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“Neijuan” in China: The psychological concept and its characteristic dimensions ZHANG Wen, PAN Chao, YAO Shiming, ZHU Jiajia, LING Dong, YANG Hanchun, XU Jingsha, MU Yan Acta Psychologica Sinica    2024, 56 (1): 107-123.   DOI: 10.3724/SP.J.1041.2024.00107 Abstract （1448）   HTML （88）    PDF （175KB）（809）       Knowledge map    With the deepening and spread of reform and opening-up, China has undergone rapid and unprecedented economic growth and societal transformations over the past few decades. Accumulating evidence has revealed the impacts of sociocultural changes on Chinese mental health. Since 2020, a popular buzzword, “Neijuan” (involution), has garnered significant attention and discussion in daily life. Neijuan could be traced back to agricultural involution, which refers to a process of inward over-elaboration in agricultural development. This concept was first identified by the anthropologist Geertz (1963), who observed that population growth failed to enhance productivity growth and economic development. Despite Neijuan's growing attention, it is still unclear about the connotation and characteristic dimensions of this social phenomenon. Cultural psychology provides a solid theoretical and empirical basis for exploring how social and cultural changes affect individuals’ psychological states and behaviors. In this context, we propose that Neijuan is a multidimensional psychological concept of great significance in this new era, closely connected to cultural changes in China’s rapid development and growth. To explore the psychological concept of Neijuan, Study 1 employed a grounded theory approach through in-depth interviews to clarify the intricate psychological components of Neijuan, including resource scarcity, social norm, psychological pressure, and competition (see Figure 1). At the macro level, limited resources of society and organization would make people conform to the implicit norms and perform irrational behaviors related to Neijuan. At the micro level, people would perceive intrinsic and extrinsic stressors to make them feel stressed and lead to no-benign competitive behaviors. Based on the results of Study 1, Studies 2 and 3 developed a measurement tool to validate the multiple characteristic dimensions of Neijuan in Chinese culture, utilizing exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). We first designed the measurement including 68 items to assess individuals’ perception of Neijuan. Based on the classical measurement theory, the discrimination ability of 68 items was analyzed by using the independent sample t test and the correlation test of total scores and each item score as the discrimination index. Through item analysis, we deleted only one item because of no difference between the low- and high-score groups. Then, principal component analysis (PCA) and the Procrustes variance maximum-oblique rotation method were used to analyze the factors of 67 items. The results showed that there are four factors for the feature value greater than 1, the cumulative total variation is 56.62%, and the load value of each item is between 0.45 and 0.88. Further, we explored the rationality of the four-factor model. The results among employees and undergraduates showed that χ2/df was less than 3, SRMR was less than 0.10, TLI and CFI were all more than 0.80, and RMSEA was less than 0.10, which suggested the model fits well. Thus, we supplied the effective 18-item measurement for assessing the individual perception of Neijuan and confirmed that Neijuan comprises four dimensions: resource scarcity, social norm, psychological pressure, and competition. Subsequently, Study 4 used a Neijuan scenario-based task in the university and workplace environments to assess participants’ behavioral tendencies related to Neijuan and examined the relationship between individuals’ perceptions of Neijuan and their actual behaviors. Results revealed that individuals with higher levels of perceived Nejuan exhibited a greater tendency to engage in behaviors associated with Neijuan among employees (r = 0.66, p < 0.001) and undergraduates (r = 0.61, p < 0.001). In summary, the series of studies sought to explore the psychological concept and multiple characteristic dimensions of Neijuan, which provides a theoretical and empirical basis for understanding this significant phenomenon in the contemporary era. The current research also offers an effective measurement tool to assess individuals’ perception of Neijuan and enlightens future research on the effect of Neijuan on psychological maladjustment and non-benign competition behaviors related to Neijuan. Table and Figures | Reference | Related Articles | Metrics

