迈向人机共生时代：人机协同决策的心理学视角

朱奥; 刘华硕; 袁佩君; 张丹

doi:10.3981/j.issn.1000-7857.2023.09.01354

科技导报 >

2025 , Vol. 43 >Issue 3: 37 - 46

DOI: https://doi.org/10.3981/j.issn.1000-7857.2023.09.01354

特色专题：混合智能中的情感交互与协同决策

迈向人机共生时代：人机协同决策的心理学视角

朱奥 ^,¹ ,
刘华硕 ¹ ,
袁佩君 ^,²^,* ,
张丹 ^,¹^,³^,*

展开

1. 清华大学心理与认知科学系, 北京 100084
2. 启元实验室, 北京 100095
3. 清华大学脑与智能实验室, 北京 100084

袁佩君（通信作者），助理研究员，研究方向为人机交互，电子信箱：yuanpeijun@qiyuanlab.com

张丹（共同通信作者），副教授，研究方向为情感计算、脑机接口，电子信箱：dzhang@tsinghua.edu.cn

朱奥，博士研究生，研究方向为人机协同决策，电子信箱：za20@mails.tsinghua.edu.cn

收稿日期: 2023-09-05

网络出版日期: 2025-03-07

基金资助

教育部高等学校心理学类专业教指委教改项目(20222008)

清华大学教学改革项目(DX05-02)

版权

收起

Towards the era of human-machine symbiosis: A psychological perspective on human-machine collaborative decision-making

Ao ZHU ^,¹ ,
Huashuo LIU ¹ ,
Peijun YUAN ^,²^,* ,
Dan ZHANG ^,¹^,³^,*

Expand

1. Department of Psychology and Cognitive Sciences, Tsinghua University, Beijing 100084, China
2. Qiyuan Lab, Beijing 100095, China
3. Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China

Received date: 2023-09-05

Online published: 2025-03-07

Copyright

Fold

摘要

在人工智能技术的驱动下，人机协同决策效能逐步提升，展现出巨大的潜力。然而，当前人机协同决策领域面临情感问题、信任问题、决策权力分配问题和道德对齐问题等挑战。其中，人机协同情感问题涉及机器如何克服人机异质性、准确识别人类的情感状态并做出适当响应；信任问题关系到人类对机器决策的依赖程度和信任水平，需要在过度信任和信任不足之间找到平衡；决策权力分配问题则聚焦于在人机协作中如何合理分配决策权，以防止机器权力过大或不足；道德对齐问题探讨如何确保机器决策符合人类的道德标准和价值观，保障决策的伦理性和社会接受度。提出了人机协同决策的未来发展方向，包括提升机器对人类情感的理解和响应能力、建立稳固的信任机制、优化决策权力的分配方式，以及确保机器决策符合伦理和道德标准。在迈向人机共生的道路上，不仅需要技术创新，还需要结合心理学视角来应对这些挑战，以实现更加高效、安全和可持续的人机协同决策。

关键词： 人机共生; 人机协作; 协同决策; 人机信任; 心理学视角

本文引用格式

朱奥 , 刘华硕 , 袁佩君 , 张丹 . 迈向人机共生时代：人机协同决策的心理学视角[J]. 科技导报, 2025 , 43(3) : 37 -46 . DOI: 10.3981/j.issn.1000-7857.2023.09.01354

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

1	黄海丰, 刘培森, 李擎, 等. 协作机器人智能控制与人机交互研究综述[J]. 工程科学学报, 2022, 44 (4): 780- 791.

2	Licklider J C R . Man-computer symbiosis[J]. IRE Transactions on Human Factors in Electronics, 1960 (1): 4- 11.

3	Silver D , Huang A , Maddison C J , et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529 (7587): 484- 489. DOI

4	Openai. Gpt-4 technical report[EB/OL]. [2023-08-05]. https://cdn.openai.com/papers/gpt-4.pdf.

5	Esteva A , Kuprel B , Novoa R A , et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542 (7639): 115- 118. DOI

6	何贵兵, 陈诚, 何泽桐, 等. 智能组织中的人机协同决策: 基于人机内部兼容性的研究探索[J]. 心理科学进展, 2022, 30 (12): 2619- 2627. DOI

7	Sheridan T B . Human-robot interaction: Status and challenges[J]. Human Factors, 2016, 58 (4): 525- 532. DOI

8	Huang M H , Rust R T . Artificial intelligence in service[J]. Journal of Service Research, 2018, 21 (2): 155- 172. DOI

9	孙效华, 张义文, 秦觉晓, 等. 人机智能协同研究综述[J]. 包装工程, 2020, 41 (18): 1- 11.

