Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling

A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour.认知神经科学中一项被广泛认可的理论构想提出，多种大脑系统共同参与决策行为。其中包含两类系统：(1) 无模型（model-free）系统，该系统利用从备选行动的结果历史中缓存得到的价值；(2) 基于模型（model-based）系统，该系统会考量行动结果与环境的转换结构。由于这一区分在诸多应用场景中得到广泛使用，因此以高信度量化二者各自的影响显得尤为关键。本文聚焦于被广泛视作衡量人类决策中基于模型与无模型系统贡献的金标准范式——双阶段任务（two-stage task）。我们对该任务所得到的测量指标的内部/时间稳定性展开了测试，其中既涵盖通过成熟计算模型估计得到的指标，也包括使用漂移扩散（drift-diffusion）模型扩展得到的指标。漂移扩散建模结果显示，第一阶段的选择行为与第二阶段的反应时（RTs）均直接受到基于模型/无模型权衡参数的影响。无论是参数恢复效果，还是基于模型估计值的稳定性，初始表现均欠佳，但当同时纳入选择行为与反应时数据（相较于仅使用选择数据），且在分析中使用比常规研究实践更多的试次时，这两项指标均得到了显著改善。本研究结果对于解读基于双阶段任务的过往与未来研究，以及刻画基于模型加工过程对决策行为的贡献，均具有重要启示意义。