描述信息与经验不一致对个体风险选择的影响：决策模型的拟合

周广方; 魏子晗; 欧阳良媛; Ngure Mary Muthoni; 王晓庄

doi:10.12139/j.1672-0628.2022.01.005

心理与行为研究 >

2022 , Vol. 20 >Issue 1: 29 - 36

DOI: https://doi.org/10.12139/j.1672-0628.2022.01.005

基础心理学

描述信息与经验不一致对个体风险选择的影响：决策模型的拟合

周广方 ,
魏子晗 ,
欧阳良媛 ,
Ngure Mary Muthoni ,
王晓庄

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1. 教育部人文社会科学重点研究基地天津师范大学心理与行为研究院，天津 300387;
2. 天津师范大学心理学部，天津 300387;
3. 学生心理发展与学习天津市高校社会科学实验室，天津 300387;
4. 天津机电职业技术学院，天津 300350

收稿日期: 2021-07-12

网络出版日期: 2022-01-20

基金资助

天津市哲学社会科学规划课题（TJJX18-012）

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The Impact of Description-Experience Conflict on Individual Risky Decision-Making: Decision Model Fitting

ZHOU Guangfang ,
WEI Zihan ,
OUYANG Liangyuan ,
NGURE Mary Muthoni ,
WANG Xiaozhuang

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1. Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387;
2. Faculty of Psychology, Tianjin Normal University, Tianjin 300387;
3. Tianjin Social Science Laboratory of Students’ Mental Development and Learning, Tianjin 300387;
4. Tianjin Vocational College of Mechanics and Electricity, Tianjin 300350

Received date: 2021-07-12

Online published: 2022-01-20

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摘要

采用描述−经验冲突范式，考察损益情境下，描述信息与经验不一致对个体风险选择的影响，通过模型拟合探究其内在机制。研究1a和研究1b采用单因素被试间设计，自变量为描述信息与经验不一致程度，因变量为风险选项选择率。结果发现，获益情境中，描述信息与经验不一致程度的主效应不显著；而损失情境中，风险选项选择率受到显著影响。研究2比较决策模型参数发现，获益情境中，描述信息权重参数ξ和选择一致性参数c显著小于损失情境，而近因参数φ显著大于损失情境。这表明，当描述信息与经验不一致时，相对于获益情境，损失情境下个体更大程度地采用描述信息进行决策。研究揭示了损益情境中描述信息与经验不一致影响个体风险选择的机制。

关键词： 损益情境; 描述决策; 经验决策; 决策模型

本文引用格式

周广方 , 魏子晗 , 欧阳良媛 , Ngure Mary Muthoni , 王晓庄 . 描述信息与经验不一致对个体风险选择的影响：决策模型的拟合[J]. 心理与行为研究, 2022 , 20(1) : 29 -36 . DOI: 10.12139/j.1672-0628.2022.01.005

Abstract

This study used the description-experience conflict paradigm to investigate the influence of description-experience inconsistency on individual risk choices in gain and loss situations, and explore its internal mechanism through decision model fitting. Both Study 1a and Study 1b adopted a single-factor between-subjects design. The independent variable was the degree of inconsistency between description and experience, and the dependent variable was the proportion of risky choices (R-rate). Results showed that in gain situations, the R-rate was not significantly influenced by degree of inconsistency between description and experience; while in the loss situations, the R-rate was significantly influenced. Study 2 found that the description’s weight parameter (ξ) in the gain situations was significantly smaller than that in the loss situations; while the recency parameter (?) was significantly larger than that in the loss situations. Therefore, under the condition of description-experience inconsistency, the individuals in loss situations were more inclined to make decisions based on the descriptive information than those in the gain situations.

Key words： gain and loss situation; decision from description; decision from experience; decision model

