A key feature of learning in humans is flexibility in adjusting the weight of new information to update predictions. This flexibility can be computationally captured by changing the learning rate in a reinforcement‐learning model. Key components of reinforcement learning—such as prediction error (δ), learning rate (α), and reward feedback (r)—have been mapped to various brain areas. However, questions regarding the functional integration patterns in the human brain under the modulation of learning factors, and their interactions, remain unanswered. To investigate these phenomena, we first applied a reinforcement‐learning model with an adaptive learning rate and functional magnetic resonance imaging to simulate the individual's reward‐learning behavior. Psychophysiological interaction (PPI) analysis was then used to examine the functional interactions of the whole brain under the experimental condition of reward (r), the integration between reward and learning rate (α × r), and the integration between the prediction error and learning rate (α × δ) in a reward‐learning task. The behavior statistical analyses indicated that the model estimates of α and δ captured the participants' learning behavioral patterns of getting high reward, by changing α and δ for different difficulties and after getting different reward feedback. The PPI analysis results showed that motor‐related regions (including the supplement motor area, precentral gyrus, and thalamus) contributed to cognitive control processing regions (including the middle temporal gyrus, anterior and middle cingulate gyrus, and inferior frontal gyrus) by α × r. Finally, α × δ modulated the interaction between subregions of the striatum.

中文翻译：

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自适应奖励学习过程中的功能性神经相互作用：一项功能性磁共振成像研究

人类学习的一个关键特征是灵活调整新信息的权重以更新预测。这种灵活性可以通过改变强化学习模型中的学习率来计算。强化学习的关键组成部分——例如预测误差 (δ)、学习率 (α) 和奖励反馈 (r)——已被映射到不同的大脑区域。然而，关于在学习因素调节下的人脑功能整合模式及其相互作用的问题仍未得到解答。为了研究这些现象，我们首先应用具有自适应学习率和功能磁共振成像的强化学习模型来模拟个人的奖励学习行为。然后通过心理生理交互（PPI）分析，考察在奖励（r）实验条件下全脑的功能交互、奖励与学习率的整合（α×r）、预测误差与学习的整合率 (α × δ) 在奖励学习任务中。行为统计分析表明，α和δ的模型估计通过针对不同的难度和在获得不同的奖励反馈后改变α和δ来捕捉参与者获得高奖励的学习行为模式。PPI分析结果显示，运动相关区（包括补充运动区、中央前回和丘脑）对认知控制处理区（包括颞中回、前扣带回、中扣带回、和额下回）由 α × r。最后，α × δ 调节了纹状体亚区域之间的相互作用。