Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： If the influence of income and trade cost were neglected, it is natural that people are willing to pay the same for something as they would require when sell it. However, Prof. Thaler discovered that it is not always the truth. It happens in daily life that individuals attach higher value to the things they own and thus their willingness to pay and willingness to sell for the very item may differ. Enlightened by the prospect theory, Thaler explained this anomaly by loss aversion and named it endowment effect. Following him, many researchers have made further exploration and argument from various angles. In this paper, we summarized previous work about endowment effect and list different explanations including loss aversion theory, psychological ownership theory, biased cognitive processing theory and evolution theory. This paper elaborated the interrelationship and disputes among the theories and finally gave the reason why endowment effect is not abnormal. Meanwhile, endowment effect plays an important role in guiding government work as well as business strategy making. Future research should enrich the theory framework, expand applications and strengthen the research in Chinese background.
Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： In social interaction, people have a tendency to copy observed actions. This automatic imitation is crucial for understanding others’ feelings behind actions, but can also result in potential conflicts between motor representations of self and other. Therefore, we need to distinguish our own motor plan from that of others and identify the conflicts. This capacity was termed self-other control (SOC). Similar to imitation control, higher levels of social cognition, such as theory of mind, perspective-taking, and empathy, also involve the processing of information about self and others. Much evidence suggested that SOC was a domain-general mechanism, as imitation control and other socio-cognitive processes in the brain shared the same SOC system to distinguish between information of self and other and regulate conflicts thereof. Some recent studies showed that, comparing with inhibitory control (IC) which was to suppress one’s own prepotent responses, SOC played a more pivotal role in social cognition, and the effect of IC on social cognition was moderated by SOC. In addition, the domain-generality of SOC indicates that in the future, individuals with certain socio-cognitive deficits (such as autism and alexithymia) would benefit from rehabilitation via motor-imitation control training.
Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Third-party punishment (TPP) is a substantial and special kind of altruistic behaviors, which could help maintain social norms and human cooperation. A large body of research has studied norm conformity like fair behaviors and its underlying cognitive mechanisms, merely few studies, however, have discussed norm enforcement behaviors like TPP and its cognitive process. One issue of strong interest is the way how cognitive control influences TPP. Thus, through (1) exploring the specific role of cognitive control in TPP by means of employing different technical methods; (2) from the perspective of developmental psychology, examining how the effects of cognitive control vary by stages of development, particularly focusing on preschoolers and adolescents, who are undergoing rapid development of cognitive control, the present project aims to deepen the understanding of the cognitive basis of TPP, explain the developmental trajectory of TPP, and help build a psychological model for the TPP decision making.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Altruistic behavior is a behavior that benefits others at a cost to oneself, which is of great significance to groups and individuals. People differ significantly in their everyday altruistic behaviors, which is partly influenced by genetic factors. Recently, researchers have focused on the role of genes in altruistic behavior. First, the heritability of altruistic behavior was explored based on the method of quantitative genetics. A large number of studies have confirmed that altruistic behavior is indeed affected by heredity, and heritability estimates vary among studies (0~0.87). The heritability of altruistic behavior may be influenced by factors such as age, the method of measurement, and some environmental factors (e.g., culture, family environments). Second, based on molecular genetic research, researchers have found four categories of altruism-related candidate genes, including dopamine receptor genes, serotonin transporter genes, oxytocin receptor genes, and vasopressin receptor genes. These findings confirmed that altruistic behavior was correlated with some gene loci. Taken together, both quantitative and molecular genetic studies have provided abundant genetic evidence of altruistic behavior. Furthermore, according to the aforementioned studies, the environment has been proven not only to affect the heritability of altruistic behavior but also to play a key role in the influence of genes on altruistic behavior in both quantitative and molecular genetic studies. On the one hand, genotype is associated with