Model-based influences on humans' choices and striatal prediction errors.

Model-based influences on humans' choices and striatal prediction errors.
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DOI:
10.1016/j.neuron.2011.02.027
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发表时间:
2011-03-24
期刊:
影响因子:
16.2
通讯作者:
Dolan RJ
Dolan RJ
中科院分区:
医学1区
文献类型:
--
作者:
Daw ND;Gershman SJ;Seymour B;Dayan P;Dolan RJ

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中纹状体多巴胺系统与无模型强化学习密切相关,腹侧纹状体的fMRI BOLD信号与无模型预测误差显著协变。然而,潜在学习和贬值的研究表明,行为也显示出基于模型的规划的特点,基于模型和无模型的价值,预测误差和偏好之间的相互作用是探索不足。我们设计了一个多步骤的决策任务,其中基于模型和无模型的影响人类的选择行为可以区分。通过显示选择反映了两种影响,我们可以测试腹侧纹状体BOLD信号的纯度作为无模型报告。与预期相反,该信号反映了无模型和基于模型的预测,其比例与最能解释选择行为的比例相匹配。这些结果挑战了一个单独的无模型学习者的概念,并提出了一个更集成的计算架构,高层次的人类决策。
The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.
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