Social Concepts Simplify Complex Reinforcement Learning

被引:2
|
作者
Hackel, Leor M. [1 ]
Kalkstein, David A. [2 ]
机构
[1] Univ Southern Calif, Dept Psychol, Los Angeles, CA 90007 USA
[2] Stanford Univ, Dept Psychol, Stanford, CA USA
关键词
concepts; generalization; reinforcement learning; relational reasoning; rewards; social cognition; open data; open materials; preregistered; RELATIONAL LANGUAGE; INFERENCES; KNOWLEDGE; SELECTION; CHOICES;
D O I
10.1177/09567976231180587
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Humans often generalize rewarding experiences across abstract social roles. Theories of reward learning suggest that people generalize through model-based learning, but such learning is cognitively costly. Why do people seem to generalize across social roles with ease? Humans are social experts who easily recognize social roles that reflect familiar semantic concepts (e.g., "helper" or "teacher"). People may associate these roles with model-free reward (e.g., learning that helpers are rewarding), allowing them to generalize easily (e.g., interacting with novel individuals identified as helpers). In four online experiments with U.S. adults (N = 577), we found evidence that social concepts ease complex learning (people generalize more and at faster speed) and that people attach reward directly to abstract roles (they generalize even when roles are unrelated to task structure). These results demonstrate how familiar concepts allow complex behavior to emerge from simple strategies, highlighting social interaction as a prototype for studying cognitive ease in the face of environmental complexity.
引用
收藏
页码:968 / 983
页数:16
相关论文
共 50 条
  • [11] Depression Detection on Social Media with Reinforcement Learning
    Gui, Tao
    Zhang, Qi
    Zhu, Liang
    Zhou, Xu
    Peng, Minlong
    Huang, Xuanjing
    CHINESE COMPUTATIONAL LINGUISTICS, CCL 2019, 2019, 11856 : 613 - 624
  • [12] Testing the reinforcement learning hypothesis of social conformity
    Levorsen, Marie
    Ito, Ayahito
    Suzuki, Shinsuke
    Izuma, Keise
    HUMAN BRAIN MAPPING, 2021, 42 (05) : 1328 - 1342
  • [13] The Evolution of Social Dominance through Reinforcement Learning
    Leimar, Olof
    AMERICAN NATURALIST, 2021, 197 (05) : 560 - 575
  • [14] Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems
    Kool, Wouter
    Gershman, Samuel J.
    Cushman, Fiery A.
    PSYCHOLOGICAL SCIENCE, 2017, 28 (09) : 1321 - 1333
  • [15] Social aspiration reinforcement learning in Cournot games
    Fatas, Enrique
    Morales, Antonio J.
    Jaramillo-Gutierrez, Ainhoa
    ECONOMIC THEORY, 2024,
  • [16] HIERARCHICAL FUNCTIONAL CONCEPTS FOR KNOWLEDGE TRANSFER AMONG REINFORCEMENT LEARNING AGENTS
    Mousavi, A.
    Ahmadabadi, M. Nili
    Vosoughpour, H.
    Araabi, B. N.
    Zaare, N.
    IRANIAN JOURNAL OF FUZZY SYSTEMS, 2015, 12 (05): : 99 - 116
  • [17] A Survey on Reinforcement Learning and Deep Reinforcement Learning for Recommender Systems
    Rezaei, Mehrdad
    Tabrizi, Nasseh
    DEEP LEARNING THEORY AND APPLICATIONS, DELTA 2023, 2023, 1875 : 385 - 402
  • [18] A reinforcement learning formulation to the complex question answering problem
    Chali, Yllias
    Hasan, Sadid A.
    Mojahid, Mustapha
    INFORMATION PROCESSING & MANAGEMENT, 2015, 51 (03) : 252 - 272
  • [19] Reinforcement Learning for Efficient Scheduling in Complex Semiconductor Equipment
    Suerich, Doug
    Young, Terry
    2020 31ST ANNUAL SEMI ADVANCED SEMICONDUCTOR MANUFACTURING CONFERENCE (ASMC), 2020,
  • [20] Complex-valued reinforcement learning with hierarchical architecture
    Yamazaki, Atsuhiro
    Hamagami, Tomoki
    Shibuya, Takeshi
    IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010), 2010,