an environment that jointly influences altruistic behavior, known as gene-environment correlation. There are three types of gene-environment correlations: passive, evocative and active. On the other hand, the effect of genetics on altruistic behavior is influenced by the environment, known as the differential susceptibility model; that is, the environment affects the development of the altruistic behavior of susceptibility gene carriers in a manner of “strengthening or weakening.” Accordingly, a large number of findings regarding the interactions between genes and the environment on altruism have been found in oxytocin receptor genes and dopamine receptor genes. The current studies have some problems, such as speculative selection of altruistic candidate genes and inconsistent conclusions. Future research needs to expand on and further explore the effect of neurobiological systems on altruistic behavior, which may focus on genome-wide research, meta-analysis, mechanism exploration, and systematic environmental intervention practice.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： Background. Academic cheating has been a challenging problem for educators for centuries. It is well established that students often cheat not only on exams but also on homework. Despites recent changes in educational policy and practice, homework remains one of the most important academic tasks for elementary school students in China. However, most of the existing studies on academic cheating for the last century have focused almost exclusively on college and secondary school students, with few on the crucial elementary school period when academic integrity begins to form and develop. Further, most research has focused on cheating on exams with little on homework cheating. The present research aimed to bridge this significant gap in the literature. We used the advanced artificial intelligence methods to investigate the development of homework cheating in elementary school children and the key contributing factors so as to provide scientific basis for the development of early intervention methods to promote academic integrity and reduce cheating. Method. We surveyed elementary school students from Grades 2 to 6 and obtained a valid sample of 2,098. The questionnaire included students’ self-reported cheating on homework (the dependent variable). The predictor variables included children’s ratings of (1) their perceptions of the severity of consequences for being caught cheating, (2) the extent to which they found cheating to be acceptable, and the extent to which they thought their peers considered cheating to be acceptable, (3) their perceptions of the effectiveness of various strategies adults use to reduce cheating, (4) how frequently they observed their peers engaging in cheating, and (5) several demographic variables. We used ensemble machine learning (an emerging artificial intelligence methodology) to capture the complex relations between cheating on homework and various predictor variables and used the Shapley importance values to identify the most important factors contributing children’s decisions to cheat on homework. Results. Overall, 33% of elementary school students reported having cheated on homework, and the rate of such self-reported cheating behavior increased with grade. The best models with the ensemble machine learning accurately predicted the students’ homework cheating with a mean Area Under the Curve (AUC) value of 80.46%. The Shapley importance values showed that all predictors significantly contributed to the high performance of our computational models. However, their importance values varied significantly. Children’s cheating was most strongly predicted by their own beliefs about the acceptability of cheatings, how commonly and frequently they had observed their peers engaging in academic cheating, and their achievement level. Other predictors such as children’s beliefs about the severity of the possible consequences of cheating (e.g., being punished by one’s teacher), their beliefs about the effectiveness of cheating deterrence strategies (e.g., working harder) and demographic characteristics, though significantly, were not important predictors of elementary school children’s homework cheating. Conclusion. This study for the first time examined elementary school students' homework cheating behavior. We used machine learning integration algorithms to systematically investigate the key factors contributing to elementary school students' homework cheating. The results showed that homework cheating already exists in the elementary school period and increases with grade. Advanced machine learning algorithms revealed that elementary school students' homework cheating largely depends on their acceptance of cheating, their peers' homework cheating, and their own academic performance level. The present findings advance our theoretical understanding of the early development of academic integrity and dishonesty and forms the scientific basis for developing early intervention programs to reduce academic cheating. In addition, this study also shows that machine learning, as the core method of artificial intelligence, is an effective method that can be used to analyze developmental data analysis.