10	Raymond R C . Use of the time-sharing computer in business planning and budgeting[J]. Management Science, 1966, 12 (8): B-363- B-381. DOI

11	Turban E . The use of mathematical models in plant maintenance decision making[J]. Management Science, 1967, 13 (6): B-342- B-358. DOI

12	Power D J. Decision support systems: A historical overview [M]//Handbook on Decision Support Systems 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008: 121-140.

13	Davis G B . Management information systems: Conceptual foundations, structure, and development[M]. New York: McGraw-Hill, Inc., 1984.

14	Power D J . Decision support systems: Concepts and resources for managers[M]. Westport: Quorum Books, 2002.

15	Kunz J C , Shortliffe E H , Buchanan B G , et al. Computerassisted decision making in medicine[J]. The Journal of Medicine and Philosophy, 1984, 9 (2): 135- 160. DOI

16	Sprague R H . A framework for the development of decisoin support systems[J]. MIS Quarterly, 1980, 4 (4): 1- 26. DOI

17	Provost F , Fawcett T . Data science and its relationship to big data and data-driven decision making[J]. Big Data, 2013, 1 (1): 51- 59. DOI

18	Sutton R S , Barto A G . Reinforcement learning: An introduction[M]. Cambridge: MIT Press, 2018.

19	Demner-Fushman D , Chapman W W , McDonald C J . What can natural language processing do for clinical decision support?[J]. Journal of Biomedical Informatics, 2009, 42 (5): 760- 772. DOI

20	Basso B , Antle J . Digital agriculture to design sustainable agricultural systems[J]. Nature Sustainability, 2020, 3: 254- 256. DOI

21	Cropley D H , Kaufman J C , Cropley A J . Measuring creativity for innovation management[J]. Journal of Technology Management & Innovation, 2011, 6 (3): 13- 30.

22	Guilford J P . Creativity: Yesterday, today and tomorrow[J]. The Journal of Creative Behavior, 1967, 1 (1): 3- 14. DOI

23	Lake B M , Ullman T D , Tenenbaum J B , et al. Building machines that learn and think like people[J]. The Behavioral and Brain Sciences, 2017, 40: e253. DOI

24	Gigerenzer G , Gaissmaier W . Heuristic decision making[J]. Annual Review of Psychology, 2011, 62: 451- 482. DOI

25	Woolley A W , Chabris C F , Pentland A , et al. Evidence for a collective intelligence factor in the performance of human groups[J]. Science, 2010, 330 (6004): 686- 688. DOI

26	Brynjolfsson E , McAfee A . The second machine age: Work, progress, and prosperity in a time of brilliant technologies[M]. New York: W.W. Norton & Company, 2014.

27	Tversky A , Kahneman D . Judgment under uncertainty: Heuristics and biases[J]. Science, 1974, 185 (4157): 1124- 1131. DOI

28	Dietvorst B J , Simmons J P , Massey C . Algorithm aversion: People erroneously avoid algorithms after seeing them err[J]. Journal of Experimental Psychology General, 2015, 144 (1): 114- 126. DOI

29	Davenport T H , Kirby J . Only humans need apply: Winners and losers in the age of smart machines[M]. New York: Harper Business, 2016.

30	Simon H A . A behavioral model of rational choice[J]. The Quarterly Journal of Economics, 1955, 69 (1): 99. DOI

31	Squire L R . Memory systems of the brain: A brief history and current perspective[J]. Neurobiology of Learning and Memory, 2004, 82 (3): 171- 177. DOI

32	Lerner J S , Keltner D . Beyond valence: Toward a model of emotion-specific influences on judgement and choice[J]. Cognition & Emotion, 2000, 14 (4): 473- 493.

33	Lim J , Wu W C , Wang J J , et al. Imaging brain fatigue from sustained mental workload: An ASL perfusion study of the time-on-task effect[J]. NeuroImage, 2010, 49 (4): 3426- 3435. DOI

34	Minsky M . Society of mind[M]. New York: Simon and Schuster, 1988.

35	Lerner J S , Li Y , Valdesolo P , et al. Emotion and decision making[J]. Annual Review of Psychology, 2015, 66: 799- 823. DOI

36	Corti K , Gillespie A . A truly human interface: Interacting face-to-face with someone whose words are determined by a computer program[J]. Frontiers in Psychology, 2015, 6: 634.

