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Proactive recruitment of frontoparietal and salience networks for voluntary decisions.

Rens, N., Bode, S., Burianová, H. and Cunnington, R., 2017. Proactive recruitment of frontoparietal and salience networks for voluntary decisions. Frontiers in Human Neuroscience, 11.

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RensEtAl_2017.pdf - Published Version
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DOI: 10.3389/fnhum.2017.00610


There is evidence that neural patterns are predictive of voluntary decisions, but findings come from paradigms that have typically required participants to make arbitrary choices decisions in highly abstract experimental tasks. It remains to be seen whether proactive neural activity reflects upcoming choices for individuals performing decisions in more complex, dynamic, scenarios. In this functional Magnetic Resonance Imaging (fMRI) study, we investigated proactive neural activity for voluntary decisions compared with instructed decisions in a virtual environment, which more closely mimicked a real-world decision. Using partial least squares (PLS) analysis, we found that the frontoparietal and salience networks were associated with voluntary choice selection from a time at which decisions were abstract and preceded external stimuli. Using multi-voxel pattern analysis (MVPA), we showed that participants’ choices, which were decodable from motor and visual cortices, could be predicted with lower accuracy for voluntary decisions than for instructed decisions. This corresponded to eye-tracking data showing that participants made a greater number of fixations to alternative options during voluntary choices, which might have resulted in less stable choice representations. These findings suggest that voluntary decisions engage proactive choice selection, and that upcoming choices are encoded in neural representations even while individuals continue to consider their options in the environment.

Item Type:Article
Uncontrolled Keywords:free choice; self-initiated decision; action selection; partial least squares; multi-voxel pattern analysis; eye-tracking
Group:Faculty of Science & Technology
ID Code:34323
Deposited By: Symplectic RT2
Deposited On:23 Jul 2020 15:44
Last Modified:14 Mar 2022 14:23


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