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Conceptual Framework

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The impact of trust in technology and trust in leadership on the adoption of new technology from employee's perspective XU Yi, LIU Yixuan 2021, 29 (10):  1711-1723.  doi: 10.3724/SP.J.1042.2021.01711 Abstract ( 1842 )   HTML ( 183 )   PDF (839KB) ( 2927 )   In today's knowledge-based economy, new technology adoption is crucial for companies to increase their core competitiveness. The success of new technology diffusion in enterprises depends on employees' trust in new technologies to overcome risks and uncertainties surrounding new technology. Although many scholars recognize the positive role of trust in the adoption of new technology, the extant literature lacks empirical evidence and theoretical underpinning. To fill the gap, we aim to explore how trust in technology and leadership affects employees' decision to adopt new technology through four different studies. Further, we introduce perceived risk and technology self-efficacy as mediators to explore the underlying mechanisms. In study 1, we validate the measurement of trust in technology with functionality, reliability, and helpfulness as three antecedent factors. The positive effects of trust in technology and trust in leadership on employees' new technology adoption are examined through an experiment. In study 2, we collect employees' data within organizations. Perceived risk and technology usefulness are added into the model to explain the relationship between trust in technology, trust in leadership, and new technology adoption from the employees' perspective. In study 3, we first refine the measurement of technology self-efficacy. Then, through an experiment, we manipulate the trust in technology (High, Low, Control) and measure employees' technology self-efficacy and new technology adoption. We propose that trust in technology can increase technological self-efficacy and further facilitate employees' new technology adoption. In study 4, we examine the contextual effects of industries' backgrounds and organizational cultures. We suggest that in high-tech industries, trust in technology could have a greater effect on employees' adoption of new technology. In addition, organizational culture could moderate the effects of trust. In particular, collectivistic organizational culture could moderate the effects of trust in leadership, while individualistic organizational culture could moderate the effects of trust in technology. Overall, the current research constructs a theoretical model and extends our understanding of employees' adoption of new technology. First, it investigates factors that affect new technology adoption from the employees' perspective with consideration of both individual and contextual factors. We propose employees' technological self-efficacy as individual differences and organizational culture and industry background as contextual factors that equally matters. Second, the current study clearly identifies trust in new technological adoption processes in organizations, as both trust in technology and trust in leadership. We further analyze the effects of the two types of trust and the underlying mechanisms. This enriches the literature of the influence of trust and its mechanism in new technology adoption. Third, we suggest that technological self-efficacy can explain the mechanism of trust in technology, which could lay the foundation for future research of trust in technology. Lastly, this study has managerial implications. Based on our findings, effective management strategies can be implemented to support new technologies integration. Figures and Tables | References | Related Articles | Metrics

Research Method

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Psychopathological network theory, methods and challenges CHEN Chen, WANG Li, CAO Chengqi, LI Gen 2021, 29 (10):  1724-1739.  doi: 10.3724/SP.J.1042.2021.01724 Abstract ( 2579 )   HTML ( 198 )   PDF (3075KB) ( 3800 )   As for the conceptualization of mental disorders, the traditional DSM-ICD classification diagnostic system, i.e., Diagnostic and Statistical Manual of Mental Disorders(DSM) and International Classification of Diseases(ICD), as well as the Research Domain Criteria (RDoC) proposed by National Institute of Mental Health(NIMH) are both based on the latent variable perspective, assuming that the symptoms of mental disorders have an underlying common cause(the disorder entity or dysfunction in different potential dimensions). However, such latent variable perspective requires local independency between variables, thus both views ignore the interaction between symptoms. In 2008, Borsboom put forward the psychopathological network theory, a new perspective different from the categorical and dimensional views of the conceptualization of mental disorders. This theory focuses on the interaction between symptoms, assuming that mental disorders are directly composed of symptoms and dynamic causal relationship between them. Based on this theory, network methods mainly estimate the partial correlation network of symptoms using the glasso algorithm with EBIC, and examine the different characteristics of mental disorder symptoms using indicators such as node centrality and network connectivity. In recent years, many new network models have emerged, such as Bayesian networks and relative importance networks that can perform causality inferences. With the increasing number of studies that applied psychopathological network theory and methods, this theory and method has clearly become one of the mainstream research theories and methods in the field of current mental health and psychopathological and psychometrics related research. But at the same time, researchers also found some remaining challenges for psychopathological network methods with respect to causality inference of symptoms, identification of central symptoms, and also reliability and replicability of network structures. Accordingly, this review briefly introduced the core idea and basic principles of psychopathology network theory, as well as the most commonly used psychopathology network analysis methods so far, and summarized important applications and values of psychopathology network theory and methods, then synthesized the main challenges that psychopathological network analysis method were currently facing. Finally, corresponding possible solutions were proposed. After reviewing a wide range of related publishments in theories, methods, and empirical since psychopathology network theory was put forward, we provided unique insights into the possible agendas for future research on psychopathological network methods, hoping the challenges and progress in the methodology could also bring new opportunities for the further improvement of psychopathological network theory. Figures and Tables | References | Related Articles | Metrics

