Regression Analyses Of Frn Component In Monetarymentioning

confidence: 79%

Monetary feedback modulates performance and electrophysiological indices of belief updating in reward learning

et al. 2019

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Belief updating entails the incorporation of new information about the environment into internal models of the world. Bayesian inference is the statistically optimal strategy for performing belief updating in the presence of uncertainty. An important open question is whether the use of cognitive strategies that implement Bayesian inference is dependent upon motivational state and, if so, how this is reflected in electrophysiological signatures of belief updating in the brain. Here, we recorded the EEG of participants performing a simple reward learning task with both monetary and nonmonetary instructive feedback conditions. Our aim was to distinguish the influence of the rewarding properties of feedback on belief updating from the information content of the feedback itself. A Bayesian updating model allowed us to quantify different aspects of belief updating across trials, including the size of belief updates and the uncertainty of beliefs. Faster learning rates were observed in the monetary feedback condition compared to the instructive feedback condition, while belief updates were generally larger, and belief uncertainty smaller, with monetary compared to instructive feedback. Larger amplitudes in the monetary feedback condition were found for three ERP components: the P3a, the feedback‐related negativity, and the late positive potential. These findings suggest that motivational state influences inference strategies in reward learning, and this is reflected in the electrophysiological correlates of belief updating.

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