Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-10-09 Cooperative journals: 《心理学报》
Abstract：本研究考察对他人可信程度的感知是否会影响对该人物面孔长相的表征及其潜在的机制。实验1让被试形成目标人物可信或不可信的印象。随后利用反相关图像分类技术将被试对目标人物面孔的心理表征可视化。结果发现无论目标人物是男性还是女性, 高可信度的目标人物与更具吸引力和积极特质的面孔表征相关。实验2从一批新的被试中可视化了可信和不可信群体的面孔表征的特征, 并与实验1中获得的目标人物的面孔表征的特征做相似性分析, 发现被描述为可信(或不可信)的目标人物的面孔表征特征与可信(或不可信)群体的面孔表征特征有更多的相似性, 说明当人们得知他人是可信(或不可信)时, 会把脑海中的对应图式特征叠加到该人物的面孔物理特征上, 从而重塑面孔表征。本研究说明自上而下的加工方式在面孔表征形成中扮演了重要作用。
Subjects: Psychology >> Social Psychology submitted time 2023-05-01
Abstract： This study examined whether the perception of an individual’s trustworthiness influenced the mental representation of that face and its underlying mechanisms.
In Experiment 1, participants were instructed to develop an opinion about a target person’s trustworthiness, perceiving him/her as either trustworthy or untrustworthy. Then, the reverse correlation image classification technology was employed to visualize participants’ mental representations of the target person’s face. The results showed that regardless of the target person’s gender, those described as highly trustworthy were associated with more positive and attractive facial representations.
In Experiment 2, group’s facial mental representation features of trustworthy and untrustworthy faces were compared with those of the target person from Experiment 1. The results revealed that the face representation features of the target people described as trustworthy (or untrustworthy) had more similarities with those of trustworthy (or untrustworthy) groups.
The findings indicated that when people perceive an individual as trustworthy (or untrustworthy), they would superimpose the corresponding schema features in their minds on the physical characteristics of the perceived individual’s face, leading to a reconfiguration of the face representation. Overall, our study underscores the importance of top-down factors in shaping face representations.
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 >> Educational Psychology Subjects: Psychology >> Developmental Psychology submitted time 2022-12-01
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.
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