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Associations between empathy and negative affect: Effect of emotion regulation GUO Xiao-dong, ZHENG Hong, RUAN Dun, HU Ding-ding, WANG Yi, WANG Yan-yu, Raymond C. K. CHAN Acta Psychologica Sinica    2023, 55 (6): 892-904.   DOI: 10.3724/SP.J.1041.2023.00892 Abstract （1054）   HTML （64）    PDF （492KB）（719）       Knowledge map    Empathy refers to understanding, inferring and sharing others’ emotional states, which can be divided into affective and cognitive components. Although empathy contributes to prosocial behaviors and harmonious interpersonal relationships, it also increases an individual’s negative emotional experiences and affect distress. Emotion regulation, the psychological process of managing one’s own emotions, has been found to be closely associated with empathy. Cognitive reappraisal and expressive suppression are two commonly used strategies to regulate emotions, of which cognitive reappraisal is effective in reducing negative emotional experiences while expressive suppression is usually correlated with more affective distress. However, the roles of emotion regulation strategies in the empathic response are still unclear. We conducted two studies to investigate the roles of emotion regulation on the negative affect related to empathy using self-report questionnaires and experimental task respectively. Study 1 administered the Questionnaire of Cognitive and Affective Empathy (QCAE), the Interpersonal Reactivity Index (IRI), the Emotion Regulation Questionnaire (ERQ), and the Depression Anxiety Stress Scale (DASS-21) to 442 college students. The moderating effects of cognitive reappraisal and expressive suppression on the association between empathy and negative affect were examined separately. Study 2 adopted the Chinese version of the Empathic Accuracy Task (EAT) to further examine the effect of emotion regulation (i.e. cognitive reappraisal) on cognitive empathy and affective responses. The EAT requires participants to continuously rate targets’ emotional valence in video clips as a second person and rate emotional valence and arousal of both targets and themselves after each video. Seventy-five participants (33 for Experiment 1 and 42 for Experiment 2) were recruited to perform the EAT under two conditions, i.e., naturally viewing without any instructions and applying cognitive reappraisal while viewing the scenarios. Paired sample t tests and repeated-measure ANOVA were performed to examine the effect of cognitive reappraisal on task performance. As shown in Figure 1, findings from Study 1 showed that affective empathy was significantly correlated with higher levels of anxiety (r = 0.14, p = 0.003) and stress (r = 0.14, p < 0.001), while empathic concern was correlated with less anxiety (r = -0.28, p < 0.001), stress (r = -0.27, p < 0.001) and depression (r = -0.22, p < 0.001). However, when participants endorsed cognitive reappraisal more frequently, such positive association between affective empathy and stress was reduced (β = 1.48, Wald = 5.22, p = 0.022), while the negative association between empathic concern and anxiety was strengthened (β = 0.66, Wald = 4.73, p = 0.030). Cognitive empathy was significantly correlated (or marginally significantly) with reduced depression (QCAE-CE: r = -0.08, p = 0.096; IRI-PT: r = -0.11, p = 0.019; IRI-FS: r = -0.10, p = 0.034). Expressive suppression strengthened the negative association between cognitive empathy and depression (β = 1.77, Wald = 5.32, p= 0.021). Moreover, negative correlations between cognitive empathy and anxiety (β = 1.33, Wald = 4.67, p = 0.031) as well as stress (β = -0.37, Wald= 4.43, p= 0.035) emerged for participants endorsing cognitive reappraisal more frequently. Findings from Study 2 showed that task performances of the EAT were significantly improved when participants endorsed cognitive reappraisal strategy compared to the condition of naturally viewing. Specifically, under the cognitive reappraisal condition participants scored higher empathic accuracy (Experiment 1: t = -2.27, p= 0.030, Cohen’s d = 0.40; Experiment 2: F(1, 40) = 4.13, p = 0.049, η2 = 0.09), experienced less negative affect (Experiment 1: t = -2.68, p= 0.012, Cohen’s d = 0.47; Experiment 2: F(1, 40) = 29.20, p < 0.001, η2 = 0.42) in reaction to others’ affect distress, and experienced more positive affect in reaction to others’ positive emotions (Experiment 1: t = -10.9, p< 0.001, Cohen’s d = 1.90; Experiment 2: F(1, 40) = 31.54, p < 0.001, η2 = 0.44) (see Table 1 & Figure 2). Taken together, the findings from these two studies suggested that both cognitive reappraisal and expressive suppression play a protective role in the associations between empathy and negative affect, and the endorsement of cognitive reappraisal would improve task performance on both cognitive and affective empathy. Our findings shed light on the psychological mechanisms of empathy and provide new approach for improving individuals’ social cognitive ability, especially for early intervention in clinical and subclinical populations. Table and Figures | Reference | Related Articles | Metrics

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Accept or change your fate: Exploring the Golem effect and underdog effect of underdog expectations MA Jun, ZHU Mengting Acta Psychologica Sinica    2023, 55 (6): 1029-1048.   DOI: 10.3724/SP.J.1041.2023.01029 Abstract （926）   HTML （30）    PDF （617KB）（2711）       Knowledge map    In organizations, some employees are heralded as rising stars, whilst others are considered underdogs with no prospects. Scholars define individuals’ perceptions that others view them as unlikely to succeed as underdog expectation. The traditional view indicates that when individuals experience underdog expectations from others, they will reduce their subsequent performance through a sense of self-efficacy. This phenomenon, in which one’s performance is manipulated by someone else’s negative assessment, is also known as the Golem effect. Indeed, some studies have suggested that underdog expectations can enhance their desire to prove others wrong to improve performance. However, such studies have only focused on the influence of underdog expectations on employee behavior as social-situation cues but have disregarded its interaction with individuals’ traits. By integrating the preceding arguments, we proposed a comprehensive model based on trait activation theory, which examines the Golem and underdog effects. Specifically, under the moderating effect of underdog expectations, employees with fixed mindsets have a negative impact on subsequent task performance through feedback- avoiding behavior. Meanwhile, employees with growth mindsets have a positive impact on subsequent task performance by proving others wrong. The task context (task focus vs. future focus) plays a role in inhibiting and amplifying the two interactions. This study aimed to explore the reasons why employees who are trapped in underdog expectations become a Golem manipulated by fate and how to counter strike and become an underdog in the workplace. This study constructed a three-term interaction model of nested moderated mediation model. Three studies were designed to explore the internal and intervention mechanisms of the Golem and underdog effects activated by underdog expectations. In the first study, the existence of three interactions was initially examined through a multi-source, multi-point questionnaire of 341 employees. To test the stability of the three interactions and the extensibility of the research conclusions in different groups, a second multi-source and multi-time questionnaire survey involving 650 employees and a field study based on a quasi-experiment were designed for retesting. Regression analysis, bootstrap method and Johnson?Neyman (J?N) technology were used to analyse the questionnaire data to examine the moderated mediation effects of the three-term interaction. T-tests were used to analyse data from the field study. The analyses of the study showed the following results. (1) The interaction between underdog expectations and fixed mindsets positively affects subsequent task performance through feedback-avoiding behavior. (2) The interaction between underdog expectations and growth mindsets positively affects subsequent task performance through the desire to prove others wrong. (3) Lastly, task focus reduces the positive moderating effect of underdog expectations on fixed mindsets, and future focus strengthens the positive moderating effect of underdog expectations on growth mindsets. Findings of our research have several theoretical and practical implications. This study revealed the causes of the Golem and underdog effects, thereby enriching and expanding the research on implicit theory. It showed that fixed and growth mindsets have different paths in processing negative information, which is helpful in integrating the research on underdog and topdog employees. It also provided a theoretical explanation and transformation idea for the emergence and popularity of the depressed culture represented by the lie down and Buddha-like mindsets. Table and Figures | Reference | Related Articles | Metrics

