Most Down Articles

Published in last 1 year | In last 2 years| In last 3 years| All| Most Downloaded in Recent Month | Most Downloaded in Recent Year|

In last 3 years

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

Moral foreign language effect and its moderating variables: A systematic review and meta-analysis ZHU Lin, LIU Jinru, LI Jing, LIU Conghui Advances in Psychological Science    2022, 30 (1): 32-50.   DOI: 10.3724/SP.J.1042.2022.00032 Abstract （210）            Knowledge map    Individual decisions in the field of moral judgement are often related to "hurting or sacrificing the innocent" and "tolerating immoral behaviors." Previous studies have shown that when presented with the moral decision-making situation within a foreign language context, the individuals will show a stronger utilitarian and a more tolerant moral evaluation tendency compared with the same situation within the native language context. This phenomenon is defined as the moral foreign language effect. The influence of the language context on the moral judgement has been investigated by numerous studies. However, the results were far from consistent. To this end, we used meta-analysis to explore the effect of the language type (native language vs. foreign language) on the individuals' utilitarian tendency in moral judgments, and we analyzed several moderating variables. A total of 19 papers were retrieved from literature, with 46 independent samples, 97 effect sizes and 9672 participants that met the inclusion criteria of the meta-analysis. First, we analyzed the effect of the language type (native language vs. foreign language) on the utilitarian tendency in moral judgments using the ‘metafor' R package. Next, the potential moderation effects of several factors were examined, including the moral dilemmas story type (personal moral dilemmas vs. impersonal moral dilemmas vs. daily moral evaluation situations), sex, scoring method (two-point scoring vs. multi-point scoring) and language family type (same vs. different). In addition, we used Bayesian factor estimation for secondary exploration of the results that had a nonsignificant moderating effect. Our meta-analysis resulted in the following findings. First, the main effect test indicated that the language type has a significant effect on the utilitarian tendency in moral judgment, with a small but stable moral foreign language effect (g = 0.23). Second, the moderation analysis indicated that the moral foreign language effect was influenced by the story type; there was a small but stable effect of the language type for personal moral dilemmas (g = 0.32), but not for impersonal moral dilemmas (g = 0.11) or daily moral evaluation situations (g = 0.12). The foreign language effect under impersonal moral dilemmas was affected by the scoring method; a significant effect was found under multi-point scoring (g = 0.27), but not under two-point scoring (g = 0.05). On the other hand, there was no significant moderating effect for the sex or language family type. In addition, Bayesian analysis showed only moderate evidence for the absence of moderating effect regarding the factors of sex, scoring methods and language family type. The stability of these conclusions can be further verified in future research. In summary, this study used meta-analysis to systematically explore the robustness and influencing factors of foreign language effects in moral judgment and answered the disputes about the stability of the moral foreign language effect. The results showed a small but relatively stable effect of the language type on the utilitarian orientation in moral judgment. We analyzed the moderating effects of multiple variables, including variables that have not been well-considered in previous studies, such as the scoring methods (two-point scoring vs. multi-point scoring). Our work did not only find the moderating effect of the type of moral dilemmas, but it also revealed the potential impact of the scoring method on the effect size. This provides certain enlightenment and guidance for future empirical studies when selecting the experimental materials and statistical methods. Finally, we used a variety of data processing methods to increase the robustness of the results. For example, robust variance estimation (RVE) was used to control the correlations between dependent effect sizes and compare our results with those of traditional meta-analysis, so as to understand how the results of the meta-analysis are influenced by the correlations between multiple dependent effect sizes. Related Articles | Metrics

Select

The neural mechanisms of developmental motor disorders in children with autism spectrum disorder WANG Lin, WANG Zhidan, WANG Hongjing Advances in Psychological Science    2021, 29 (7): 1239-1250.   DOI: 10.3724/SP.J.1042.2021.01239 Abstract （63）            Knowledge map    Developmental motor disorders are the common feature of autism spectrum disorder (ASD). Through a systematic review of the neuroscience literature, it is found that the alteration in the concentration of GABA and of serotonin and the abnormal expression of GABA-related protein and of shank protein led to not only the defects of the development of the central nervous system but also the synaptic excitation/ inhibition imbalance, thus in turn resulting in the changes of the functional connectivity between cerebellum and motor cortex in children with ASD. The abnormalities in the structure of the cerebellum, basal ganglia, and corpus callosum had a negative impact on the whole-brain connectivity in children with ASD. The disorders in neurobiochemical mechanisms and the abnormalities of brain structure together triggered abnormal brain function of children with ASD, which ultimately resulted in developmental motor disorders. In addition, the common neural basis shared by the developmental motor disorders and the core symptoms of ASD mainly included the mirror neuron dysfunction, the abnormalities of the thalamus, the basal ganglia, the cerebellum and mutations of SLC7A5 and PTEN. Future researches need to focus on other neurotransmitters closely related to motor, such as acetylcholine and dopamine, to explore the dynamic mechanism and formation of the neural network of developmental motor disorders, and to analyze the interaction between the underlying neural mechanisms of motor developmental disorders and that of core symptoms of autism. Related Articles | Metrics

