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2020, Nature Human Behaviour. DOI: 10.1038/s41562-019-0804-2
Quantum reinforcement learning during human decision-making
Ji-An Li, Daoyi Dong, Zhengde Wei, Ying Liu, Yu Pan, Franco Nori, Xiaochu Zhang
Abstract:
Classical reinforcement learning (CRL) has been widely applied in neuroscience and psychology; however, quantum reinforcement learning (QRL), which shows superior performance in computer simulations, has never been empirically tested on human decision-making. Moreover, all current successful quantum models for human cognition lack connections to neuroscience. Here we studied whether QRL can properly explain value-based decision-making. We compared 2 QRL and 12 CRL models by using behavioural and functional magnetic resonance imaging data from healthy and cigarette-smoking subjects performing the Iowa Gambling Task. In all groups, the QRL models performed well when compared with the best CRL models and further revealed the representation of quantum-like internal-state-related variables in the medial frontal gyrus in both healthy subjects and smokers, suggesting that value-based decision-making can be illustrated by QRL at both the behavioural and neural levels.
2023-12-01 00:00:00
#paper, https://doi.org/10.1038/s41562-019-0804-2,Quantum reinforcement learning during human decision-making,这篇nature子刊很有意思,探讨了量子强化学习(QRL)在人类决策中的应用。QRL在人类决策中的新颖应用:该研究是首次将QRL应用于人类决策的实证研究。QRL在计算机模拟中表现出色,但此研究首次在人类决策环境中对其进行了特殊测试。研究利用了参与者在执行爱荷华赌博任务时的行为数据和功能性磁共振成像(fMRI)数据,将2个QRL模型与12个已建立的CRL模型进行了对比。研究者开发了两种新的QRL模型:量子叠加状态学习(QSL)和量子叠加状态加持续性(QSPP)。这些模型的表现在某些方面优于最好的CRL模型。这一发现在包括健康个体和吸烟者在内的不同受试者群体中得到了确认,表明这些模型的稳健性和普遍适用性。量子类过程的神经表征:该研究的一个重要创新是确定了表示量子类过程的神经基质。例如,QSPP模型显示了在大脑中如何表征量子距离和转换幅度——QRL的关键概念。这一发现弥合了认知量子模型和神经科学之间的差距,为决策中的量子类过程提供了神经生物学基础。对于决策中的不确定性理解,论文还探讨了决策中不确定性的角色。通过将QSPP模型与CRL模型(VPPDecayTIC)进行比较,突出了大脑如何不同地处理由不稳定的外部环境影响的内部状态不确定性。这一研究方面强调了QRL模型在提供对决策认知过程更细微洞察方面的潜力。​​
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