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The impact of different types of academic stress on subcomponents of executive function in high school students of different grades MA Chao, WANG Yanyun, FU Junjun, ZHAO Xin Acta Psychologica Sinica    2025, 57 (1): 18-35.   DOI: 10.3724/SP.J.1041.2025.0018 Abstract （894）   HTML （46）    PDF （1249KB）（1545）       Knowledge map    This study investigated the roles of four dimensions of academic stress in various executive function components among 985 high school students from grades 10 to 12 using correlation analysis and structural equation modeling. The results revealed that as students progressed through the high school grades, the negative predictive effects of parental stress and teacher stress on various executive function components gradually increased, while the negative predictive effect of social stress gradually decreased. In contrast, self-imposed stress exhibited a positive predictive effect on interference inhibition, response inhibition, and attention switching abilities among high school students, and this positive effect strengthened with advancing grades. These findings indicate that different types of academic stress have varying predictive effects on executive function components, and these effects change as students progress through high school. The conclusions drawn from this study have important implications for educators in effectively developing strategies to cope with academic stress among high school students. Table and Figures | Reference | Related Articles | Metrics

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Psychological richness increases behavioral intention to protect the environment WEI Xinni, YU Feng, PENG Kaiping, ZHONG Nian Acta Psychologica Sinica    2023, 55 (8): 1330-1343.   DOI: 10.3724/SP.J.1041.2023.01330 Abstract （818）   HTML （37）    PDF （369KB）（1269）       Knowledge map    Understanding the relationship between happiness and positive factors and pro-environmental behavior offers important practical implications for sustainable social development. To investigate the positive antecedents of pro-environmental behavior, the current study focused on psychological richness and examined its influence on pro-environmental behavior as well as potential mechanisms and boundary conditions through 10 studies (N = 2979). It is shown that psychological richness facilitates engagement in sustainable activities (Studies 1.1-1.4) through an increased level of self-expansion (Studies 2.1-2.4). Furthermore, the effect of self-expansion on pro-environmental behavior was more significant when individuals viewed nature as smaller than themselves (Studies 3.1-3.2). These findings reveal the positive effect of happiness on pro-environmental behaviors and provide insights to promote people's participation in building a sustainable society. Table and Figures | Reference | Related Articles | Metrics

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The influence of cultural differences between China and the West on moral responsibility judgments of virtual humans YAN Xiao, MO Tiantian, ZHOU Xinyue Acta Psychologica Sinica    2024, 56 (2): 161-178.   DOI: 10.3724/SP.J.1041.2024.00161 Abstract （757）   HTML （30）    PDF （1051KB）（832）       Knowledge map    Virtual humans are digital characters created in computer graphics software that take a first-person view of the world and have a social media presence. Compared with real humans, however, are people likely to attribute moral responsibility differently to virtual humans when they do something morally wrong? This important empirical question remains unanswered. Therefore, we addressed this query using Mental Perception Theory. We did so through exploring the influence and mechanism of cultural differences between China and the West on individuals’ moral responsibility judgments of virtual humans versus real humans. Findings revealed that, when virtual humans engaged in immoral behaviors—irrespective of whether real humans or artificial intelligence (AI) controlled them—people in China (vs. the West) attributed more moral responsibility to virtual humans but equal moral responsibility to real humans (Study 1a~1c). Perceived mental capacity, especially perceived experience, mediated the interaction effect of the culture differences (Study 2). Furthermore, compared with Westerners, Chinese people were more likely to punish virtual (vs. real) humans, such as by no longer following their social accounts (Study 3). The current research provided evidence for the cultural differences between Chinese people and Westerners on moral responsibility judgments of virtual humans and contributed to literature on cultural differences and the theory about moral judgments on non-human entities. Table and Figures | Reference | Related Articles | Metrics

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The influence of positive co-experience on teacher-student relationship: The mediating role of emotional bonding DING Yuting, ZHANG Chang, LI Ranran, DING Wenyu, ZHU Jing, LIU Wei, CHEN Ning Acta Psychologica Sinica    2023, 55 (5): 726-739.   DOI: 10.3724/SP.J.1041.2023.00726 Abstract （729）   HTML （52）    PDF （332KB）（693）       Knowledge map    Based on questionnaire survey, field experiment and laboratory experiment, this paper investigates the influence and mechanism of positive co-experience on adolescent teacher-student relationship. The results show that: (1) positive co-experiences positively affect teacher-student relationship, and different types of experiences (recall, imagination, example) are prominent promoting effect; (2) Positive emotional bonding plays a stable mediating role in the influence of positive co-experiences on teacher-student relationship. This study preliminarily proposed the “co-experience relationship effect model”, which promotes the research on the influence mechanism of teacher-student relationship, and has good ecological validity and practical educational value. Table and Figures | Reference | Related Articles | Metrics

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New research paradigms and agenda of human factors science in the intelligence era XU Wei, GAO Zaifeng, GE Liezhong Acta Psychologica Sinica    2024, 56 (3): 363-382.   DOI: 10.3724/SP.J.1041.2024.00363 Abstract （721）   HTML （35）    PDF （807KB）（1404）       Knowledge map    This paper first proposes the innovative concept of “human factors science” to characterize engineering psychology, human factors engineering, ergonomics, human-computer interaction, and other similar fields. Although the perspectives in these fields differ, they share a common goal: optimizing the human-machine relationship by applying a “human-centered design” approach. AI technology has brought in new characteristics, and our recent research reveals that the human-machine relationship presents a trans-era evolution from “human-machine interaction” to “human-AI teaming.” These changes have raised questions and challenges for human factors science, compelling us to re-examine current research paradigms and agendas. In this context, this paper reviews and discusses the implications of the following three conceptual frameworks that we recently proposed to enrich the research paradigms for human factors science. (1) human-AI joint cognitive systems: This model differs from the traditional human-computer interaction paradigm and regards an intelligent system as a cognitive agent with a certain level of cognitive capabilities. Thus, a human-AI system can be characterized as a joint cognitive system in which two cognitive agents (human and intelligent agents) work as teammates for collaboration. (2) human-AI joint cognitive ecosystems: An intelligent ecosystem with multiple human-AI systems can be represented as a human-AI joint cognitive ecosystem. The overall system performance of the intelligent ecosystem depends on optimal cooperation and design across the multiple human-AI systems. (3) intelligent sociotechnical systems (iSTS): human-AI systems are designed, developed, and deployed in an iSTS environment. From a macro perspective, iSTS focuses on the interdependency between the technical and social subsystems. The successful design, development, and deployment of a human-AI system within an iSTS environment depends on the synergistic optimization between the two subsystems. This paper further enhances these frameworks from the research paradigm perspective. We propose three new research paradigms for human factors science in the intelligence ear: human-AI joint cognitive systems, human-AI joint cognitive ecosystems, and intelligent sociotechnical systems, enabling comprehensive human factors science solutions for AI-based intelligent systems. Further analyses show that the three new research paradigms will benefit future research in human factors science. Furthermore, this paper looks forward to the future research agenda of human factors science from three aspects: “human-AI interaction,” “intelligent human-machine interface,” and “human-AI teaming.” We believe the proposed research paradigms and the future research agenda will mutually promote each other, further advancing human factors science in the intelligence era. Table and Figures | Reference | Related Articles | Metrics

