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      Common and distinct equity preferences in children and adults

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          Abstract

          Fairness plays a crucial role in children’s social life and has garnered considerable attention. However, previous research and theories primarily examined the development of children’s fairness behaviors in the conflict between self-interest motivation and fairness-complying motivation, neglecting the influence of advantage-seeking motivation. Moreover, despite the well-established role of gain/loss frame in human decision-making, it remains largely unclear whether the framing effect modulates fairness behaviors in children. It was hypothesized that children would exhibit advantage-seeking motivation resulting in more selfish behaviors in the loss context. To examine the hypothesis, we combined an adapted dictator game and computational modeling to investigate various motivations underlying fairness behaviors of children in both loss and gain contexts and to explore the developmental directions by contrasting children and adults. In addition, the current design enabled the dissociation between fairness knowledge and behaviors by asking participants to decide for themselves (the first-party role) or for others (the third-party role). This study recruited a total of 34 children (9–10 years, M age = 9.82, SD age = 0.38, 16 females) and 31 college students ( M age = 19.81, SD age = 1.40, 17 females). The behavioral results indicated that children behaved more selfishly in first-party and more fairly in third-party than adults, without any significant framing effects. The computational results revealed that both children and adults exhibited aversion to advantageous and disadvantageous inequity in third-party. However, they showed distinct preferences for advantageous inequity in first-party, with advantage-seeking preferences among children and aversion to advantageous inequity among adults. These findings contribute to a deeper understanding of children’s social preferences and their developmental directions.

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          Most cited references106

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          Multimodel Inference: Understanding AIC and BIC in Model Selection

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            Stan: A Probabilistic Programming Language

            Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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              Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2507139/overviewRole: Role: Role: Role: Role:
                Role:
                URI : https://loop.frontiersin.org/people/224091/overviewRole:
                Role:
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                URI : https://loop.frontiersin.org/people/995769/overviewRole: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/876643/overviewRole: Role: Role: Role: Role:
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                14 February 2024
                2024
                : 15
                : 1330024
                Affiliations
                [1] 1School of Psychology, Shenzhen University , Shenzhen, China
                [2] 2Department of Psychology, University of Mannheim , Mannheim, Germany
                [3] 3Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education , Guangzhou, China
                [4] 4School of Psychology, South China Normal University , Guangzhou, China
                [5] 5Center for Studies of Psychological Application, South China Normal University , Guangzhou, China
                [6] 6Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University , Guangzhou, China
                [7] 7Department of Psychology, Sun Yat-Sen University , Guangzhou, China
                [8] 8Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions , Shenzhen, China
                Author notes

                Edited by: Kai S. Cortina, University of Michigan, United States

                Reviewed by: Yansong Li, Nanjing University, China

                Alessandra Geraci, Dante Alighieri University for Foreigners, Italy

                *Correspondence: Chunliang Feng, chunliang.feng@ 123456m.scnu.edu.cn
                Article
                10.3389/fpsyg.2024.1330024
                10899522
                38420165
                248596ac-ae6d-4c28-b3d1-1cf58f5598f5
                Copyright © 2024 Xu, Luo, Zhu, Zhao, Zhang, Zhang, Feng and Guan.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 October 2023
                : 30 January 2024
                Page count
                Figures: 6, Tables: 1, Equations: 5, References: 107, Pages: 15, Words: 11647
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Funded by: Natural Science Foundation of Guangdong Province, doi 10.13039/501100003453;
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (32071100, 32271126, 31920103009), Natural Science Foundation of Guangdong Province (2021A1515010746), the Major Project of National Social Science Foundation (20&ZD153), and Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions (2019SHIBS0003, 2023SHIBS0003).
                Categories
                Psychology
                Original Research
                Custom metadata
                Developmental Psychology

                Clinical Psychology & Psychiatry
                fairness,framing effect,inequity aversion,advantage-seeking,computational model

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