Otto A Ross, Skatova Anya, Madlon-Kay Seth, Daw Nathaniel D
New York University.
J Cogn Neurosci. 2015 Feb;27(2):319-33. doi: 10.1162/jocn_a_00709.
Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information--in the service of overcoming habitual, stimulus-driven responses--in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior.
关于决策及其神经基础的描述长期以来一直假定，在选择行为的控制中存在着独立的、相互竞争的估值系统。最近的理论和实验工作表明，这种在行为和神经上可分离的习惯性和目标导向性（或更一般地说，自动和受控）选择系统之间的经典区分，可能源于强化学习（RL）的两种计算策略，即无模型RL和基于模型的RL。但是，在行为控制中一个系统可能比另一个系统占主导地位的认知或计算过程，仍是一个正在研究的问题。为了阐明这个问题，我们利用认知控制的理论框架，证明在既定的认知控制范式中，为克服习惯性的、刺激驱动的反应而利用与目标相关的情境信息的个体差异，能够预测在一个单独的顺序选择任务中的基于模型的行为。认知控制与基于模型的RL之间的行为对应有力地表明，可能有一组共同的过程支撑这两种行为。特别是，最初提出的作为受控行为基础的计算机制，可能适用于理解基于模型和无模型选择行为之间的相互作用。