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Trend analysis of marital satisfaction of mainland Chinese couples in the past 20 years HOU Juan, JIA Keke, FANG Xiaoyi Acta Psychologica Sinica    2024, 56 (7): 895-910.   DOI: 10.3724/SP.J.1041.2024.00895 Abstract （721）   HTML （16）    PDF （200KB）（315）       Knowledge map    Table and Figures | Reference | Related Articles | Metrics

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The cognitive mechanism of reducing procrastination by emotion regulation: The mediation role of task aversiveness TONG Tingting, BAI Youling, FENG Tingyong Acta Psychologica Sinica    2024, 56 (4): 458-468.   DOI: 10.3724/SP.J.1041.2024.00458 Abstract （715）   HTML （50）    PDF （364KB）（415）       Knowledge map    Prior studies have demonstrated that employing adaptive emotion-regulation strategies, such as cognitive reappraisal, can effectively mitigate procrastination. Nevertheless, the cognitive mechanisms that account for the impact of emotion regulation on procrastination still lack clarity. The temporal decision model of procrastination postulates that procrastination is primarily influenced by the tradeoff between task aversiveness and outcome utility. When task aversiveness exceeds outcome utility, individuals are prone to procrastination; conversely, if outcome utility outweighs task aversiveness, they are more likely to take immediate action. Consequently, emotion regulation has the potential to reduce procrastination by either diminishing task aversiveness or enhancing outcome utility. In order to explore this matter, this study adopts Gross’s emotion regulation theory and the temporal decision model of procrastination. Specifically, the study targets individuals with high levels of procrastination, as indicated by scores above 67.5 on the General Procrastination Scale. These individuals were assigned to the positive reappraisal group (n = 34) and the ineffective strategy group (n = 34), respectively. The longitudinal tracking of both groups took place over a period of 7 days, resulting in a total of 14 data collection points obtained through empirical sampling. The results showed that: (1) There was no notable disparity between the two groups in task executive willingness during the pre-test (M Pre-test of positive reappraisal group = 2.05, SD = 1.67, M Pre-test of ineffective strategy group = 2.42, SD = 2.17; F(1, 202) = 1.88, p = 0.172), while the positive reappraisal group demonstrated a significantly higher task executive willingness compared to the ineffective strategy group in the post-test (M Post-test of positive reappraisal group = 5.26, SD = 1.97, M Post-test of ineffective strategy group = 2.91, SD = 2.44; F(1, 202) = 57.49, p < 0.001, partial η2 = 0.22) (Figure 1a), indicating that positive reappraisal significantly enhanced individuals’ task executive willingness. (2) No significant difference was observed in task aversiveness between the two groups during the pre-test (M Pre-test of positive reappraisal group = − 5.81, SD = 1.65, M Pre-test of ineffective strategy group = −5.56, SD = 1.88; F(1, 202) = 1.06, p = 0.304), while the positive reappraisal group exhibited noticeably lower levels of task aversiveness compared to the ineffective strategy group in the post-test (M Post-test of positive reappraisal group = − 0.77, SD = 3.19, M Post-test of ineffective strategy group = − 3.75, SD = 3.02; F(1, 202) = 46.59, p < 0.001, partial η2 = 0.19) (Figure 2a). Additionally, initial outcome utility levels did not differ significantly between the two groups (M Pre-test of positive reappraisal group = 6.94, SD = 2.33, M Pre-test of ineffective strategy group = 6.81, SD = 2.62; F(1, 202) = 0.14, p = 0.714), while the positive reappraisal group demonstrated significantly higher outcome utility compared to the ineffective strategy group in the post-test (M Post-test of positive reappraisal group = 7.69, SD = 1.90, M Post-test of ineffective strategy group = 6.68, SD = 2.69; F(1, 202) = 9.58, p = 0.002, partial η2 = 0.05) (Figure 3a). (3) Mediation analysis indicated that the reduction of task aversiveness mediated the influence of emotion regulation on the degree of improvement in procrastination (that is, the increase in task executive willingness)(b = 0.44, 95% CI = [0.765, 1.561]) (Figure 4), whereas the increase of outcome utility did not mediate the impact of emotion regulation on the degree of improvement in procrastination (that is, the increase in task executive willingness) (b = 0.06, 95% CI = [− 0.013, 0.367]). These findings suggest that emotion regulation primarily enhances individuals’ task executive willingness by diminishing task aversiveness, consequently mitigating procrastination behavior. This provides a robust theoretical basis for interventions that aim to address procrastination by focusing on emotion regulation. Table and Figures | Reference | Related Articles | Metrics