37	McDuff D , El Kaliouby R , Senechal T , et al. Automatic measurement of ad preferences from facial responses gathered over the Internet[J]. Image and Vision Computing, 2014, 32 (10): 630- 640. DOI

38	Breazeal C . Toward sociable robots[J]. Robotics and Autonomous Systems, 2003, 42 (3/4): 167- 175.

39	Franti E , Ispas I , Dragomir V , et al. Voice based emotion recognition with convolutional neural networks for companion robots[J]. Science and Technology, 2018, 20 (3): 222- 240. DOI

40	Castellano G, Villalba S D, Camurri A. Recognising human emotions from body movement and gesture dynamics[M]//Affective Computing and Intelligent Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007: 71-82.

41	Eyben F, Weninger F, Gross F, et al. Recent developments in openSMILE, the Munich open-source multimedia feature extractor[C]//Proceedings of the 21st ACM International Conference on Multimedia. New York: ACM, 2013: 835-838.

42	Koelstra S , Muhl C , Soleymani M , et al. DEAP: A database for emotion Analysis; Using physiological signals[J]. IEEE Transactions on Affective Computing, 2012, 3 (1): 18- 31. DOI

43	Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality [EB/OL]. [2023-05-06]. https://arxiv.org/abs/1310.4546v1.

44	Zhang Z P , Luo P , Loy C C , et al. Learning deep representation for face alignment with auxiliary attributes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38 (5): 918- 930. DOI

45	Hu X , Chen J J , Wang F , et al. Ten challenges for EEG-based affective computing[J]. Brain Science Advances, 2019, 5 (1): 1- 20. DOI

46	Calvo R A , D'Mello S . Affect detection: An interdisciplinary review of models, methods, and their applications[J]. IEEE Transactions on Affective Computing, 2010, 1 (1): 18- 37. DOI

47	Hu X , Wang F , Zhang D . Similar brains blend emotion in similar ways: Neural representations of individual difference in emotion profiles[J]. NeuroImage, 2022, 247: 118819. DOI

48	Riek L D, Rabinowitch T C, Chakrabarti B, et al. How anthropomorphism affects empathy toward robots[C]//Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction. New York: ACM, 2009: 245-246.

49	Rafaeli A , Vilnai-Yavetz I . Emotion as a connection of physical artifacts and organizations[J]. Organization Science, 2004, 15 (6): 671- 686. DOI

50	Bickmore T W , Picard R W . Establishing and maintaining long-term human-computer relationships[J]. ACM Transactions on Computer-Human Interaction, 2005, 12 (2): 293- 327. DOI

51	Riek L D . Healthcare robotics[J]. Communications of the ACM, 2017, 60 (11): 68- 78. DOI

Freedy A, Weltman G, Freedy E, et al. Adaptive delegation interfaces (ADI) for improved situation awareness and reduction of workload in controlling multiple unmanned vehicles (UV) [EB/OL]. [2023-03-01]. https://www.researchgate.net/publication/235131830_Adaptive_Delegation_Interfaces_ADI_for_Improved_Situation_Awareness_and_Reduction_of_Workload_in_Controlling_Multiple_Unmanned_Vehicles_UV.

53	Parasuraman R , Riley V . Humans and automation: Use, misuse, disuse, abuse[J]. Human Factors, 1997, 39 (2): 230- 253. DOI

54	Schaefer K E , Chen J Y C , Szalma J L , et al. A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems[J]. Human Factors, 2016, 58 (3): 377- 400. DOI

55	Kok B C , Soh H . Trust in robots: Challenges and opportunities[J]. Current Robotics Reports, 2020, 1 (4): 297- 309. DOI

56	Chen M , Nikolaidis S , Soh H , et al. Trust-aware decision making for human-robot collaboration: Model learning and planning[J]. ACM Transactions on Human-Robot Interaction, 2020, 9 (2): 1- 23.

57	Lee J D , See K A . Trust in automation: Designing for appropriate reliance[J]. Human Factors, 2004, 46 (1): 50- 80. DOI

58	Merritt S M , Ilgen D R . Not all trust is created equal: Dispositional and history-based trust in human-automation interactions[J]. Human Factors, 2008, 50 (2): 194- 210. DOI

59	Shortliffe E H , Sepúlveda M J . Clinical decision support in the era of artificial intelligence[J]. JAMA, 2018, 320 (21): 2199- 2200. DOI

60	Kirchkamp O , Strobel C . Sharing responsibility with a machine[J]. Journal of Behavioral and Experimental Economics, 2019, 80: 25- 33. DOI

61	Madhavan P , Wiegmann D A . Similarities and differences between human-human and human-automation trust: An integrative review[J]. Theoretical Issues in Ergonomics Science, 2007, 8 (4): 277- 301. DOI

62	Swar B , Hameed T , Reychav I . Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search[J]. Computers in Human Behavior, 2017, 70: 416- 425. DOI

63	Poursabzi-Sangdeh F, Goldstein D G, Hofman J M, et al. Manipulating and measuring model interpretability[C]//Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. New York: ACM, 2021: 1-52.