参考文献

高青林, 周媛. (2021). 计算模型视角下信任形成的心理和神经机制——基于信任博弈中投资者的角度. 心理科学进展, 29(1), 178–189
项鑫, 王乙. (2021). 中国人口老龄化现状、特点、原因及对策. 中国老年学杂志, 41(18), 4149–4152
张力元, 毕研玲, 张宝山, 陈璐. (2015). 老年人行为决策: 领域现状与挑战. 心理科学进展, 23(5), 858–870
Asgarova, R., Macaskill, A. C., & Hunt, M. J. (2020). Gain-loss asymmetry in experiential probability discounting. The Psychological Record, 70(3), 359–371, doi: 10.1007/s40732-020-00379-1.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48.
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370, doi: 10.1037/1089-2680.5.4.323.
Bodenhausen, G. V., Gabriel, S., & Lineberger, M. (2000). Sadness and susceptibility to judgmental bias: The case of anchoring. Psychological Science, 11(4), 320–323, doi: 10.1111/1467-9280.00263.
Dai, J. Y., Kerestes, R., Upton, D. J., Busemeyer, J. R., & Stout, J. C. (2015). An improved cognitive model of the Iowa and Soochow Gambling Tasks with regard to model fitting performance and tests of parameter consistency. Frontiers in Psychology, 6, 229.
Don, H. J., Otto, A. R., Cornwall, A. C., Davis, T., & Worthy, D. A. (2019). Learning reward frequency over reward probability: A tale of two learning rules. Cognition, 193, 104042, doi: 10.1016/j.cognition.2019.104042.
Dong, G. H., Lin, X., Zhou, H. L., & Du, X. X. (2014). Decision-making after continuous wins or losses in a randomized guessing task: Implications for how the prior selection results affect subsequent decision-making. Behavioral and Brain Functions, 10(1), 1–11, doi: 10.1186/1744-9081-10-1.
Fox, J., & Weisberg, S. (2019). car: Companion to Applied Regression (Version 4. 0. 2) . Retrieved February 7, 2020, from https://cran.r-project.org/web/packages/car/index.html
Glöckner, A., Fiedler, S., Hochman, G., Ayal, S., & Hilbig, B. E. (2012). Processing differences between descriptions and experience: A comparative analysis using eye-tracking and physiological measures. Frontiers in Psychology, 3, 173.
Gurevich, G. (2017). Description-experience gap in choice under risk: Are emotions involved? Retrieved June 30, 2019, from https://ssrn.com/abstract=2921415
Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15(8), 534–539, doi: 10.1111/j.0956-7976.2004.00715.x.
Hilbig, B. E. (2012). Good things don’t come easy (to mind): Explaining framing effects in judgments of truth. Experimental Psychology, 59(1), 38–46, doi: 10.1027/1618-3169/a000124.
Jessup, R. K., Bishara, A. J., & Busemeyer, J. R. (2008). Feedback produces divergence from prospect theory in descriptive choice. Psychological Science, 19(10), 1015–1022, doi: 10.1111/j.1467-9280.2008.02193.x.
Kahneman, D., & Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39(4), 341–350, doi: 10.1037/0003-066X.39.4.341.
Lebreton, M., Bacily, K., Palminteri, S., & Engelmann, J. B. (2019). Contextual influence on confidence judgments in human reinforcement learning. PLoS Computational Biology, 15(4), e1006973, doi: 10.1371/journal.pcbi.1006973.
Lee, D., Seo, H., & Jung, M. W. (2012). Neural basis of reinforcement learning and decision making. Annual Review of Neuroscience, 35, 287–308, doi: 10.1146/annurev-neuro-062111-150512.
Lejarraga, T., & Gonzalez, C. (2011). Effects of feedback and complexity on repeated decisions from description. Organizational Behavior and Human Decision Processes, 116(2), 286–295, doi: 10.1016/j.obhdp.2011.05.001.
Packard, M. G., & Knowlton, B. J. (2002). Learning and memory functions of the basal ganglia. Annual Review of Neuroscience, 25, 563–593, doi: 10.1146/annurev.neuro.25.112701.142937.
Schonberg, T., Daw, N. D., Joel, D., & O’Doherty, J. P. (2007). Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making. Journal of Neuroscience, 27(47), 12860–12867, doi: 10.1523/JNEUROSCI.2496-07.2007.
Siegrist, M., & Cvetkovich, G. (2001). Better negative than positive? Evidence of a bias for negative information about possible health dangers. Risk Analysis, 21(1), 199–206, doi: 10.1111/0272-4332.211102.
Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515–518, doi: 10.1126/science.1134239.
Weiss-Cohen, L., Konstantinidis, E., Speekenbrink, M., & Harvey, N. (2016). Incorporating conflicting descriptions into decisions from experience. Organizational Behavior and Human Decision Processes, 135, 55–69, doi: 10.1016/j.obhdp.2016.05.005.
Weiss-Cohen, L., Konstantinidis, E., Speekenbrink, M., & Harvey, N. (2018). Task complexity moderates the influence of descriptions in decisions from experience. Cognition, 170, 209–227, doi: 10.1016/j.cognition.2017.10.005.

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