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Application of electrical brain stimulation in the auditory language processing MA Minxuan, LI Wenjie, QIN Mengling, WEI Yaohong, TAN Qianbao, SHEN Lu, CHEN Qi, HAN Biao 2021, 29 (10):  1740-1754.  doi: 10.3724/SP.J.1042.2021.01740 Abstract ( 1367 )   HTML ( 71 )   PDF (2196KB) ( 1540 )   Auditory language comprehension plays an important role in interpersonal communication in daily life, however, we still do not fully understand its underlying neural mechanisms. Electrical brain stimulation (EBS) is an experimental technique with a very long history but has only recently been widely used on human subjects. By performing electrical stimulation, analyzing the induced transient behavioral functional changes and recording the neural activity, it is possible to directly reveal the functional roles within brain regions and the effective connections between brain areas during auditory language processing. Electrical brain stimulation offers a very high spatial and temporal resolution and employs recording electrodes that can reach deep into subcortical areas. Given these unique advantages, electrical brain stimulation has received increasing research interest in recent years. Auditory language processing is a fairly complex process and involves a wide range of brain areas. In general, the process of auditory language processing in the brain is as follows: incoming speech from the external environment enters the thalamus, which then passes to the auditory cortex (AC) for primary processing of acoustic-phonological information, followed by more advanced language processing in the temporal and frontal language areas. In addition, frontal language areas may also generate speech-related predictions that feedback to temporal language areas to facilitate auditory language processing. Electrical brain stimulation allows relatively flexible cortical or subcortical stimulation in subjects who were performing an auditory language task. By comparing the differences in task performance before and after electrical stimulation, the relationship between stimulated brain areas and cognitive function could be analyzed and thus the distribution of functionally relevant areas could be mapped. Besides, electrical brain stimulation, as a means to reflect effective connections between brain areas, can also reveal the functional connections during auditory language processing. Therefore, this paper, from the perspective of auditory language processing, is divided into three parts: thalamus and auditory cortex, auditory language processing within auditory cortex, and higher language cortex and auditory cortex. By reviewing the available studies on electrical brain stimulation during auditory language processing, the functional characteristics of the brain areas involved in auditory language processing and the information transfer mechanisms between different brain areas are summarized, providing a new perspective for further exploring the mechanisms of auditory language processing and the application of electrical brain stimulation techniques in the study of brain function. Electrical brain stimulation has broad application prospects in auditory language research, and the increased application of this technique will also bring more causal evidence on the brain function and connectivity, providing the possibility of further understanding the neural mechanisms of auditory language processing. Figures and Tables | References | Related Articles | Metrics

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A unification and extension on the multivariate longitudinal models: Examining reciprocal effect and growth trajectory LIU Yuan 2021, 29 (10):  1755-1772.  doi: 10.3724/SP.J.1042.2021.01755 Abstract ( 1603 )   HTML ( 83 )   PDF (1509KB) ( 2526 )   When conducting the multivariate longitudinal studies, reciprocal relationship and latent trajectory are two of the focusing issues. The reciprocal relationship is often examined by a cross-lagged model that could build autoregressive influence and the multivariate influence between target variables, while the latent trajectory is usually defined by a latent growth model that explores the growth pattern simultaneously with individual difference. These two kinds of models are easily built under the SEM framework, at the same time could be flexibly combined by other research questions, such as the measurement error, the random factor, as well as the combination of the above issues. Such a combination yields a more complex model definition exploring the longitudinal relations, such as factor cross-lagged model, random-intercept cross-lagged model, trait-state-error model, autoregressive trajectory model, latent change score model, etc. In the study, we built a unified framework to analyze the above series of models according to the variance decomposition. First, the between-person difference was built by the latent trajectory often modeled as the latent growth. Second, the within-person difference was further decomposed as the within-person carry-over and the reciprocal relations between variables, which is the key question in the cross-lagged model series. Finally, the measurement error could be added to increase the measuring accuracy, where the trait-state-error model usually answers such a question. Since the research question of interest could be easily drawn from any above components, in summary, a “factor latent curve model with structured reciprocals” model was built as an extension and unified framework including all the components discussed above. We also used an empirical dataset to compare the above models. The data was driven from the Early Childhood Longitudinal Survey-Kindergarten (ECLS-K) project. There were 21,049 participants selected from 6 waves of measures from kindergarten to Grade 8. Reading and mathematics abilities IRT scores were used calibrated on the same scale. We first decided on the shape of the growth trajectory, where a series of alternative models indicated that the piecewise growth model best fit the data. Followed, longitudinal models suggested in our unified framework were adopted, i.e., (random intercept) cross-lagged model, trait-state-error model, latent growth model, (latent variable) autoregressive latent trajectory model, as well as (factor) latent curve model with structured residuals/reciprocals. Results indicated that the trait-state-error model best described the data. It showed that after controlling for the between-person difference (the trait factor—reading and mathematics ability), individually carry-over effects were significantly influential typically for students in the early elementary years. The significant reciprocal effect between reading and mathematics was also obtained showing these two domains of subjects influenced one another. Finally, we summarized how the results could be interpreted and offered suggestions on model selection for the researchers. Figures and Tables | References | Related Articles | Metrics

