Poster No:
713
Submission Type:
Abstract Submission
Authors:
Madoka Matsumoto1, Ryuta Aoki1, Kazuki Iijima2, Hiroshige Takeichi3, Kenji Matsumoto2, Mitsunari Abe4, Takashi Hanakawa5
Institutions:
1Kyoto University, Sakyo-ku, Kyoto, 2Tamagawa University, Machida, Tokyo, 3RIKEN, Yokohama, Kanagawa, 4National Center of Neurology and Psychiatry, Kodaira, Tokyo, 5Kyoto University, Kyoto, Kyoto
First Author:
Co-Author(s):
Mitsunari Abe
National Center of Neurology and Psychiatry
Kodaira, Tokyo
Introduction:
People's sense of control in everyday life (e.g., the belief that one has free choice and control over one's life) is widely recognized as a key component of happiness and life satisfaction. Previous behavioral studies using reinforcement learning (RL) models have suggested that free choice amplifies positive reward prediction error (i.e., reward prediction error in response to positive feedback), even when the objective reward probabilities are identical (Cockburn et al., 2014). This phenomenon effectively assigns a "free-choice premium" (Niv et al., 2015) to options selected through free choice compared to forced choice. Several functional magnetic resonance imaging (fMRI) studies have demonstrated that reward-related brain regions are differentially activated, depending on whether participants had opportunities to choose from multiple options (Leotti et al., 2011). However, the detailed neural dynamics underlying the interplay between reward processing and choice opportunities remain unknown. To investigate this, we combined computational modeling of behavioral data and magnetoencephalography (MEG) recording.
Methods:
We recorded brain activity using MEG while participants (N = 34) performed a reward-based learning task in which choice contexts were alternated by allowing them to choose from multiple options or imposing a single option (i.e., free vs. forced choice). After preprocessing the MEG data, we first examined the effects of the choice contexts on the spatiotemporal patterns of neural activity using a hidden Markov model (HMM). This data-driven approach allowed us to probe fast whole-brain dynamics and inform whether certain brain states were preferentially associated with the free- vs. forced-choice condition. Second, we employed an RL model to estimate reward prediction errors based on participants' choice behavior. We then performed regression analyses with trial-by-trial MEG responses as the dependent variables. Using source-reconstructed MEG time series (via beamforming), we identified local brain activations modulated by reward prediction errors and the choice contexts (the free vs. forced-choice condition).
Results:
Using the HMM analysis, we identified two distinct types of whole-brain dynamics preferentially associated with the free- vs. forced-choice condition. One brain state was characterized by a sustained increase in task-evoked occupancy from action execution to outcome feedback. A transient increase in the timing of outcome feedback characterized the other brain state. Immediately after outcome feedback, the transition probability between the states from time t-1 to t was significantly higher for the transition from the former to the latter state in the free- vs. forced-choice condition (p < 0.001, permutation test).
At the timing of outcome feedback, the MEG responses in the inferior parietal lobule and hippocampus showed significant main effects of the choice contexts (p < 0.05, voxel-wise FWE corrected across the whole brain). In contrast, the MEG responses in the dorsal striatum showed a significant interaction effect between positive reward prediction error and the choice contexts (p < 0.05).
Conclusions:
We observed neural processes between action execution and outcome feedback and positive modulation in feedback processing depending on the choice contexts (i.e., free vs. forced choice). These results suggest that the brain retains information about choice (not only which option was selected, but also whether the choice was made by themselves) and modulates reward processing, contributing to representation of the perceived control in reward-based learning.
Higher Cognitive Functions:
Decision Making 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Cognition
Computational Neuroscience
MEG
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
MEG
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
FSL
Free Surfer
Other, Please list
-
OSL
Provide references using APA citation style.
Niv, Yael, Angela Langdon, and Angela Radulescu. (2015). A Free-Choice Premium in the Basal Ganglia. Trends in Cognitive Sciences, 19(1), 4–5.
Cockburn, Jeffrey, Anne G.E. Collins, and Michael J. Frank. (2014). A Reinforcement Learning Mechanism Responsible for the Valuation of Free Choice. Neuron, 83(3), 551–57.
Leotti, Lauren A., and Mauricio R. Delgado. (2011). The Inherent Reward of Choice. Psychological Science, 22(10), 1310–18.
No