Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain: a combined fMRI–EEG study
While there is a growing body of functional magnetic resonance imaging (fMRI) evidence implicating a corpus of brain regions in value-based decision-making in humans, the limited temporal resolution of fMRI cannot address the relative temporal precedence of different brain regions in decision-making. To address this question, we adopted a computational model-based approach to electroencephalography (EEG) data acquired during a simple binary choice task. fMRI data were also acquired from the same participants for source localization. Post-decision value signals emerged 200 ms post-stimulus in a predominantly posterior source in the vicinity of the intraparietal sulcus and posterior temporal lobe cortex, alongside a weaker anterior locus. The signal then shifted to a predominantly anterior locus 850 ms following the trial onset, localized to the ventromedial prefrontal cortex and lateral prefrontal cortex. Comparison signals between unchosen and chosen options emerged late in the trial at 1050 ms in dorsomedial prefrontal cortex, suggesting that such comparison signals may not be directly associated with the decision itself but rather may play a role in post-decision action selection. Taken together, these results provide us new insights into the temporal dynamics of decision-making in the brain, suggesting that for a simple binary choice task, decisions may be encoded predominantly in posterior areas such as intraparietal sulcus, before shifting anteriorly.
Additional Information© 2014 The Author(s) Published by the Royal Society. Published 29 September 2014. The authors thank Simon Dunne and Teresa Furey for their help running the experiment, and Dr Paul Dockree for his advice and suggestions. Funding statement. This work was supported by grants from Science Foundation Ireland and the Gordon and Betty Moore Foundation to J.O.D. One contribution of 18 to a Theme Issue 'The principles of goal-directed decision-making: from neural mechanisms to computation and robotics'.
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