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Random intercept latent transition analysis (RI-LTA): Separating the between-subject variation from the within-subject variation WEN Congcong, ZHU Hong 2021, 29 (10):  1773-1782.  doi: 10.3724/SP.J.1042.2021.01773 Abstract ( 2294 )   HTML ( 82 )   PDF (745KB) ( 2098 )   Traditional latent transition analysis (LTA) is usually done using single-level modeling, but can also be viewed as a two-level modeling from a multi-level perspective. In 2020, Muthén and Asparouhov proposed a so-called random intercept latent transition analysis (RI-LTA) model which separates between-subject variation from within-subject variation. By integrating a random intercept factor, latent class transitions are represented on the within level, whereas the between level captures the variability across subjects. The random intercept factor f is the most important. If the factor loadings on the random intercept factor are large, this indicates that the item probabilities are large and thus the cases have large differences on these items. From this perspective, RI-LTA can be viewed as absorbing the measurement non-invariance of the model. Due to large item differences, the different latent classes are easy to distinguish. These differences are absorbed by the random intercept factor but are not set to influence the latent class variables. Therefore, the off-diagonal values of the transition probability matrix are larger. In traditional LTA, large differences across classes are not absorbed by the random intercept factor, which leads to smaller off-diagonal but larger diagonal values of the transition probability matrix. Performing RI-LTA in Mplus software can be done in three to four steps. First, implementing LCA across different time points; second, implementing traditional LTA and RI-LTA; third, saving the parameter estimates obtained in the second step and using them as population values to do a Monte Carlo simulation study; fourth, in the event of previous knowledge or existing applications, one may include covariates or distal outcomes in the model. Researchers can also perform multiple-group analysis, Markov chain mover-stayer analysis, multi-level RI-LTA, or longitudinal factor analysis to have deeper insight into the data. In the current study, a two-wave longitudinal data collection from undergraduates attending in the year 2016 at a research-oriented university was used to demonstrate how to implement RI-LTA in Mplus. The first three steps used were as described in the previous paragraph. For the fourth step, we performed a multiple-group analysis and investigated the interaction effects by including a “type of university enrolment” covariate. Results showed that students of the class labeled “strong intrinsic and extrinsic motivation” class tended to switch to “strong intrinsic motivation but low extrinsic motivation” class and “low intrinsic and extrinsic motivation” class at a 33.0% transition probability of staying in the original class with RI-LTA analysis, while these students tended to stay in the original class at a 68.9% staying transition probability with traditional LTA analysis. This indicated that RI-LTA avoided overestimation on the transition probabilities of students staying in the original class and allowed for clearer interpretation of the data. The RI-LTA model was shown to be better than the traditional LTA model in this situation. By including a “type of university enrolment” covariate, the multiple-group analysis indicated that measurement invariance should be established. Most of the regression coefficients of latent classes on covariate were not significant except c1#1 on dummy2, which was significant at a value of -2.364. This indicated that students who were enrolled via the independent admission examinations and endorsed the “low intrinsic and extrinsic motivation” class were fewer than the recommended students We also found that the interaction effects of the covariate and c1 on c2 were not significant. Thus, a more parsimonious measurement invariant multiple-group analysis including a covariate but without interaction effect model should be chosen. Future research could use Monte Carlo simulation studies to investigate the applicability of RI-LTA, for example by manipulating sample sizes, numbers of indicators, latent classes, and time points. Inspired by multi-level modeling, the implementation of multi-level RI-LTA in statistical software should also be explored further. Figures and Tables | References | Related Articles | Metrics

Meta-Analysis

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The neural mechanism of self-face recognition: An ALE meta-analysis of fMRI studies NA Yuting, ZHAO Yuwen, GUAN Lili 2021, 29 (10):  1783-1795.  doi: 10.3724/SP.J.1042.2021.01783 Abstract ( 1594 )   HTML ( 97 )   PDF (1061KB) ( 1983 )   Self-face recognition reflects the process whereby someone can recognize their own face by distinguishing it from the other. Generally, people recognize self-faces faster than they do other faces, and self-face recognition can elicit enhanced brain activity compared with that of other face recognitions. Researching self-face recognition is valuable because of its close relationship with self-awareness. Recently, many studies used functional magnetic resonance imaging (fMRI) to investigate the neural basis of self-face recognition. However, there are no consistent results regarding the key brain regions involved in self-face recognition. Therefore, in the current study, a quantitative meta-analysis of fMRI studies, using activation likelihood estimation (ALE), was performed to localize the neural structures engaged in recognizing self-face. Twenty-seven studies involving 635 participants met the inclusion criteria. The meta-analysis was conducted in the standard Montreal Neurological Institute (MNI) space, and we translated results reported using Talairach coordinates into MNI coordinates. The statistical analysis of the transformed foci was validated using the Monte Carlo Simulation (1,000 permutations) with a cluster-forming voxel-level threshold at uncorrected p < 0.001 combined with cluster-size correction using family-wise error at p < 0.05. We used Mango software to project the activation coordinates onto a brain template to provide a visual representation of activation distributions. Results showed that the contrast of self-face versus other-face displayed increased activations of the right superior parietal lobule/precuneus/middle occipital gyrus, middle frontal gyrus, inferior frontal gyrus, fusiform gyrus, postcentral gyrus, insula, and left precuneus. There was no active region in the contrast of other-face versus self-face. Based on the meta-analysis results and on previous event-related potential (ERP) studies, self-face recognition may involve two levels of processing, perceptual integration processing and the accompanying process of evaluation and emotional response. In the process of recognizing self-face, the occipital gyrus, fusiform gyrus, and precuneus are involved in the perceptual integration process. The occipital cortices may be involved in the processing of self-related facial features in the early stages of face recognition. The fusiform gyrus is involved in low-level sensory processing, and it is also sensitive to the categorization of faces in terms of self versus nonself. The precuneus is recruited in the perceptual integration of self-related information. The superior parietal lobule, middle frontal gyrus, inferior frontal gyrus, and insula are mainly recruited in the evaluation and the emotional response at the middle and late stages of recognizing self-face. The superior parietal lobule and middle frontal gyrus have been shown to play an important role in the processing of evaluating self-face. Moreover, their activations reflect the influence of social and cultural factors on self-face recognition. The inferior frontal gyrus and insula are also involved in the processing of evaluating self-face. Furthermore, they play a direct role in the subjective emotional experience of viewing or evaluating self-face. In sum, the current meta-analysis reveals the neural basis of self-face recognition and suggests two levels of processing of self-face recognition (perceptual integration processing and the accompanying process of evaluation and emotional response). The current study provides support for investigating the neural mechanism of self-face recognition and, based on the limitations of previous studies, makes suggestions for future research. Future studies could use magnetoencephalography (MEG) or simultaneous EEG-fMRI to combine brain location and time course, thereby revealing the cognitive and neural mechanisms of self-face recognition. Close attention should be paid to the structural and functional connectivity of brain areas and brain networks and to the neural correlates of interoception and self-face recognition. Clinical studies should investigate abnormal neural activity in patients with self-processing impairment and explore the influence of threatening information on self-face recognition. Figures and Tables | References | Related Articles | Metrics