Select

A new type of mental health assessment using artificial intelligence technique JIANG Liming, TIAN Xuetao, REN Ping, LUO Fang Advances in Psychological Science    2022, 30 (1): 157-167.   DOI: 10.3724/SP.J.1042.2022.00157 Abstract （510）            Knowledge map    The application of artificial intelligence and big data mining technology in the field of mental health has promoted the development of intelligent mental health assessment. Intelligent mental health assessment entails the application of artificial intelligence technology in acquiring and analyzing data and modeling the relationship between behavioral features and mental health problems. Intelligent mental health assessment has broadened the forms of data and the analysis methods of traditional mental health assessment, enabling researchers to obtain multi-modal data based on more simulated situations and achieve more efficient and accurate assessments. At present, researchers mainly carry out mental health assessments based on social media data, smart device data, video game data, and wearable device data to explore various features related to mental health and build predictive models. Social media data mainly refer to the text content posted by users on social media, which is widely used in psychological assessment. Researchers have explored text features related to mental health. Foreign researchers mainly predict users' mental health conditions based on the contents posted on platforms such as Facebook and Twitter. Domestic researchers mostly rely on Weibo and other platforms to conduct related research. Smartphones and other devices record individual daily behavioral data, including application software use, communication, location movement (based on GPS), etc. These behavioral data provided effective information for predicting the psychological characteristics of individuals. Besides, with the widespread use of smartphones and other mobile devices, collecting audio and video data has become more convenient. Researchers can extract features such as actions, voices, and expressions to achieve an immediate and automatic evaluation of participants' mental health. Video game data refers to the log data of the player during the game. It contains a wealth of behavioral performance information of the individual in the virtual environment. Researchers can evaluate the individual's abilities and psychological characteristics based on the data. Game-based assessment is mainly used to assess individual abilities and cognitive impairment. However, there are few studies on mental health assessment based on games, only some assessments of the positive personality. Mental health problems are often accompanied by obvious physiological reactions. Researchers use wearable devices to collect physiological indicators such as brain electricity, eye movements, heart rate, and skin temperature for mental health monitoring. Researchers use EEG data and eye movement data to identify mental health problems related to emotions and attention. Indicators of skin temperature and heart rate reflect the individual's mood and stress state and therefore have the potential to predict the level of individual mental health. The future research directions of intelligent mental health assessment mainly include five aspects. First, previous research on intelligent mental health assessment has often used data-driven methods to explore features and construct predicting models, which is hard to explain the complex relationship between behavioral indicators and latent mental health state. Therefore, further improvement of pertinence and refinement is demanded. Researchers should design tasks based on psychological theories, carry out meaningful feature extraction, and gradually refine from rough dichotomous diagnosis to continuous and typed diagnosis. Second, unsupervised data mining is difficult to ensure the validity and interpretability of assessment. To carry out effective assessment and reduce errors in the new simulated environment, the task design of intelligent mental health assessment should be designed based on the evidence center. Third, the current intelligent mental health assessment mainly uses the indicators in the computer field, and the relevant research considering the reliability and validity is very rare. Researchers should select prediction models based on specific tasks and test the generalization and stability of prediction models in different datasets and scenarios. Fourth, different data sources and features have unique advantages. Researchers could obtain multi-modal data for modeling and analysis with the application of the advanced technology of artificial intelligence. Finally, privacy protection and ethical issues are essential for intelligent mental health assessment. Subjects should be notified before data acquisition and use. Related Articles | Metrics

Select

Random intercept latent transition analysis (RI-LTA): Separating the between-subject variation from the within-subject variation WEN Congcong, ZHU Hong Advances in Psychological Science    2021, 29 (10): 1773-1782.   DOI: 10.3724/SP.J.1042.2021.01773 Abstract （333）            Knowledge map    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. Related Articles | Metrics