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Cognitive outsourcing based on generative artificial intelligence: An Analysis of interactive behavioral patterns and cognitive structural features WANG Fancong, TANG Xiaoyu, YU Shengquan Acta Psychologica Sinica    2025, 57 (6): 967-986.   DOI: 10.3724/SP.J.1041.2025.0967 Abstract （706）   HTML （27）    PDF （3482KB）（367）       Knowledge map    Humans can enhance task efficiency and quality by delegating part of their cognitive tasks to generative artificial intelligence (AI), a process referred to as cognitive outsourcing. However, individuals’ effectiveness in using AI varies. To identify the key characteristics and inherent requirements of effective cognitive outsourcing, this study designed a cognitive outsourcing activity for graduate students. Participants wrote articles on open-ended topics with the assistance of a generative AI system and were divided into high-performance and low-performance groups based on their article scores. Differential analysis of knowledge pre-tests revealed that the high-performance group exhibited significantly higher prior domain knowledge compared to the low-performance group. Through lag sequential analysis and epistemic network analysis of interaction process data, differences in interactive behavioral patterns and cognitive structural features between the two groups were identified: participants in the high-performance group demonstrated more diversified behavioral transitions, forming a pattern characterized by “rapid and autonomous task comprehension and planning, efficient and precise human-computer interaction, selective information extraction and deep processing”; the cognitive structure of the high-performance group was balanced and comprehensive, primarily engaging with higher-level cognitive processing, while the low-performance group's cognitive structure was unbalanced and fragmented, primarily engaging with lower-level cognitive processing. In conclusion, effective cognitive outsourcing is a multifaceted process that necessitates active participation and profound cognitive processing. It demands proficient integration between internal cognitive frameworks and external technological tools. Table and Figures | Reference | Related Articles | Metrics

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Automatic processing of facial width-to-height ratio WANG Hailing, CHEN Enguang, LIAN Yujing, LI Jingjing, WANG Liwei Acta Psychologica Sinica    2023, 55 (11): 1745-1761.   DOI: 10.3724/SP.J.1041.2023.01745 Abstract （684）   HTML （26）    PDF （304KB）（239）       Knowledge map    The facial width-to-height ratio (fWHR) is a stable perceptual structure of all faces. It is calculated by dividing the face width (the distance between the left and right zygion) by the face height (the distance between the eyebrow and the upper lip). Previous studies have demonstrated that men's facial width-to-height ratio is a reliable clue to noticing aggressive tendencies and behavior. Individuals with higher fWHR were considered by observers as more aggressive than those with lower fWHR. The researchers proposed that this may be related to facial expression. Observers more readily saw anger in faces with a relatively high fWHR and more readily saw fear in faces with a relatively low fWHR. However, it is unclear what the neural mechanism of fWHR is, particularly in the absence of attention. The present study investigated this issue by recording visual mismatch negativity (vMMN), which indicates automatic processing of visual information under unattended conditions. We hypothesized that faces with high fWHR would elicit a larger vMMN compared to faces with low fWHR. If the above result is related to the fact that high fWHR faces appear angrier and low fWHR faces appear more fearful, then high fWHR faces displaying an angry expression would evoke vMMN and low fWHR faces displaying a fearful expression would evoke vMMN. Participants performed a size-change-detection task on a central cross, while random sequences of faces were presented in the background using a deviant-standard-reverse oddball paradigm. High fWHR faces (deviant stimuli) were presented less frequently among low fWHR faces (standard stimuli), or vice versa. This paradigm allows us to investigate the vMMN induced by the same physical stimulus, as the same stimulus is utilized as both the deviant and the standard stimulus in different blocks, thus reducing the influence of lower-level physical stimulus attributes on ERP components. 41 (19 females, 21.05 ± 1.70 years) and 25 (13 females, 20.56 ± 1.635 years) Chinese participated in Experiment 1 and 2, respectively. In Experiment 1, faces with neutral expressions were used. We employed 2 (fWHR: high vs. low) × 2 (stimuli: deviant vs. standard) within-subject design. The occipital-temporal vMMN (the deviant stimuli elicited more negative responses than the standard stimuli) emerged in the latency range of 200~500 ms for faces with high fWHR (200~250 ms: 4.117 ± 0.591 vs. 4.685 ± 0.582 μV, p < 0.001, 95% CI = [-0.804, -0.331]; 250~300 ms: 3.273 ± 0.562 vs. 4.869 ± 0.553 μV, p < 0.001, 95% CI = [-2.043, -1.150]; 300~350 ms: 2.026 ± 0.532 vs. 3.725 ± 0.510 μV, p < 0.001, 95% CI = [-2.114, -1.284]; 350~400 ms: 2.104 ± 0.483 vs. 3.692 ± 0.443 μV, p < 0.001, 95% CI = [-2.064, -1.113]; 400~450 ms: 1.163 ± 0.463 vs. 2.936 ± 0.431 μV, p < 0.001, 95% CI = [-2.231, -1.316]; 450~500 ms: 0.331 ± 0.449 vs. 2.231 ± 0.434 μV, p < 0.001, 95% CI = [-1.889, -0.752]) and in the latency range of 200~250 ms (4.117 ± 0.591 vs. 4.685 ± 0.582 μV, p < 0.001, 95% CI = [-0.804, -0.331]) and 300~350 ms (2.563 ± 0.648 vs. 3.256 ± 0.588 μV, p = 0.009, 95% CI = [-1.207, -0.179]) for faces with low fWHR (Figure 1). More importantly, faces with high fWHR elicited a higher vMMN than those with low fWHR faces in the 300~350 ms latency range (-1.728 ± 0.242 vs. -0.693 ± 0.254 μV, p = 0.010, 95% CI = [-1.804, -0.266]). In Experiment 2, faces with expressions of fear and anger were used. We employed 2 (fWHR: high vs. low) × 2 (stimuli: deviant vs. standard) × 2 (face expression: angry vs. fearful) within-subject design. Results showed that high-fWHR faces displaying an angry expression elicited a vMMN in the 200~250 ms at P4/PO8 electrode sites (P4: 2.291 ± 0.547 vs. 2.694 ± 0.542 μV, p = 0.039, 95% CI = [-0.784, -0.022]; PO8: 1.298 ± 0.669 vs. 1.966 ± 0.664 μV, p = 0.011, 95% CI = [-1.166, -0.169]) and 300~400 ms latency ranges (300~350 ms: P3: 1.068 ± 0.361 vs. 1.492 ± 0.291 μV, p = 0.009, 95% CI = [-0.731, -0.116]; PO5: 0.689 ± 0.580 vs. 1.097 ± 0.525 μV, p = 0.044, 95% CI = [-0.804, -0.012]; PO8: 0.775 ± 0.636 vs. 1.348 ± 0.702 μV, p = 0.049, 95% CI = [-1.143, -0.002]. 350~400 ms: P3: 0.613 ± 0.307 vs. 0.979 ± 0.229 μV, p = 0.031, 95% CI = [-0.696, -0.036]; PO8: 0.730 ± 0.553 vs. 1.343 ± 0.587 μV, p = 0.035, 95% CI = [-1.180, -0.047]), while low-fWHR faces displaying a fearful expression elicited a vMMN in the 250~400 ms latency range (250~300 ms: 1.484 ± 0.600 vs. 1.911 ± 0.551 μV, p = 0.026, 95% CI = [-0.797, -0.056]; 300~350 ms: 0.239 ± 0.538 vs. 0.820 ± 0.510 μV, p = 0.022, 95% CI = [-1.069, -0.092]; 350~400 ms: 0.657 ± 0.435 vs. 1.109 ± 0.390 μV, p = 0.035, 95% CI = [-0.870, -0.035]), especially in the left hemisphere (Figure 2). To gain a better understanding of the effect of facial expression on the degree of automatic processing in high and low fWHR, we compared vMMN responses to faces with high fWHR presenting neutral and angry expressions, and faces with low fWHR showing neutral and fear expressions (Table 1 and 2). The results revealed that faces with high fWHR displaying an angry expression elicited smaller vMMN than those displaying a neutral expression (300~350 ms at PO5 site: t(64) = -3.654, p = 0.001, Cohen’s d = 0.272, 95% CI = [-2.180, -0.639]; 300~350 ms at PO8 site: t(64) = -3.455, p = 0.001, Cohen’s d = 0.289, 95% CI = [-2.581, -0.690]; 350~400 ms at PO8 site: t(64) = -3.279, p = 0.002, Cohen’s d = 0.305, 95% CI = [-2.538, -0.617]). In conclusion, the present findings suggest that the facial width-to-height ratio is associated with automatic processing and provide new electrophysiological evidence for the different mechanisms underlying high and low fWHR faces under unattended conditions. The automatic processing of high fWHR exhibits greater neural activity than that of low fWHR, which might be related to facial expressions representing facial aggression. Consistent with previous studies, the current finding demonstrates that automatic processing of high and low fWHR is promoted by expressions of anger and fear, respectively. At the same time, due to the automatic processing of facial expressions, the automatic processing of faces with high fWHR is weakened by angry faces relative to neutral faces. Table and Figures | Reference | Related Articles | Metrics

