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How much of reinforcement learning is working memory not reinforcement learning? A behavioral computational and neurogenetic analysis

机译：钢筋学习多少是工作记忆而不是加强学习？行为计算和神经肝分析

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Instrumental learning involves corticostriatal circuitry and the dopaminergic system. This system is typically modeled in the reinforcement learning (RL) framework by incrementally accumulating reward values of states and actions. However, human learning also implicates prefrontal cortical mechanisms involved in higher level cognitive functions. The interaction of these systems remains poorly understood, and models of human behavior often ignore working memory (WM) and therefore incorrectly assign behavioral variance to the RL system. Here we designed a task that highlights the profound entanglement of these two processes, even in simple learning problems. By systematically varying the size of the learning problem and delay between stimulus repetitions, we separately extracted WM-specific effects of load and delay on learning. We propose a new computational model that accounts for the dynamic integration of RL and WM processes observed in subjects' behavior. Incorporating capacity-limited WM into the model allowed us to capture behavioral variance that could not be captured in a pure RL framework even if we (implausibly) allowed separate RL systems for each set size. The WM component also allowed for a more reasonable estimation of a single RL process. Finally, we report effects of two genetic polymorphisms having relative specificity for prefrontal and basal ganglia functions. Whereas the COMT gene coding for catechol-O-methyl transferase selectively influenced model estimates of WM capacity, the GPR6 gene coding for G-protein-coupled receptor 6 influenced the RL learning rate. Thus, this study allowed us to specify distinct influences of the high-level and low-level cognitive functions on instrumental learning, beyond the possibilities offered by simple RL models.
机译：仪器学习涉及皮质棘轮电路和多巴胺能系统。该系统通常通过递增状态和动作的奖励值来建模在加强学习（RL）框架中。然而，人类学习也含有涉及更高级别的认知功能的前逆转皮层机制。这些系统的交互仍然理解，人类行为的模型通常忽略工作存储器（WM），因此不正确地将行为方差分配给RL系统。在这里，我们设计了一项任务，即使在简单的学习问题中，即使在简单的学习问题中，也突出了这两个过程的深刻纠缠。通过系统地改变学习问题的大小和刺激重复之间的延迟，我们分别提取了负荷和学习延迟的WM特定效果。我们提出了一种新的计算模型，该计算模型考虑了在受试者行为中观察到的RL和WM过程的动态集成。将容量限制为模型，允许我们捕获即使我们（易用）允许为每个集大小允许单独的RL系统，也捕获无法在纯RL框架中捕获的行为方差。 WM组件还允许更合理地估计单个RL过程。最后，我们报告了两个遗传多态性对前额外和基础神经节函数具有相对特异性的影响。虽然COMT基因编码用于儿茶酚-O-甲基转移酶的选择性地影响WM容量的模型估计，编码用于G蛋白偶联受体6的GPR6基因影响RL学习率。因此，本研究允许我们指定对仪器学习的高级和低级认知功能的不同影响，超出了简单RL模型所提供的可能性。

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