Select

Moral injury: A review from the perspective of psychology AI Pan, DAI Yan Advances in Psychological Science    2022, 30 (1): 168-178.   DOI: 10.3724/SP.J.1042.2022.00168 Abstract （155）            Knowledge map    Moral injury refers to the long-lasting psychological, biological, spiritual, behavioral and social impact on an individual after the exposure to morally injurious events, which entail “perpetrating, failing to prevent, bearing witness to, or learning about acts that transgress deeply held moral beliefs and expectations” (Litz et al., 2009). Since Litz et al. (2009) redefined this concept from the perspective of psychology, moral injury has attracted extensive attention in the fields of psychology, ethics, psychiatry, and sociology. The present article reviews and summarizes the concept, relevant mechanisms, measurements, and interventions of moral injury and offer recommendations for future research. We first review the background of moral injury. Moral injury can be traced back to survivor guilt, which has long been regarded as one of the symptoms of post-traumatic stress disorder. However, Litz et al.(2009) pointed out that moral injury and post-traumatic stress disorder are two different concepts, and Shay(2014) listed the five differences between moral injury and post-traumatic stress disorder in detail. Next, we review the mechanisms of moral injury. Under the influence of individual and social factors, potentially morally injurious events that severely violate an individual's moral code can lead to cognitive dissonance and intrapsychic conflict, and eventually produce lasting shame, guilt, and anxiety. In addition, different types of potentially morally injurious events may lead to different types of moral injury, but the specific mechanism is still unclear. Self-oriented events (e.g., committing a crime, failing to prevent a crime, etc.) are more likely to result in negative internal emotions and cognitions (e.g., guilt, shame, inability to forgive oneself), whereas other-oriented events (e.g., witnessing an act of violence, betrayal by a trusted person) are more likely to lead to negative external emotions and cognitions (e.g., anger, loss of trust, inability to forgive).Third, we summarizes the existing moral injury scales, with a focus on the scope of application and each scale's advantages and disadvantages. These scales can be divided into two categories according to their contents, with one group assessing moral injury symptoms alone, and another assessing both the moral injury events and symptoms. Researchers or clinicians can choose these scales according to their practical needs. Moreover, current interventions for moral injury include Cognitive Behavior Therapy, CBT-based Adaptive Disclosure Therapy, CPT-based Spiritually Integrated Cognitive Processing Therapy, etc. While being commonly used in the treatment of PTSD, those therapies are equally effective in treating the core symptoms of moral injury. We concluded this article with limitations of existing research and suggestions for future research. Moral injury events and moral injury outcomes need to be further distinguished, moral injury mechanisms need to be further studied, and the diagnostic criteria of moral injury need to be established. Researchers also need to pay attention to the differences of moral standards in different cultures, expand research on moral injury to more groups, and widen the application of research on moral injury. Related Articles | Metrics