64	Nass C , Moon Y . Machines and mindlessness: Social responses to computers[J]. Journal of Social Issues, 2000, 56 (1): 81- 103. DOI

65	Parasuraman R , Sheridan T B , Wickens C D . A model for types and levels of human interaction with automation[J]. IEEE Transactions on Systems, Man, and Cybernetics Part A, Systems and Humans, 2000, 30 (3): 286- 297. DOI

66	Xiong W , Wang C , Ma L . Partner or subordinate? Sequential risky decision-making behaviors under human-machine collaboration contexts[J]. Computers in Human Behavior, 2023, 139: 107556. DOI

67	Parasuraman R , Manzey D H . Complacency and bias in human use of automation: An attentional integration[J]. Human Factors, 2010, 52 (3): 381- 410. DOI

68	Haesevoets T , De Cremer D , Dierckx K , et al. Human-machine collaboration in managerial decision making[J]. Computers in Human Behavior, 2021, 119: 106730. DOI

69	Langer E J . The illusion of control[J]. Journal of Personality and Social Psychology, 1975, 32 (2): 311- 328. DOI

70	Byrne E A , Parasuraman R . Psychophysiology and adaptive automation[J]. Biological Psychology, 1996, 42 (3): 249- 268. DOI

71	Scerbo M W. Adaptive automation[M]//Neuroergonomics. Oxford: Oxford University Press, 2006: 239-252.

72	Bobadilla-Suarez S , Sunstein C R , Sharot T . The intrinsic value of choice: The propensity to under-delegate in the face of potential gains and losses[J]. Journal of Risk and Uncertainty, 2017, 54 (3): 187- 202. DOI

73	Bonnefon J F , Shariff A , Rahwan I . The social dilemma of autonomous vehicles[J]. Science, 2016, 352 (6293): 1573- 1576. DOI

74	Buolamwini J, Gebru T. Gender shades: Intersectional accuracy disparities in commercial gender classification[C]// Conference on fairness, accountability and transparency. New York: PMLR, 2018: 77-91.

75	Nyholm S , Smids J . The ethics of accident-algorithms for self-driving cars: An applied trolley problem?[J]. Ethical Theory and Moral Practice, 2016, 19 (5): 1275- 1289. DOI

76	Barocas S , Selbst A D . Big data's disparate impact[J]. California Law Review, 2016, 104: 671.

77	Bryson J J , Diamantis M E , Grant T D . Of, for, and by the people: The legal Lacuna of synthetic persons[J]. Artificial Intelligence and Law, 2017, 25 (3): 273- 291. DOI

78	李睿晶, 房超, 陈凯. 新时代我国人工智能发展回顾与展望[J]. 科技智囊, 2023 (1): 14- 21.

79	Miller T . Explanation in artificial intelligence: Insights from the social sciences[J]. Artificial Intelligence, 2019, 267: 1- 38. DOI

80	Crandall J W , Oudah M , Tennom , et al. Cooperating with machines[J]. Nature Communications, 2018, 9 (1): 233. DOI

81	Arntz M, Gregory T, Zierahn U. The risk of automation for jobs in OECD countries: A comparative analysiss[R]. Paris: OECD Publishing, 2016.

82	Sauer J , Hockey G R J , Wastell D G . Effects of training on short-and long-term skill retention in a complex multiple-task environment[J]. Ergonomics, 2000, 43 (12): 2043- 2064. DOI

83	张丹, 李佳蔚. 探索思维的力量: 脑机接口研究现状与展望[J]. 科技导报, 2017, 35 (9): 62- 67.

84	Gao X R , Wang Y J , Chen X G , et al. Interface, interaction, and intelligence in generalized brain-computer interfaces[J]. Trends in Cognitive Sciences, 2021, 25 (8): 671- 684. DOI

Options

文章导航

摘要

本文引用格式

参考文献

联系我们

访问统计

模态框（Modal）标题

摘要

本文引用格式

参考文献

联系我们

访问统计