当前位置: X-MOL 学术Nat. Hum. Behav. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Quantum reinforcement learning during human decision-making
Nature Human Behaviour ( IF 29.9 ) Pub Date : 2020-01-20 , DOI: 10.1038/s41562-019-0804-2
Ji-An Li 1, 2 , Daoyi Dong 3 , Zhengde Wei 1, 4 , Ying Liu 5 , Yu Pan 6 , Franco Nori 7, 8 , Xiaochu Zhang 1, 9, 10, 11
Affiliation  

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.



中文翻译:

人类决策过程中的量子强化学习

经典强化学习(CRL)已广泛应用于神经科学和心理学;然而,在计算机模拟中表现出卓越性能的量子强化学习 (QRL) 从未在人类决策中进行过经验测试。此外,目前所有成功的人类认知量子模型都缺乏与神经科学的联系。在这里,我们研究了 QRL 是否可以正确解释基于价值的决策。我们通过使用来自执行爱荷华州赌博任务的健康和吸烟受试者的行为和功能磁共振成像数据来比较 2 个 QRL 和 12 个 CRL 模型。在所有组中,

更新日期:2020-01-20
down
wechat
bug