Subjects: Physics >> General Physics: Statistical and Quantum Mechanics, Quantum Information, etc. submitted time 2023-03-16 Cooperative journals: 《心理学报》
Abstract： Background. Academic cheating has been a challenging problem for educators for centuries. It is well established that students often cheat not only on exams but also on homework. Despites recent changes in educational policy and practice, homework remains one of the most important academic tasks for elementary school students in China. However, most of the existing studies on academic cheating for the last century have focused almost exclusively on college and secondary school students, with few on the crucial elementary school period when academic integrity begins to form and develop. Further, most research has focused on cheating on exams with little on homework cheating. The present research aimed to bridge this significant gap in the literature. We used the advanced artificial intelligence methods to investigate the development of homework cheating in elementary school children and the key contributing factors so as to provide scientific basis for the development of early intervention methods to promote academic integrity and reduce cheating. Method. We surveyed elementary school students from Grades 2 to 6 and obtained a valid sample of 2,098. The questionnaire included students’ self-reported cheating on homework (the dependent variable). The predictor variables included children’s ratings of (1) their perceptions of the severity of consequences for being caught cheating, (2) the extent to which they found cheating to be acceptable, and the extent to which they thought their peers considered cheating to be acceptable, (3) their perceptions of the effectiveness of various strategies adults use to reduce cheating, (4) how frequently they observed their peers engaging in cheating, and (5) several demographic variables. We used ensemble machine learning (an emerging artificial intelligence methodology) to capture the complex relations between cheating on homework and various predictor variables and used the Shapley importance values to identify the most important factors contributing children’s decisions to cheat on homework. Results. Overall, 33% of elementary school students reported having cheated on homework, and the rate of such self-reported cheating behavior increased with grade. The best models with the ensemble machine learning accurately predicted the students’ homework cheating with a mean Area Under the Curve (AUC) value of 80.46%. The Shapley importance values showed that all predictors significantly contributed to the high performance of our computational models. However, their importance values varied significantly. Children’s cheating was most strongly predicted by their own beliefs about the acceptability of cheatings, how commonly and frequently they had observed their peers engaging in academic cheating, and their achievement level. Other predictors such as children’s beliefs about the severity of the possible consequences of cheating (e.g., being punished by one’s teacher), their beliefs about the effectiveness of cheating deterrence strategies (e.g., working harder) and demographic characteristics, though significantly, were not important predictors of elementary school children’s homework cheating. Conclusion. This study for the first time examined elementary school students' homework cheating behavior. We used machine learning integration algorithms to systematically investigate the key factors contributing to elementary school students' homework cheating. The results showed that homework cheating already exists in the elementary school period and increases with grade. Advanced machine learning algorithms revealed that elementary school students' homework cheating largely depends on their acceptance of cheating, their peers' homework cheating, and their own academic performance level. The present findings advance our theoretical understanding of the early development of academic integrity and dishonesty and forms the scientific basis for developing early intervention programs to reduce academic cheating. In addition, this study also shows that machine learning, as the core method of artificial intelligence, is an effective method that can be used to analyze developmental data analysis.
Subjects: Psychology >> Other Disciplines of Psychology submitted time 2019-02-25 Cooperative journals: 《心理学报》
Abstract：在社会互动中，人们具有自动模仿他人动作的倾向。尽管这种自动模仿有利于个体理解他人动作的感受，但有时也会与自身的动作意图产生冲突。因此我们需要将自身动作意图与他人动作进行区分并调控二者之间的冲突。这种能力被称为自我―他人控制(self-other control, SOC)。与动作模仿控制相同，心理理论、观点采择和共情等更高级的社会认知同样涉及对自我和他人信息的加工。很多证据表明，SOC 可能是一种领域普遍的(domain-general)加工机制，即在动作模仿控制和其他社会认知中，大脑对自我和他人双方信息的区分和冲突调控共用同一套 SOC 系统。最近一些研究发现，相比于抑制自身优势反应的抑制控制(inhibitory control)，SOC 是社会认知中一个更为关键的影响因素，抑制控制对社会认知的作用受到 SOC 的调节。此外，SOC 的领域普遍性提示我们，未来可以通过简单的动作模仿控制训练，来为社会认知受损个体（如孤独症和述情障碍者）进行康复训练。
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