Work on multiple-system theories of cognition mostly focused on the systems themselves, while limited work has been devoted to understanding the interactions between systems. Generally, multiple-system theories include a model-based decision system supported by the prefrontal cortex and a model-free decision system supported by the striatum. Here we propose a neurobiological model to describe the interactions between model-based and model-free decision systems in category learning. The proposed model used spiking neurons to simulate activity of the hyperdirect pathway of the basal ganglia. The hyperdirect pathway acts as a gate for the response signal from the model-free system located in the striatum. We propose that the model-free system’s response is inhibited when the model-based system is in control of the response. The new model was used to simulate published data from young adults, people with Parkinson’s disease, and aged-matched older adults. The simulation results further suggest that system-switching ability may be related to individual differences in executive function. A new behavioral experiment tested this model prediction. The results show that an updating score predicts the ability to switch system in a categorization task. The article concludes with new model predictions and implications of the results for research on system interactions.

多系统认知理论的工作主要集中在系统本身，而有限的工作致力于理解系统之间的相互作用。通常，多系统理论包括由前额叶皮层支持的基于模型的决策系统和由纹状体支持的无模型决策系统。在这里，我们提出了一个神经生物学模型来描述类别学习中基于模型和无模型的决策系统之间的相互作用。所提出的模型使用尖峰神经元来模拟基底神经节超直接通路的活动。超直接通路充当来自位于纹状体中的无模型系统的响应信号的门。我们建议当基于模型的系统控制响应时，无模型系统的响应被抑制。新模型用于模拟来自年轻人、帕金森病患者和年龄匹配的老年人的已发表数据。模拟结果进一步表明系统切换能力可能与执行功能的个体差异有关。一个新的行为实验测试了这个模型预测。结果表明，更新分数可以预测在分类任务中切换系统的能力。本文以新模型预测和结果对系统交互研究的意义作为结尾。结果表明，更新分数可以预测在分类任务中切换系统的能力。本文以新模型预测和结果对系统交互研究的意义作为结尾。结果表明，更新分数可以预测在分类任务中切换系统的能力。本文以新模型预测和结果对系统交互研究的意义作为结尾。