Tracking motivational biases and their suppression in time and space
Publication year
2019Publisher
S.l. : s.n.
In
2019 Conference on Cognitive Computational Neuroscience, pp. 118-121Annotation
2019 Conference on Cognitive Computational Neuroscience (13-16 September 2019, Berlin, Germany)
Publication type
Article in monograph or in proceedings
Display more detailsDisplay less details
Organization
SW OZ DCC SMN
PI Group Motivational & Cognitive Control
PI Group Predictive Brain
Languages used
English (eng)
Book title
2019 Conference on Cognitive Computational Neuroscience
Page start
p. 118
Page end
p. 121
Subject
170 000 Motivational & Cognitive Control; 180 000 Predictive Brain; Action, intention, and motor controlAbstract
Action selection is not only based on acquired knowledge about action-outcome contingencies, but also by evolutionary "priors" such as motivational biases: Organisms show a tendency to invigorate responding when hoping for rewards, and to hold back when attempting to avoid punishments. While these biases are likely adaptive in many situations, they need to be inhibited when maladaptive. We probed the neural basis of overcoming these biases by measuring simultaneous EEG and fMRI. Successful detection and suppression of biases was associated with an increased synchronization in the alpha band 175-325 ms post-stimulus, which on a trial-by-trial basis was negatively correlated with BOLD signal in left MFG and right SMG. At a later time window around responses, there was a much stronger synchronization for executed vs. withheld actions in lower frequencies (peak in theta band), which was positively correlated on a trial-by-trial basis with BOLD signal in ACC/ SMA as well as bilateral motor cortex and operculum. Our work spatially locates oscillatory signatures of action selection and motivational conflict resolution.
This item appears in the following Collection(s)
- Academic publications [246326]
- Donders Centre for Cognitive Neuroimaging [4040]
- Electronic publications [133968]
- Faculty of Social Sciences [30461]
- Open Access publications [107450]
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