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The relationship between narcissism and BIS/BAS: A meta-analysis CHENG Hao, ZHANG Yali, YAO Xue, ZHANG Xiangkui 2021, 29 (10):  1796-1807.  doi: 10.3724/SP.J.1042.2021.01796 Abstract ( 1947 )   HTML ( 195 )   PDF (873KB) ( 2965 )   Since the 1980s, narcissism has become more and more popular in research. Narcissism may be important for developing self-consciousness and guiding the future. Narcissism is regarded as an aspect of the evolution of human psychological adaptation, but the specific physical, psychological and social reasons of narcissism have yet to be determined. Temperament is the basis for the formation and development of personality, and an important susceptibility factor for the formation of narcissistic personality. Campbell's agency model connects narcissism with the level of approach and avoidance behaviors, and believes that narcissism as a self-regulating behavioral system, with a clear focus on motivation to approach rewarding stimuli. Gray divides personality traits into two basic, separate and brain-motivation systems responsible for behavior regulation: the aversive and the appetitive motivation systems. The aversive motivation system is called the behavior inhibition system. This system is responsible for controlling anxiety caused by specific stimuli. The behavioral activation system is associated with dopaminergic function and controls desire motivation. The system is sensitive to positive signals, reward, and avoidance of punishment, and its activation can regulate targeted behaviors. Gray's Reinforcement Sensitivity Theory believes that the Behavioral Approach System represents approaching expected reward behavior, and the Behavioral Inhibition System represents anxiety, behaviors that are activated by approaching avoidance conflict and are related to memory, environment, and risk. Numerous studies have explored the relationship between narcissism and BIS/BAS. Previous studies have found that the correlation between narcissism and BIS/BAS is not completely consistent. Due to the input errors in some data and the reliability of the scale has not been corrected, which may affect the authenticity and reliability of the results. In addition, in the analysis process, the quality of the literature, the single homogeneity test method, and the publication bias of the literature were not considered. Our study uses a more comprehensive and accurate meta-analysis method to analyze the relationship between narcissism and BIS/BAS, and explores whether there are moderated variables that lead to inconsistencies in the experimental results, avoiding the bias of a single study restricted by the sample size, and obtaining more general, more precise conclusions. Through literature retrieval, 25 independent effect sizes together with 7702 participants which met the inclusion criteria of meta-analysis were selected. Homogeneity test indicated that random effects model was appropriate for the meta-analysis. The results of funnel plot and Egger's intercept illustrated no publication bias. Main-effect test indicated a significant negative correlation between narcissism and BIS (r = -0.27, 95% CI = [-0.34, -0.21]). Further moderation analysis revealed that the association between narcissism and BIS was moderated by measurement tools of narcissism; Main-effect test indicated a significant positive correlation between narcissism and BAS (r = 0.46, 95% CI = [0.40, 0.52]). Further moderation analysis revealed that the association between narcissism and BAS was moderated by participant types. The results supported the agency model of narcissism and the revised reinforcement sensitivity theory of personality. Figures and Tables | References | Related Articles | Metrics

