Hampton Alan N, Bossaerts Peter, O'Doherty John P
Computation and Neural Systems Program, California Institute of Technology, Pasadena, California 91125, USA.
J Neurosci. 2006 Aug 9;26(32):8360-7. doi: 10.1523/JNEUROSCI.1010-06.2006.
Many real-life decision-making problems incorporate higher-order structure, involving interdependencies between different stimuli, actions, and subsequent rewards. It is not known whether brain regions implicated in decision making, such as the ventromedial prefrontal cortex (vmPFC), use a stored model of the task structure to guide choice (model-based decision making) or merely learn action or state values without assuming higher-order structure as in standard reinforcement learning. To discriminate between these possibilities, we scanned human subjects with functional magnetic resonance imaging while they performed a simple decision-making task with higher-order structure, probabilistic reversal learning. We found that neural activity in a key decision-making region, the vmPFC, was more consistent with a computational model that exploits higher-order structure than with simple reinforcement learning. These results suggest that brain regions, such as the vmPFC, use an abstract model of task structure to guide behavioral choice, computations that may underlie the human capacity for complex social interactions and abstract strategizing.
许多现实生活中的决策问题包含高阶结构，涉及不同刺激、行动及后续奖励之间的相互依存关系。目前尚不清楚参与决策的脑区，如腹内侧前额叶皮层（vmPFC），是使用存储的任务结构模型来指导选择（基于模型的决策），还是仅仅像标准强化学习那样在不假设高阶结构的情况下学习行动或状态值。为了区分这些可能性，我们在人类受试者执行具有高阶结构的简单决策任务——概率性反转学习时，用功能磁共振成像对他们进行扫描。我们发现，关键决策区域vmPFC中的神经活动，与利用高阶结构的计算模型比与简单强化学习更为一致。这些结果表明，诸如vmPFC这样的脑区使用任务结构的抽象模型来指导行为选择，这些计算可能是人类复杂社会互动和抽象策略制定能力的基础。