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The impact of product transparency on consumer brand perceptions XU Xiaobing, ZHANG Minshuo, ZENG Shuaifan, FAN Zhuoyi Acta Psychologica Sinica    2023, 55 (10): 1696-1711.   DOI: 10.3724/SP.J.1041.2023.01696 Abstract （681）   HTML （34）    PDF （291KB）（591）       Knowledge map    In the current market, transparent appearance is becoming more popular and is increasingly used in product and packaging design (e.g., transparent frames). However, previous studies have focused on the effects of product transparency (transparent vs. opaque appearance) on food consumption quantity, product evaluation, purchase intention, and consumption decision. In comparison, there is a paucity of understanding of how consumers’ brand perceptions are affected by product transparency and the underlying mechanisms of these effects. In the current research, we extend the extant literature by examining consumer perceptions and attitudes toward brands when faced with the appearance of products with different degrees of transparency. Table and Figures | Reference | Related Articles | Metrics

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Traditional pettism: The influence of pet ownership status, pet type, and pet properties on pet moral standing XU Kepeng, OU Qianqian, XUE Hong, LUO Dongli, ZHANG Shuyue, XU Yan Acta Psychologica Sinica    2023, 55 (10): 1662-1676.   DOI: 10.3724/SP.J.1041.2023.01662 Abstract （676）   HTML （15）    PDF （444KB）（2213）       Knowledge map    The bond between humans and their pets is becoming ever closer, and the ethical status of pets is also evolving. This article examines how pet owner identity, pet type, and pet properties affect pet moral standing through three studies by dividing pets into traditional and non-traditional categories. The results showed that : (1) Traditional pets are seen as having a higher moral standing than non-traditional pets, which is explained by agency, sensitivity, and harmfulness. (2) Traditional pet owners consider traditional pets to have a higher moral standing than non-traditional pet owners, though there is no major difference in the moral standing of non-traditional pets between the two. (3) Animal Empathy was identified as a mediator between traditional pet attachment and traditional pet moral standing. These findings suggest that pets are viewed differently in terms of morality, which is manifested in traditional petism; and the relationship between pet owners and their pets is a major factor in promoting it. Table and Figures | Reference | Related Articles | Metrics