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Effects of mindfulness-based interventions on self-compassion: A meta-analysis SUN Tengwei, YAN Yuxian, JIN Yuchang, AN Junxiu 2021, 29 (10):  1808-1828.  doi: 10.3724/SP.J.1042.2021.01808 Abstract ( 3170 )   HTML ( 261 )   PDF (2465KB) ( 4776 )   Both mindfulness and self-compassion have positive effects on mental health, and the relationship between them has always been the focus of researchers. And mindfulness and self-compassion are also closely related. Previous research has found that mindfulness can predict an individual's level of self-compassion, a component of which also includes mindfulness. However, different studies used the same two variables to measure, and the correlation coefficient between mindfulness and self-compassion was significantly different. Therefore, it is necessary to explore the reasons for the large correlation coefficient difference between mindfulness and self-compassion. In this study, meta-analysis method was adopted, combined with empirical research results from global research, to deeply explore the relationship between mindfulness-based interventions and self-compassion in different groups and different contrast ways, as well as the factors that affect the relationship between them. Through a comprehensive search of Chinese and English literature up to November 2020, a total of 65 literature were finally included in this meta-analysis, including 62 English literature and 3 Chinese literature, and 99 independent effects were obtained, with a total sample size of 8103. In addition, 22 articles were included in the literature using horizontal comparison, 42 articles using longitudinal comparison; 16 articles using follow-up comparison. This paper mainly used Pearson correlation coefficient r as the effect value, and the effect value of the literature was coded as an effect value for each independent sample. If a paper reported multiple independent samples at the same time, the effect value would be coded separately to generate multiple independent effect sizes. After testing the publication bias through funnel plot, Rosenthals Classic Fail-safe N test, and Egger's test, it is found that there was no publication bias. After the heterogeneous test, the article also has high heterogeneity, so the analysis after the random effect model is selected. Sensitivity analysis showed that the degree of heterogeneity was effectively reduced after using One-study removed to gradually delete the research with higher heterogeneity, but regardless of the degree of heterogeneity, the intervention based on mindfulness was positively correlated with self-sympathy. This study discussed the moderating effects of different contrast methods (horizontal comparison, longitudinal comparison, follow-up comparison), sample groups (students, health care related workers, social related workers), and measurement tools (SCS, SCS-SF) on the relationship between mindfulness and self-compassion. The subgroup test showed that the relationship between mindfulness-based intervention and self-compassion was influenced by the moderating effect of contrast style, but not by the sample group or measurement tool. The results showed that the mindfulness-based intervention had a significant positive correlation with self-compassion, and the mindfulness-based intervention had a positive impact on the level of self-compassion. The relationship between the mindfulness-based intervention and self-compassion was affected by the way of measurement comparison, but did not have a significant moderating relationship with the group and the measurement tool. The reason why there is such a big difference among many domestic and foreign studies is probably due to the difference of measurement methods and the difference caused by the in unity of measurement methods. Overall, in many experimental studies on the relationship between mindfulness and self-compassion, no matter which contrast method is used for measurement, the individual's level of self-compassion has increased after mindfulness-based intervention. Figures and Tables | References | Related Articles | Metrics

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Paternalistic leadership and employee performance: A meta-analysis of Chinese samples LIU Doudou, XU Yan, LI Chaoping 2021, 29 (10):  1829-1846.  doi: 10.3724/SP.J.1042.2021.01829 Abstract ( 1683 )   HTML ( 111 )   PDF (1315KB) ( 2400 )   As a paradigm of local research, paternalistic leadership has gradually been popular in the field of Organizational Behavior. Paternalistic leadership is a leadership style that expresses absolute authority, elder's care, and moral role model in the context of humanism. As a core element in organizational context, employee performance is crucial for organizations, leaders, and employees, etc. However, the conclusion of the relationship between paternalistic leadership and employee performance is still controversial, as existing meta-analyses focus on the relationship between this relationship, but relatively ignore the important impact of cultural on the results; besides, most scholars have analyzed the authoritarian, benevolent, and moral dimensions of paternalistic leadership independently or parallelly, failing comprehensively consider the relationship between the three dimensions to conduct research on employee performance; in addition, the effectiveness of leadership style is the result of the joint action of leaders and employees, and previous studies have relatively ignored the moderating effects of gender and age characteristics on employee performance in China. Based on this, this study used meta-analysis and meta-analytic criterion profile analysis to explore the relationship between paternalistic leadership and employee performance in the Chinese context. Through a comprehensive search and screening of the Chinese and English literature on the relationship between paternalistic leadership and performance (including task performance, organizational citizenship behavior, and counterproductive performance) conducted in the Chinese context, a total of 139 studies with 400 effect values were included (N = 44605). Our research contained three steps. In the first step, the main effects of authoritarian leadership, benevolent leadership and moral leadership on employees are estimated, including task performance, organizational behavior, and counterproductive performance with the Hunter-Schmidt meta-analytic method. Thereafter, we identify the paternalistic leadership profile when it is best related to employee performance through multiple regression and determine which profile of paternalistic leadership has the strongest predictive power for task performance, organizational citizenship behavior, and counterproductive performance respectively with the meta-criterion profile. Thirdly, we also tested whether employee's characteristics of gender and age played a moderating role in those relationships. The results of the meta-analysis found that (1) Benevolent leadership and Moral leadership have strong positive correlations with both task performance and organizational citizenship behavior, and strong negative correlations with counterproductive behavior. In contrast, Authoritarian leadership has a significant negative correlation with task performance and organizational citizenship behavior, and a significant positive correlation with counterproductive behavior. (2) Low authoritative leadership profile (high level of benevolent and moral leadership) has the strongest predictive power for task performance and organizational citizenship behavior, and high authoritative leadership profile (low level of benevolent and moral leadership) has the strongest predictive power for counterproductive behavior. (3) ?Moderator analyses revealed that average age produces a meaningful impact on the relationships between authoritarian leadership and organizational citizenship behavior and counterproductive performance, the strength of the relationship between moral leadership and task performance. Moreover,the moderating effect of gender on the relationship between paternalistic leadership and performance is not significant. The findings firstly revealed the "truth" about the relationship between paternalistic leadership and employee performance in the Chinese context, which guide the direction of subsequent leadership research; Secondly, the relationship between the profile of the three-dimensions combination of paternalistic leadership and employee performance is explored from the perspective of person-centered, and the conclusion is more accurate and effective; Finally, the boundary conditions between paternalistic leadership and employee performance are clarified from the perspective of employee demographic characteristics. Further, this research has implications for management practice. On the one hand, leaders should adjust the combination of paternalistic leadership styles in time according to the environment to promote the optimal performance of subordinates; On the other hand, leaders should treat employees of different genders equally and adjust their leadership styles according to the needs of employees of different ages. Figures and Tables | References | Related Articles | Metrics

