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Multiple associative structures created by reinforcement and incidental statistical learning mechanisms

机译:通过强化和附带统计学习机制创建的多个关联结构

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摘要

Learning the structure of the world can be driven by reinforcement but also occurs incidentally through experience. Reinforcement learning theory has provided insight into how prediction errors drive updates in beliefs but less attention has been paid to the knowledge resulting from such learning. Here we contrast associative structures formed through reinforcement and experience of task statistics. BOLD neuroimaging in human volunteers demonstrates rigid representations of rewarded sequences in temporal pole and posterior orbito-frontal cortex, which are constructed backwards from reward. By contrast, medial prefrontal cortex and a hippocampal-amygdala border region carry reward-related knowledge but also flexible statistical knowledge of the currently relevant task model. Intriguingly, ventral striatum encodes prediction error responses but not the full RL- or statistically derived task knowledge. In summary, representations of task knowledge are derived via multiple learning processes operating at different time scales that are associated with partially overlapping and partially specialized anatomical regions.
机译:学习世界的结构可以通过强化来驱动,但也可以通过经验来偶然发生。强化学习理论提供了关于预测错误如何驱动信念更新的见解,但对这种学习所产生的知识的关注却较少。在这里,我们对比了通过任务统计的增强和经验形成的关联结构。人类志愿者中的粗体神经影像显示了颞极和后眶额皮质中奖励序列的刚性表示,这些序列是从奖励向后构造的。相比之下,内侧前额叶皮层和海马-杏仁核边界区域既包含奖励相关知识,又包含当前相关任务模型的灵活统计知识。有趣的是,腹侧纹状体对预测错误响应进行编码,但对完整的RL或统计派生的任务知识不进行编码。总之,任务知识的表示是通过在不同时标下运行的多个学习过程得出的,这些学习过程与部分重叠和部分专门的解剖区域相关联。

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