Quantum reinforcement learning during human decision-making
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...
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| Published in | Nature human behaviour Vol. 4; no. 3; pp. 294 - 307 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Nature Publishing Group UK
01.03.2020
Nature Publishing Group |
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| 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.
Li et al. show that human value-based decision-making can be modelled using the quantum reinforcement learning framework. These new models reveal the importance of the medial frontal cortex in this quantum-like decision-making process. |
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| AbstractList | 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.
Li et al. show that human value-based decision-making can be modelled using the quantum reinforcement learning framework. These new models reveal the importance of the medial frontal cortex in this quantum-like decision-making process. 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.Li et al. show that human value-based decision-making can be modelled using the quantum reinforcement learning framework. These new models reveal the importance of the medial frontal cortex in this quantum-like decision-making process. 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.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. 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. |
| Author | Liu, Ying Nori, Franco Li, Ji-An Pan, Yu Zhang, Xiaochu Dong, Daoyi Wei, Zhengde |
| Author_xml | – sequence: 1 givenname: Ji-An orcidid: 0000-0003-2419-2281 surname: Li fullname: Li, Ji-An organization: Eye Center, Dept. of Ophthalmology, the First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Department of Statistics and Finance, School of Management, University of Science and Technology of China – sequence: 2 givenname: Daoyi orcidid: 0000-0002-7425-3559 surname: Dong fullname: Dong, Daoyi organization: School of Engineering and Information Technology, University of New South Wales – sequence: 3 givenname: Zhengde surname: Wei fullname: Wei, Zhengde organization: Eye Center, Dept. of Ophthalmology, the First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine – sequence: 4 givenname: Ying surname: Liu fullname: Liu, Ying organization: The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China – sequence: 5 givenname: Yu surname: Pan fullname: Pan, Yu organization: Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University – sequence: 6 givenname: Franco orcidid: 0000-0003-3682-7432 surname: Nori fullname: Nori, Franco organization: Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Department of Physics, The University of Michigan – sequence: 7 givenname: Xiaochu orcidid: 0000-0002-7541-0130 surname: Zhang fullname: Zhang, Xiaochu email: zxcustc@ustc.edu.cn organization: Eye Center, Dept. of Ophthalmology, the First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei Medical Research Centre on Alcohol Addiction, Anhui Mental Health Centre, Academy of Psychology and Behaviour, Tianjin Normal University, Centres for Biomedical Engineering, University of Science and Technology of China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31959921$$D View this record in MEDLINE/PubMed |
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| Title | Quantum reinforcement learning during human decision-making |
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