Select

Understanding mechanisms of prediction error cost in Chinese reading for older adults LI Lin, ZHAO Sainan, ZHANG Lijuan, WANG Jingxin Advances in Psychological Science    2022, 30 (1): 1-14.   DOI: 10.3724/SP.J.1042.2022.00001 Abstract （288）            Knowledge map    An important question for research on reading across the lifespan concerns whether mechanisms of cognitive processing undergo only quantitative changes or also qualitative changes with aging. To process written language effectively, readers use their existing knowledge to make predictive inferences about linguistic information. Quite often this will facilitate the processing of newly acquired information but will sometimes incur a processing cost due to predictive error. As Older adults appear to rely more heavily on lexical prediction during reading (Zhao et al., 2019, 2021). However, it is currently unknown whether, like young adults, they experience a processing cost due to predictive error, and whether the magnitude of this cost differs across age adult groups. Accordingly, the present research aims to understand the processing consequence of predictive error in both young and older adults, using methods that can shed light on both the behavioral and neural bases of these effects. This will be achieved using novel co-registration methods that synchronize the recording of electroencephalographic (EEG) signals with eye movements, so that behavioral and neural indices of language processing can be acquired simultaneously, in real-time, during natural reading. In particular, this approach will enable the analysis of fixated-related potentials (FRPs), which are averaged EEG waveforms time-locked to a fixation on a target word in a sentence during normal reading. Study 1 will manipulate whether a target word is predictable from the prior sentence context, using contexts in which the target word is predictable, ones in which it is unpredictable, and neutral contexts containing an unpredictable word. Crucially, comparisons of an unpredictable word in neutral compared to constraining contexts will provide a measure of prediction error, which is the cost incurred when the target word is unpredicted in a constraining context. The study will investigate the behavioral and neural correlates of this prediction error using a combination of eye movement measures and FRPs for target words. Moreover, by investigating age differences in these effects (i.e., for young compared to older adults) the study will reveal whether this prediction error differs across adult age groups. Study 2 will test these effects further by examining both the contribution to the prediction error cost of parafoveal information availability and individual differences in visual, cognitive and linguistic abilities. To examine the contribution of these individual differences, we will comprehensively assess the visual, cognitive and linguistic abilities of young and older adult participants prior their taking part in experiments. We will obtain information about participants' educational background, vocabulary knowledge and recent reading experience to match participants in terms of formal educational experience and to obtain indices of linguistic experience. In addition, we will assess processing speed, working memory, and inhibition as measures of cognitive capabilities. The data obtained will be used for the linear mixed-effects modelling of Study 3. Experiment 1 will use the boundary paradigm to investigate age differences in the prediction error cost when parafoveal information is available or not. The aim of this experiment is to establish whether limiting the availability of parafoveal information about an upcoming word differentially impacts lexical prediction by young and older adults. Experiment 2 will use masking text paradigm to investigate the aging effects on prediction error cost under high or low working memory load conditions. The aim of this experiment is to explore the effect of working memory load on prediction processing mechanism of young and older readers. Finally, in Experiment 3, the older adult participants will be divided into good and poor reading skill groups to examine whether there is a difference in the prediction error cost for older participants with good and poor reading skills as compared to skilled young adult readers. This will reveal how reading skills mediates predictive processing by older adults. Study 3 will use linear mixed-effects modelling and data-mining methods. All relevant factors will be included in the model analysis as covariates to investigate their effects on the prediction processing of older readers. Moreover, survival analysis and distribution analysis will be adopted to investigate the time course and individual differences of the above-mentioned effects (using data from Study 1 and 2). The findings from these studies will provide important insights into the nature of effects of cognitive aging and individual differences in visual, cognitive and linguistic abilities on neural and cognitive indices of word prediction in reading, and will form the basis for future models of these effects in Chinese reading. Moreover, the findings will shed light on the contribution of parafoveal processing, memory load, and reading skill on the predictive abilities of older adult readers. Related Articles | Metrics

Select

The effect of time and money concepts on consumers' purchase decision and its psychological mechanism HE Ruwan, LI Bin, ZHANG Shuying, CUI Xinyue, LEI Li Advances in Psychological Science    2021, 29 (9): 1684-1695.   DOI: 10.3724/SP.J.1042.2021.01684 Abstract （93）            Knowledge map    Time and money are two important resources that affect consumer decision-making differently. Based on the dual-process theory, this paper discusses the effect of time and money concepts on consumers' purchase decisions and its psychological mechanism through reviewing previous literature. Specifically, the effect of time and money would be differential in consumers' the purchase decision process such as pre-purchase stage, purchasing stage, and post purchase stage. For pre-purchase decisions stage, the concept of time and money will affect consumers' product search and product evaluation strategy. When consumers search for products, currency of search (time or money) will moderate the effect of magnitude of search costs on people's willingness to search. When the currency is money, lower (vs. higher) search costs will result in higher willingness to search. When the currency is time, this effect of search costs on willingness to search will be relatively weaker. As for consumer's product evaluation, they will adopt an alternative-based evaluation strategy to evaluate product information after they are activated time concept and adopt an attribute-based evaluation strategy to evaluate product information after they are activated money concept. For purchasing decision stage, the impacts of priming time and money on product selection are different. Consumers will make different choices between virtue and vice products, hedonic and utilitarian products, experiential purchases and material purchases, and anthropomorphized products when they are activated time or money concepts. That is, if the time concept is activated, consumers tend to choose a virtue product, hedonic product, experiential purchases and prefer anthropomorphic products with no prominent functional purpose. If money is activated, consumers tend to choose vice products, utilitarian products, material purchases and prefer the anthropomorphic products with prominent functional purpose. For post-purchase decision stage, the concepts of time and money also have different effects on consumers' product attitude and the consistency of product preferences. Time priming leads to a more positive attitude toward products and a higher degree of consistency in preferences than money priming. However, for luxury goods, free goods and high materialists, money priming has a better effect than time priming. From the perspective of the dual-process theory, the psychological mechanism due to different cognitive processing modes and mindsets that are primed by time and money. Concretely, because of the value of time are more ambiguous, difficult to calculate, difficult to explain, irreplaceable and intangible than money, consumers are more dependent on the experience system to process time and product information heuristically, holistically and affectively and fall into emotional maximization mindset. Because the value of money is more specific, easy to analyze, replaceable and tangible than time, consumers are more dependent on the rational system to process money and product information analytically and fall into value maximization mindset. As a result, due to the difference of time and money concepts, different processing methods and thinking patterns further lead to consumers make different purchase decisions in three aspects: pre-purchase decision (product search and product evaluation strategy), purchasing decision (product selection) and post-purchase decision (product attitude and the consistency of product preferences). Future research should further explore the following issues: (1) Elaborating the different effects of priming time and money on purchase decisions. (2) Considering the impact from the tradeoff between time and money on purchase decisions. (3) Further exploring the different influences of priming time and money on the pre-purchase decision. (4) Exploring the neural mechanisms underlying the different effects of time and money on purchase decisions. Related Articles | Metrics