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Perceived opacity leads to algorithm aversion in the workplace ZHAO Yijun, XU Liying, YU Feng, JIN Wanglong Acta Psychologica Sinica    2024, 56 (4): 497-514.   DOI: 10.3724/SP.J.1041.2024.00497 Abstract （670）            Knowledge map    With algorithms standing out and influencing every aspect of human society, people's attitudes toward algorithmic invasion have become a vital topic to be discussed. Recently, algorithms as alternatives and enhancements to human decision-making have become ubiquitously applied in the workplace. Despite algorithms offering numerous advantages, such as vast data storage and anti-interference performance, previous research has found that people tend to reject algorithmic agents across different applications. Especially in the realm of human resources, the increasing utilization of algorithms forces us to focus on users' attitudes. Thus, the present study aimed to explore public attitudes toward algorithmic decision-making and probe the underlying mechanism and potential boundary conditions behind the possible difference. To verify our research hypotheses, four experiments (N = 1211) were conducted, which involved various kinds of human resource decisions in the daily workplace, including resume screening, recruitment and hiring, allocation of bonuses, and performance assessment. Experiment 1 used a single-factor, two-level, between-subjects design. 303 participants were randomly assigned to two conditions (agent of decision-making: human versus algorithm) and measured their permissibility, liking, and willingness to utilize the agent. Experiment 1 was designed to be consistent with Experiment 2. The only difference was an additional measurement of perceived transparency to test the mediating role. Experiment 3 aimed to establish a causal chain between the mediator and dependent variables by manipulating the perceived transparency of the algorithm. In Experiment 4, a single-factor three-level between-subjects design (non-anthropomorphism algorithm versus anthropomorphism algorithm versus human) was utilized to explore the boundary condition of this effect. As anticipated, the present research revealed a pervasive algorithmic aversion across diverse organizational settings. Specifically, we conceptualized algorithm aversion as a tripartite framework encompassing cognitive, affective, and behavioral dimensions. We found that compared with human managers, participants demonstrated significantly lower permissibility (Experiments: 1, 2, and 4), liking (Experiments: 1, 2, and 4), and willingness to utilize (Experiment 2) algorithmic management. And the robustness of this result was demonstrated by the diversity of our scenarios and samples. Additionally, this research discovered perceived transparency as an interpretation mechanism explaining participants' psychological reactions to different decision-making agents. That is to say, participants were opposed to algorithmic management because they thought its decision processes were more incomprehensible and inaccessible than humans (noted in Experiment 2). Addressing this “black box” phenomenon, Experiment 3 showed that providing more information and principles about algorithmic management positively influenced participants' attitudes. Crucially, the result also demonstrated the moderating effect of anthropomorphism. The result showed that participants exhibited greater permissibility and liking for the algorithm with human-like characteristics, such as a human-like name and communication style, over more than a mechanized form of the algorithm. This observation underlined the potential of anthropomorphism to ameliorate resistance to algorithmic management. These results bridge the gap between algorithmic aversion and decision transparency from the social-psychological perspective. Firstly, the present research establishes a three-dimensional (cognitive, affective, and behavioral) dual-perspective (employee and employer) model to elucidate the negative responses toward algorithmic management. Secondly, it reveals that perceived opacity acts as an obstacle to embracing algorithmic decision-making. This finding lays the theoretical foundation of Explainable Artificial Intelligence (XAI) which is conceptualized as a “glass box”. Ultimately, the study highlights the moderating effect of anthropomorphism on algorithmic aversion. This suggests that anthropomorphizing algorithms could be a feasible approach to facilitate the integration of intelligent management systems. Related Articles | Metrics

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Personality subtypes of depressive disorders and their functional connectivity basis LI Yu, WEI Dongtao, QIU Jiang Acta Psychologica Sinica    2023, 55 (5): 740-751.   DOI: 10.3724/SP.J.1041.2023.00740 Abstract （631）   HTML （47）    PDF （403KB）（231）       Knowledge map    Heterogeneity among mental health issues has always attracted considerable attention, thereby restricting research on mental health and cognitive neuroscience. Additionally, the person-centred approach to personality research, which emphasizes population heterogeneity, has received more attention. On the other hand, the heterogeneity among depressive patients has been a problem that cannot be ignored (most studies ignored the actual situation and directly assumed sample homogeneity). A large number of empirical studies have provided evidence that isolated personality traits are often associated with depression. Only a few studies have considered the probable effect from a taxonomy perspective. Moreover, the neural mechanisms of personality types in depression remain unclear. This study aimed to reveal different personality subtypes of depressive disorders and elucidate subtypes from the perspective of resting-state functional connectivity. Personality and resting-state functional imaging data of 159 depressive patients and 156 controls were collected. Demographic characteristics are shown in Table 1. First, combined with “depression diagnosis”, the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected the amygdala, hippocampus, insula (AAL atlas) and limbic network, default network, and control network (Schaefer-Yeo template) as regions of interest and calculated the functional connectivity of the subcortical regions to the networks. ANOVA was used to compare resting-state functional connectivity between the personality types. The results showed the following. (1) Depression was more common among individuals with high neuroticism and low extraversion tendencies, but there were also individuals with low neuroticism and high extraversion tendencies. The controls were more likely to be individuals with low neuroticism and high extraversion (see Figure 1). (2) The results of resting-state functional connectivity showed no significant difference between depression and controls. (3) The functional connectivity strength of the left amygdala-limbic network (F(6, 214) = 4.273, p = 0.0004, threshold-controlling FDR at 0.05/6) and left insula-limbic network (F(6, 214) = 4.177, p = 0.0005, threshold-controlling FDR at 0.05/6) was significantly different across personality subtypes. The post-hoc tests are presented in Table 2, Figure 2 and Figure3. In summary, the personality subtypes of depression identified by person-centred perspectives are more in line with reality and individual cognitive patterns, and they have potential clinical adaptive value. The findings of this study enhance the understanding of heterogeneity among depressive disorders. Table and Figures | Reference | Related Articles | Metrics

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The relationship between gratitude and social well-being: Evidence from a longitudinal study and a daily diary investigation YE Ying, ZHANG Linting, ZHAO Jingjing, KONG Feng Acta Psychologica Sinica    2023, 55 (7): 1087-1098.   DOI: 10.3724/SP.J.1041.2023.01087 Abstract （631）   HTML （28）    PDF （489KB）（453）       Knowledge map    The positive psychological construct of gratitude is crucial for health and well-being. Previous studies have shown a significant positive correlation between gratitude and social well-being. However, to the best of our knowledge, no studies have examined this potentially reciprocal relationship from a longitudinal perspective. According to the broaden-and-build theory and gratitude amplification theory, we hypothesized that gratitude has a predictive effect on social well-being. In addition, based on the personality and social relationships model and self-determination theory, we proposed that social well-being is an antecedent to gratitude. In summary, this research combines a longitudinal study and a daily diary investigation to systematically explore the causal relation between gratitude and social well-being. Study 1 employs a two-wave cross-lagged design to explore the long-term relationship between trait gratitude and social well-being. The sample comprised 563 undergraduate students, who all participated online. Pursuant to the study purpose, participants were asked to complete the gratitude and social well-being scales twice, separated by a seven-month interval. The cross-lagged path analysis suggested reciprocal effects between trait gratitude and social well-being. To reduce recall bias and explore the short-term association between gratitude and social well-being, Study 2 employs a daily diary method. A total of 274 young adults completed daily gratitude and social well-being measures for 21 consecutive days. In Study 1, trait gratitude at T1 significantly positively predicted social well-being at T2, while social well-being at T1 also significantly predicted trait gratitude at T2. These effects remained significant after controlling for age and gender. Consistent with Study 1, Study 2 also revealed a reciprocal relationship: state gratitude on one day positively predicted social well-being the next day, while social well-being on one day also positively predicted state gratitude the next day. Moreover, these relationships were stable after controlling for time trends. Overall, the results of Study 1 and Study 2 support the hypotheses by showing reciprocal predictive effects between gratitude and social well-being. In summary, we predicted that experiencing gratitude would lead to higher social well-being, which would, in turn, result in higher gratitude, activating an upward spiral. This work deepens understanding of the interaction between gratitude and social well-being, paving the way for future intervention research to help increase both. Table and Figures | Reference | Related Articles | Metrics

