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Quantum reinforcement learning during human decision-making

Nature Human Behaviour volume 4, pages 294–307 (2020)Cite this article

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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.

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Fig. 1: Task diagram and task performance.
Fig. 2: Diagrams of model architecture.
Fig. 3: The AICc and BIC of each model, computed separately for each group.
Fig. 4: The inferred model probability of each model, computed separately for each group.
Fig. 5: The simulation results of each model, computed separately for each group.
Fig. 6: Generalized quantum distance (computed by the QSPP model)-related activity in the control group.
Fig. 7: fMRI results of the uncertainty × penalty/reward interaction.

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Data availability

All data are available from the corresponding author on reasonable request.

Code availability

All code used to generate the results central to the main claims in this study is available from the corresponding author on reasonable request.

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Acknowledgements

We thank Y. Yang, R. Zha, J. Besumeyer and N. Ma for their inspirational comments. We thank L. Acerbi, G. R. Yang, C. Gneiting, A. Miranowicz, X. Li, Z. Jin and X. Li for their helpful suggestions. This work was supported by grants from the National Key Basic Research Programme (grant nos. 2016YFA0400900 and 2018YFC0831101), the National Natural Science Foundation of China (grant nos. 31471071, 31771221, 61773360, 71671115, 71874170 and 71942003), the Fundamental Research Funds for the Central Universities of China, the MURI Center for Dynamic Magneto-Optics via the Air Force Office of Scientific Research (AFOSR; grant no. FA9550-14-1-0040), the Army Research Office (ARO; grant no. W911NF-18-1-0358), the Asian Office of Aerospace Research and Development (AOARD; grant no. FA2386-18-1-4045), the Japan Science and Technology Agency (JST; via the Q-LEAP programme and CREST grant no. JPMJCR1676), the Japan Society for the Promotion of Science (JSPS; JSPS–RFBR grant no. 17-52-50023 and JSPS–FWO grant no. VS.059.18N), the RIKEN–AIST Challenge Research Fund, the Templeton Foundation, the Foundational Questions Institute (FQXi) and the NTT PHI Laboratory, the Australian Research Council’s Discovery Projects funding scheme under Project DP190101566, the Alexander von Humboldt Foundation and the US Office of Naval Research. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the Bioinformatics Centre of the University of Science and Technology of China, School of Life Science for providing supercomputing resources for this project.

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Authors and Affiliations

  1. 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, China

    Ji-An Li, Zhengde Wei & Xiaochu Zhang

  2. Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, China

    Ji-An Li

  3. School of Engineering and Information Technology, University of New South Wales, Canberra, Australian Capital Territory, Australia

    Daoyi Dong

  4. Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Zhengde Wei

  5. The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

    Ying Liu

  6. Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China

    Yu Pan

  7. Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Wakoshi, Japan

    Franco Nori

  8. Department of Physics, The University of Michigan, Ann Arbor, MI, USA

    Franco Nori

  9. Hefei Medical Research Centre on Alcohol Addiction, Anhui Mental Health Centre, Hefei, China

    Xiaochu Zhang

  10. Academy of Psychology and Behaviour, Tianjin Normal University, Tianjin, China

    Xiaochu Zhang

  11. Centres for Biomedical Engineering, University of Science and Technology of China, Hefei, China

    Xiaochu Zhang

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Contributions

L.J.-A., Y.P. and X.Z. conceived the study. Y.L. and Z.W. provided the devices and collected the data. L.J.-A. built the models. L.J.-A. and Z.W. analysed the data. All authors participated in discussions. L.J.-A., D.D., Y.P., F.N. and X.Z. wrote the paper. X.Z. supervised the project and acquired funding.

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Correspondence to Xiaochu Zhang.

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Li, JA., Dong, D., Wei, Z. et al. Quantum reinforcement learning during human decision-making. Nat Hum Behav 4, 294–307 (2020). https://doi.org/10.1038/s41562-019-0804-2

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