A machine learning-based model for the quantification of mental conflict

被引：0
|
作者：
Naoki, Honda [1 ]
Konaka, Yuki [1 ]
机构：
[1] Hiroshima Univ, Higashihiroshima, Hiroshima, Japan
来源：
NATURE COMPUTATIONAL SCIENCE | 2023年 / 3卷 / 5期
关键词：
D O I：
10.1038/s43588-023-00444-z
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
We often encounter mental conflict in our lives. Such mental conflict has long been regarded as subjective. However, a machine learning method can be used to quantify the temporal dynamics of conflict between reward and curiosity from behavioral time-series.
引用
收藏
页码：370 / 371
页数：2
相关论文
共 50 条
  • [1] A machine learning-based model for the quantification of mental conflict
    [J]. Nature Computational Science, 2023, 3 : 370 - 371
  • [2] Machine Learning-based SON function conflict resolution
    Rojas, Diego Fernando Preciado
    Mitschele-Thiel, Andreas
    [J]. 2019 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), 2019, : 964 - 969
  • [3] Machine Learning-Based EDFA Gain Model
    You, Yuren
    Jiang, Zhiping
    Janz, Christopher
    [J]. 2018 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATION (ECOC), 2018,
  • [4] Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification
    Zhang, Longbin
    Zhang, Xiaochen
    Zhu, Xueyu
    Wang, Ruoli
    Gutierrez-Farewik, Elena M.
    [J]. FRONTIERS IN NEUROSCIENCE, 2023, 17
  • [5] Machine learning-based approach for identifying mental workload of pilots
    Mohanavelu, K.
    Poonguzhali, S.
    Janani, A.
    Vinutha, S.
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 75
  • [6] Quantification of human sperm concentration using machine learning-based spectrophotometry
    Lesani, Ali
    Kazemnejad, Somaieh
    Zand, Mahdi Moghimi
    Azadi, Mojtaba
    Jafari, Hassan
    Mofrad, Mohammad R. K.
    Nosrati, Reza
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 127 (127)
  • [7] Machine Learning-Based Uncertainty Quantification of Passive Intermodulation in Aluminum Contact
    Treviso, Felipe
    Trinchero, Riccardo
    Canavero, Flavio G.
    [J]. 2022 3RD URSI ATLANTIC AND ASIA PACIFIC RADIO SCIENCE MEETING (AT-AP-RASC), 2022,
  • [8] Machine learning-based morphological quantification of replicative senescence in human fibroblasts
    Emma M. Welter
    Sofia Benavides
    Trevor K. Archer
    Oksana Kosyk
    Anthony S. Zannas
    [J]. GeroScience, 2024, 46 : 2425 - 2439
  • [9] Machine learning-based morphological quantification of replicative senescence in human fibroblasts
    Welter, Emma M.
    Benavides, Sofia
    Archer, Trevor K.
    Kosyk, Oksana
    Zannas, Anthony S.
    [J]. GEROSCIENCE, 2024, 46 (02) : 2425 - 2439
  • [10] Machine Learning-Based Recommendation Trust Model for Machine-to-Machine Communication
    Eziama, Elvin
    Jaimes, Luz M. S.
    James, Agajo
    Nwizege, Kenneth Sorle
    Balador, Ali
    Tepe, Kemal
    [J]. 2018 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT), 2018,
12345