Regular Articles

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The processing mechanism of aesthetic pleasure in the perspective of neuroaesthetics ZHANG Xuan, ZHOU Xiaolin 2021, 29 (10):  1847-1854.  doi: 10.3724/SP.J.1042.2021.01847 Abstract ( 1833 )   HTML ( 104 )   PDF (648KB) ( 2431 )   The aesthetic objects arouse aesthetic pleasure that is specific and intense. The Pleasure-Interest Aesthetic model (PIA) suggests that aesthetic processing is a dual-process including the automatic process for sensory pleasure and the control process for aesthetic interest pleasure. Here we review recent work on the neural substrates of aesthetic pleasure. A large body of studies demonstrates that the orbitofrontal cortex is automatically activated by the objects of aesthetic appreciation. The orbitofrontal cortex which is responsible for automatic emotion regulation and reward processing of pleasure is generally activated in aesthetic activities and it is the neural basis of the automatic processing for sensory pleasure. Different modes of functional connectivity with the striatum support different aspects of aesthetic processing: the release of endogenous dopamine in the caudate nucleus is concentrated in the early aesthetic stage, and then gradually decreases during the in-depth process of aesthetic experience, while the release of endogenous dopamine in the nucleus accumbens gradually increases during the in-depth phase. This is evidence for the PIA model. However, additional brain circuitry is engaged such that the default mode network (DMN) is activated and the lateral prefrontal cortex is deactivated when the aesthetic flow experience occurs, indicating that beyond the dual-process highlighted by the PIA model there is a higher level of aesthetic flow pleasure. The automatic processing for sensory pleasure and the control processing for aesthetic interest pleasure are different from the aesthetic flow pleasure. Aesthetic flow pleasure is not the satisfaction of the needs of the senses, but the high-level pleasure which is liberated from the spirit; it is the experience of the soul gaining strength and courage and it is related to a clear self-consciousness. Therefore, aesthetic flow pleasure is independent of the automatic processing for sensory pleasure and the control processing for aesthetic interest pleasure. We point out that the PIA model needs to be expanded to include this dimension of aesthetic processing. The extended model includes three levels of aesthetic pleasure including sensory pleasure, aesthetic interest pleasure, and aesthetic flow pleasure. They are generated respectively in three stages of aesthetic appreciation: automatic processing, controlled processing, and integration and sublimation. Further studies should be conducted on how the aesthetic experience could impact upon creativity and to what extent different aesthetic experiences have the same or differential neural bases for giving rise to aesthetic pleasure. Figures and Tables | References | Related Articles | Metrics

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Mirror equivalence or invariance and its breaking: Evidence from behavioral to cognitive neural mechanism QI Xingliang, CAI Houde 2021, 29 (10):  1855-1865.  doi: 10.3724/SP.J.1042.2021.01855 Abstract ( 936 )   HTML ( 35 )   PDF (736KB) ( 1135 )   Mirror equivalence or invariance, also known as mirror generalization or symmetry generalization, is a perceptual property in which individuals regard the left-right mirror of a visual perception object as the same stimulus. Experimental evidence of animal behavior and human cognition shows that mirror equivalence or invariance is an evolutionary adaptive process of animal and human individuals to the bilateral symmetry of natural objects, which has obvious advantages in reducing cognitive learning load and survival pressure. Recent studies on cognitive neural mechanism finds that mirror equivalence or invariance is hierarchically processed in the ventral visual pathway of primates, and the fusiform gyrus cortex in the human brain is a key region for processing mirror equivalence or invariance information of objects or faces. Importantly, mirror equivalence or invariance may hinder the reading of script containing mirror characters, leading to mirror errors in the early reading for normal children. Therefore, it is necessary for readers to learn to use the inhibitory mechanism of “unlearning” of mirror generalization, so as to break the mirror equivalent or invariance and to acquire the ability of identifying the mirror characters. In this process, the left fusiform gyrus cortex gradually develops into the VWFA capable of recognizing mirror characters, but it still exhibits mirror equivalence or invariance for objects or faces. This is consistent with the neuronal recycling hypothesis, i.e., learning to read must occupy neurons in the left fusiform gyrus previously used for object or face processing. Furthermore, developmental dyslexia children (DD) have difficulty in inhibition of mirror generalization, suggesting the mechanism of breaking mirror equivalence or invariance may be abnormal in DD. Therefore, exploring the cognitive neural mechanism on breaking the mirror equivalent or invariance is important for elucidating the brain plasticity of learning to read. In this paper, we first briefly discuss the evolutionary adaptive theory of mirror equivalence or invariance and early related behavioral and cognitive study evidence. Then we systematically review recent evidence on the hierarchical processing of mirror equivalence or invariance in the ventral visual pathway, the role of the fusiform gyrus cortex of human brain in the process of mirror equivalence or invariance, the cognitive neural mechanism on breaking the mirror equivalent or invariance during learning to read, and the difficulty in mirror generalization inhibition and related brain network abnormality in DD. We propose that the interaction between the left fusiform gyrus or the VWFA and the early visual cortex, the parietal cortex and the brain network of spoken language may be an important neural basis for learning to use the inhibitory mechanism of mirror generalization for breaking the mirror equivalence or invariance. Future studies are needed to focus on the role of the two hemispheres and their commissure fibers in mirror equivalence or invariance processing, the detailed processing mechanism of mirror generalization and inhibition, the influence of mirror generalization and inhibition on mirror writing, and the mirror generalization processing of Chinese characters in normal Chinese children. Figures and Tables | References | Related Articles | Metrics

