Reward value and prediction error in human medial frontal cortex: a model-based fMRI study

Abstract

Event Abstract Back to Event Reward value and prediction error in human medial frontal cortex: a model-based fMRI study Massimo Silvetti1*, Ruth Seurinck1 and Tom Verguts1 1 Ghent University, Belgium The anterior cingulate cortex (ACC) has been shown to be critically involved in reinforcement learning, uncertainty computation, and volatility estimation, revealing a central role of this area in decision-making. Single cell recordings in the ACC revealed neural populations coding for reward expectations and the difference between such expectations and the real environmental outcomes (prediction error). In humans, influencing fMRI findings suggested that the activity of this region could mainly driven by volatility estimation. The connection between the microscopic level of neurophysiological data and the macroscopic level of human fMRI paradigms of reward-based decision-making remains still unclear. Here we propose a biologically plausible neural model of ACC, the Reward Value and Prediction Model (RVPM), based exclusively on data from single-cell recordings, and computing reward expectations and prediction errors. Computer simulations revealed that the prediction errors-related activity of our model could account for both effects of uncertainty and volatility observed in fMRI paradigms, and predicted that high uncertain environments should activate the ACC more than volatile environments. In a further step, we tested this prediction by a model-based fMRI study, in which we administered to a group of healthy volunteers a task with monetary rewards in environments with different levels of volatility and uncertainty. We then fitted the resulting BOLD signal with the signal obtained by the RVPM, and the results suggested that human ACC shows a profile of activation closely matching the one predicted by our model, indicating that this area responses mainly to uncertainty rather than to volatility. Keywords: decision-making, fMRI Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Poster Presentation Topic: Poster Sessions: Decision Making, Reward Processing & Response Selection Citation: Silvetti M, Seurinck R and Verguts T (2011). Reward value and prediction error in human medial frontal cortex: a model-based fMRI study. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00379 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Nov 2011; Published Online: 28 Nov 2011. * Correspondence: Dr. Massimo Silvetti, Ghent University, Ghent, Belgium, massimo.silvetti@istc.cnr.it Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Massimo Silvetti Ruth Seurinck Tom Verguts Google Massimo Silvetti Ruth Seurinck Tom Verguts Google Scholar Massimo Silvetti Ruth Seurinck Tom Verguts PubMed Massimo Silvetti Ruth Seurinck Tom Verguts Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.