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Effects of memory conversation and group identity on collective memory GUO Qianlin, GUAN Jian Acta Psychologica Sinica    2023, 55 (6): 1016-1028.   DOI: 10.3724/SP.J.1041.2023.01016 Abstract （630）   HTML （33）    PDF （334KB）（250）       Knowledge map    Collective memory often appears in everyday conversations. Communicating with others about what happened to the past inevitably affects our collective memory by social context. In recent years, several studies have believed that the formation of collective memory depends on a dynamic system of communication, but few research has started from dialogue and interaction backgrounds and used empirical methods to explore factors that may affect collective memory at the group level. On the basis of existing studies, this research thus explores factors that may influence collective memory in an interactive way (i.e., memory conversation). It investigates how memory outcomes under this framework are affected by the interaction social context, which includes memory conversation, information matching, and group identity. First, the effect of conversation on collective memory was investigated. Specifically, Experiment 1 explored the effect of activating information content through memory conversation and adopted a single factor (activation mode: conversation vs. individual) pretest-posttest experimental design (Figure 1). Second, the effect of information matching on collective memory was determined. Experiment 2 adopted a single factor (information matching: matching vs. mismatching) pretest-posttest experimental design. Third, the effect of group identity on collective memory in memory conversation was investigated. Experiment 3 adopted a single factor (group identity: in-group vs. out-group) pretest-posttest experimental design (Figure 2). The results of the three experiments are as follows: (1) Conversation improved participants’ free recall scores at the individual level, whereas the nominal group did not. At the collective level, the conversation did not affect the group’s collective memory convergence. However, the conversation reduced the number of items forgotten by participants in common, F(1, 19) = 6.55, p = 0.019 (Table 1). (2) Given matching information on memory conversation, individuals can perform better in free recall. Information matching does not affect collective memory convergence, but information matching can increase the number of items remembered in common among participants, F(1, 13) = 13.60, p = 0.003 (Table 2). (3) When inconsistent information comes from the outgroup, individuals’ free recall performance is better. Only if the inconsistent information comes from the outgroup can make the collective memory among participants have more parts to remember in common, F(1, 13) = 25.37, p < 0.001. Meanwhile, inconsistent information from the ingroup and outgroup did not affect the number of items forgotten by participants in common (Table 3 & Figure 3). In conclusion, these findings have important implications for understanding the mechanism underlying the effects of memory conversation and group identity on collective memory. Moreover, the function of communication is not to make the memory content close to facts, but to abstract our memories and remember things effectively. Our findings confirm that collective memory is not only recognized as an individual psychological phenomenon but also likely involves a kind of social property. Therefore, given the significant role of communication with collective remembering, placing people in a conversational background is a direct way to investigate collective memory. Table and Figures | Reference | Related Articles | Metrics

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The Influence Relationship among Variables and Types of Multiple Influence Factors Working Together WEN Zhonglin, WANG Yifan, MA Peng, MENG Jin, LIU Xiqin Acta Psychologica Sinica    2024, 56 (10): 1462-1470.   DOI: 10.3724/SP.J.1041.2024.01462 Abstract （626）   HTML （26）    PDF （223KB）（341）       Knowledge map    The investigation of relationships among variables is the main focus of empirical research in psychology and other social science disciplines. Many empirical studies based on questionnaire surveys involve the influence relationship between variables. However, the lack of a universally accepted definition for this concept has led to ambiguity, and it is often conflated with causal or correlational relationships, which may cause problems for studies on mediating effects. This article defines the influence relationship as a directional correlation, clarifying relationships among correlation, influence and causation in terms of denotation and connotation. We propose several ways to identify evidence for modeling the influence relationship. Our discussion involves the joint effects of multiple factors influencing a dependent variable. The paper provides theoretical support for studying relationships between variables in questionnaire research. Table and Figures | Reference | Related Articles | Metrics

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The application of artificial intelligence methods in examining elementary school students’ academic cheating on homework and its key predictors ZHAO Li, ZHENG Yi, ZHAO Junbang, ZHANG Rui, FANG fang, FU Genyue, LEE Kang Acta Psychologica Sinica    2024, 56 (2): 239-254.   DOI: 10.3724/SP.J.1041.2024.00239 Abstract （625）   HTML （33）    PDF （478KB）（297）       Knowledge map    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. Despite 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 advanced artificial intelligence methods to investigate the development of homework cheating in elementary school children and the key contributing factors so as to provide the 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 to 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 (see Figure 1). 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% (see Figure 2). 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 cheating (10.49%), how commonly and frequently they had observed their peers engaging in academic cheating (3.83%), and their achievement level (3.26%). Other predictors (1%-2%), such as children’s beliefs about the severity of the possible consequences of cheating (e.g., being punished by one’s teacher), whether cheating is considered acceptable by peers in general and demographic characteristics, though significantly, were not important predictors of elementary school children’s homework cheating (see Figure 3 for details). 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 form 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. Table and Figures | Reference | Related Articles | Metrics