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The big-five personality profiles: A person-centered approach YIN Kui, ZHAO Jing, ZHOU Jing, NIE Qi 2021, 29 (10):  1866-1877.  doi: 10.3724/SP.J.1042.2021.01866 Abstract ( 3010 )   HTML ( 141 )   PDF (646KB) ( 3639 )   The big-five personality profile is the combination of the high and low level big-five personality traits in individuals, which fully considers the interaction between personality traits and reflects the differences in quantity and quality of the big-five personality traits among different subgroups. The big-five personality profile is significant to explain the variable-centered contradictory conclusions, which meets the needs of organizational management practice and has a stronger guiding significance for practice. To date, more and more research has applied person-centered approach to examine the role of personality profile in personnel evaluation, human resource development and decision-making. However, the existing relevant reviews of the big-five personality were variables-centered, and there is a lack of systematically reviewing the core issues of the big-five personality, such as the theoretical basis for dividing individuals into different subgroups based on the big-five personality, the number of profiles composed of the big-five personality traits and the characteristic similarity of the profiles obtained from different studies, etc. Moreover, the research on the big-five personality profiles has just started in the organizational behavior and human resource management field, so it is uncertain that how many big-five personality profiles can effectively explain the predictive role of personality. The advantages of the big-five personality profiles research over the big-five personality traits research are reflected in: (1) The former considers personality as an integrated system, fully considering the interaction between the big-five personality traits. As an important supplement to the latter, it can expand the understanding of the relationship between personality traits and different outcomes. (2) The study of big-five personality profiles is convenient for variable combination, and the constructed profiles can be used as a variable, which is beneficial to explain the contradictory conclusions of past variable-centered research. (3) The research of big-five personality profiles, a typical application of person-centered approach, is more in line with the reality of sample heterogeneity. (4) The big-five personality profiles are more in line with reality of the individual's cognitive model and has a stronger guiding significance for practice. Based on the person-centered approach, a systematic review of relevant research on the big-five personality profiles in the field of organizational behavior and human resource management. We found: (1) The number of big-five personality profiles is affected by measurement tools, research situation, sample characteristics, research methods and so on. Based on the ego control - ego resiliency model, four profiles can be identified, which include commonly known Resilient profile, Ordinary profile and Rigid profile. (2) The big-five personality profiles act more as independent variables to explore whether there are differences in key outcomes and as moderators regarded as important resources for individuals to cope with identity transformation and work pressures. Four directions for future research were proposed: (1) Strengthen the theoretical foundation and explore the role of other theories in explaining the big-five personality profiles. (2) Strengthen repetitive research and identify the general big-five personality profiles, which is conducive to the comparison of subsequent research conclusions and also to provide guidance for practical managers. (3) Identify the antecedents of the big-five personality profiles to better understand why different research conclusions differ. (4) Include more personality traits to describe the personality profiles more thoroughly. Finally, in the field of organizational management and human resources management, future research can learn from the personality profiles in psychology to probe into the employee category with multiple personality traits to realize employee category management more comprehensively and accurately. Figures and Tables | References | Related Articles | Metrics

