Name
Title | ||
|---|---|---|
Neural substrates of updating the prediction through prediction error during decision making. |
Abstract | ||
|---|---|---|
Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e.g., in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neuroimage.2017.05.041 | NeuroImage |
Keywords | Field | DocType |
Decision making,Updating the prediction,Prediction error,fMRI,HD-tDCS | Developmental psychology,Mean squared prediction error,Functional magnetic resonance imaging,Cognitive psychology,Psychology,Computational model,Anterior cingulate cortex,Electrical brain stimulation,Posterior cingulate,Reinforcement learning,Iowa gambling task | Journal |
Volume | ISSN | Citations |
157 | 1053-8119 | 1 |
PageRank | References | Authors |
0.40 | 4 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Wang | 1 | 147 | 63.35 |
| Ning Ma | 2 | 1 | 0.40 |
| Xiaosong He | 3 | 1 | 0.40 |
| Nan Li | 4 | 1 | 0.40 |
| Zhengde Wei | 5 | 1 | 0.73 |
| Lizhuang Yang | 6 | 1 | 1.41 |
| Rujing Zha | 7 | 1 | 0.73 |
| Long Han | 8 | 1 | 0.40 |
| Xiaoming Li | 9 | 63 | 11.30 |
| Daren Zhang | 10 | 34 | 5.65 |
| Ying Liu | 11 | 12 | 1.84 |
| Xiaochu Zhang | 12 | 17 | 5.79 |