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The expression mechanism of individual behavior in the perspective of I3 model ZHANG Lu, WU Yuntena, JIN Tonglin 2021, 29 (10):  1878-1886.  doi: 10.3724/SP.J.1042.2021.01878 Abstract ( 836 )   HTML ( 40 )   PDF (603KB) ( 1184 )   I3 model, which is also known as “I-cubed model”, has shaped a theoretical framework for explaining individual behavior, which argues that all behaviors emerge from a combination of instigation, impellance and inhibition. Each factor can change independently of the other two. The structure of I3 model is a comprehensive model of 12 paths consisting of three factors (instigation, impellance, and inhibition), a mediator (behavioral proclivity), and an outcome (behavior). The 12 paths predict the individual behavior mechanism in specific contexts by describing 18 problems, such as aggressive behaviors, eating behaviors, etc. As a theoretical framework for behavior research, Finkel and other researchers verified and supported the theory with empirical studies on a large number of studies over the past 10 years, which suggests that the theory is consistent with the data. In addition, in terms of the self-consistent nature of the model system, the I3 model has certain rationality, compatibility, and unique value compared with other classical theories and behavior models. First, the I3 model is compatible with the Planned Behavior Theory and Dual-Process Theory. In this sense, Finkel demonstrates the rationality of constructing I3 model by referring to the behavioral tendency of the Planned Behavior Theory and the behavior reaction of the Dual-Process Theory. Second, the I3 model has its own peculiarities as well as its superficial similarities with general behavioral models such as the "S-O-R'' model. Overall, Finkel tries to combine the research results and theoretical viewpoints under various mediating and regulating research paradigms to establish a grand theoretical system of human behavior. Third, in the same way, Finkel specifically analyzes the similarities and differences of I3 model with General Aggression Model and Goal Conflict Model in explaining the attack behavior and feeding behavior, thus revealing the uniqueness of I3 model. However, this model has the following problems: (1) From the perspective of the entire research system, this model is only a scientific theory and has not yet reached the level of a metatheory. (2) From the perspective of theoretical guidance, the model does not clearly explain the boundary conditions and scope of the theory, and does not consider the sources of other research questions. Although various main effects and interaction tests have been examined, it is still a data-driven test, and it does not reveal the actual interaction mechanism of these three forces; (3) From the practical application, the I3 model only focuses the individual behaviors, such as eating behaviors and aggressive behaviors, few researchers explore other individual behaviors and social behaviors, such as prosocial behaviors, learning behaviors, etc. There are also certain flaws in the interpretation of some specific behaviors and the explanation of some specific behaviors also has certain defects. Consequently, it is necessary to broaden the field of behavioral research on the I3 model in the future, not only to analyze the subtypes of the behavior and the types of behavior in different scenarios but also to explore the tendency and intention of the individual behavior; Secondly, we should apply the I3 model according to local condition, which needs to determine the type and quantity of factors combined with specific research problems, so as to achieve the results we want; Last but not least, we should integrate I3 model with other theories, and further explore the boundary conditions of I3 model. In summary, I3 model improves explanatory power of behavior theory to various behavior types and meets the current concern regarding behavioral research, such as behavior nudge and network behavior, and enriches theoretical support and theoretical framework for behavioral analysis. Figures and Tables | References | Related Articles | Metrics

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Automatic perspective taking: The debate between implicit mentalizing and submentalizing LI Yi, XIAO Feng 2021, 29 (10):  1887-1900.  doi: 10.3724/SP.J.1042.2021.01887 Abstract ( 1657 )   HTML ( 87 )   PDF (1504KB) ( 1927 )   Abstract: Automatic perspective taking refers to an individual tracking other people's opinions or being affected by others' perspectives, even when he or she is not explicitly required to adopt such perspectives. This phenomenon is revealed by the consistency effect, which suggests that a worsening perspective taking performance is triggered when others' perspectives and one's own perspective are different. There are two main views regarding automatic perspective taking: the implicit mentalizing view holds that automatic perspective taking is a domain-specific process that spontaneously selects and processes others' perspectives, and the submentalizing view proposes a domain-general process, such as reflective attentional orientation or the spatial coding of a location, that simulates the role of the mentalizing process in thesocial environment. The common tasks in automatic perspective taking include the dot perspective taking task, the "social" Simon effect, the ambiguous number task, and the anticipatory looking paradigm. The consistency effect found in each task can be explained by both the implicit mentalizing view and the submentalizing view. Among the abovementioned tasks, the dot perspective taking task is the most typical type of automatic perspective taking task due to its utilization of the least cognitive resources compared to other automatic perspective taking tasks. Most behavioural experiments involving the dot perspective taking task pay great attention to the implicit mentalizing factors (including the social relevance of cues, the visual attribution state of cues, and the acquisition of a social perspective), but the results of the same factors support different views. In addition, cognitive neuroscience studies mainly compare the distinct neural mechanisms of one's own perspective with those of others' perspectives, but the consistency effect from a single perspective is less explored.Therefore, the interpretation of automatic perspective taking is still debatable. The evidence of the two views in automatic perspective taking shows that one view is unable to completely negate the other one, so it is possible to synthesize the two views into a framework, i.e., the synergistic model of implicit mentalizingand submentalizing processes.The consistency effect in automatic perspective taking can be achieved in three ways:(1) after the presentation of a visual stimulus that is first perceptually processed, if only directional cues but no social cues are found, then the consistency effect will be triggered by a directional process;(2) if social cues are present but do not reach the threshold of social perception activation, then they are instead processed as directional cues, and thus, automatic perspective taking will be activated by the submentalizing process; and (3) if social cues are present and reach the threshold of social perception activation, then automatic perspective taking is activated by the implicit mentalizing process. If social cues are present and reach the threshold of social perceptual activation and directionality is enhanced, then the implicit mentalizing and submentalizing processes will be performed together. The processing threshold of activating social perception has not been clearly defined, so further evidence is required to determine this threshold and to establish the occurrence context of minimum automatic perspective taking. Previous studies adopting the dot perspective taking task have focused more on the implicit mentalizing factors with different task details, in contrast to the submentalizing factors, so it is difficult to directly compare the results. Second, the small sample sizes of the experiments may reduce the reproducibility of the results. In addition, special individuals, such as infants, groups with autism spectrum disorder, and deaf children, may have different developmental performance levels in terms of perspective taking. Therefore, manipulating the implicit mentalizing and submentalizing factors of special individuals, as well as utilizing eye-tracking and/or cognitive neuroscience technologies, can provide further evidence of the underlying mechanisms of automatic perspective taking. Figures and Tables | References | Related Articles | Metrics