Select

Mixture Model Method: A new method to handle aberrant responses in psychological and educational testing LIU Yue, LIU Hongyun Advances in Psychological Science    2021, 29 (9): 1696-1710.   DOI: 10.3724/SP.J.1042.2021.01696 Abstract （100）            Knowledge map    Aberrant responses have been repeatedly reported in psychological and educational measurement. If traditional measurement models or methods (e.g., item response theory, IRT) are applied to data sets contaminated by aberrant responses, parameter estimates may be biased. Therefore, it is necessary to identify aberrant responses and to reduce their detrimental effects. In the literature, there are two traditional response time (RT)-based methods to detect aberrant responses: RT threshold method and RT residual method. The focus of these methods is to find a threshold of RT or RT residual. If a RT or RT residual is remarkably less than the threshold, this response should be regarded as an aberrant response with extremely short RT (e.g., speededness, rapid-guessing), and consequently does not provide information about the test taker's latent trait. Afterwards, down-weighting strategy, which tries to limit the influence of aberrant responses on parameter estimation by reducing their weight in the sample, can be applied. The mixture model method (MMM), is a new method proposed to handle data contaminated by aberrant responses. This method applies the accommodating strategy, which is to extend a model in order to account for the contaminations directly. MMM shows more advantages in terms of: (1) detecting aberrant responses and obtaining parameter estimates simultaneously, instead of two steps (detecting and down-weighting); (2) precisely recovering the severity of aberrant responding. There are two categories of MMM. The first category of methods assumes that the classification (i.e., whether the item is answered normally or aberrantly) can be predicted by RT. While the second category is a natural extension of van der Linden's (2007) hierarchical model, which models responses and RTs jointly. In this method, the observed RT, as well as the correct response probability of each item-by-person encounter can be decomposed to RT (or probability) caused by normal response and that caused by aberrant response according to the most important difference between the two distinct behaviors. This method leads to more precisely estimated item and person parameters, as well as excellent classification of aberrant/normal behavior. First, this article compares the basic logic of the two traditional RT-based methods and MMM. Aberrant responses are regarded as outliers in both RT threshold method and RT residual method. Therefore, they rely heavily on the severity of aberrance. If data set is contaminated by aberrant responses seriously, the observed RT (or RT residual) distribution will be different from the expected distribution, which in turn leads to low power and sometimes high false detection rate. On the other hand, MMM, which assumes that both observed RT and correct response probability follow a mixture distribution, treats aberrant and normal responses equally. In that way, it has little reliance on the severity of aberrance. In addition to that, MMM can apply to the situation when all the respondents actually respond regularly in theoretic. In that situation, all the responses are assumed to be classified into one category. Second, this article summarizes the disadvantages of the three methods. MMM has three primary limitations: (1) it usually relies heavily on strong assumptions, which means that it may not perform well if these assumptions are violated; (2) low proportion of aberrant response may lead to convergence problem and model identification problem; (3) it is quite complex and time-consuming. In all, practitioners should choose a proper method according to the characteristics of tests and categories of aberrant responses (e.g., rapid-guessing, item with preknowledge, cheating). In the end, this article suggests future researches may investigate the performance of MMM when its assumptions are violated or data consists of more types of aberrant response patterns. Fixing item parameter estimates, proposing some index to help choosing suitable methods, are encouraged to improve the efficiency of MMM. Related Articles | Metrics

Select

Intensive longitudinal data analysis: Models and application ZHENG Shufang, ZHANG Lijin, QIAO Xinyu, PAN Junhao Advances in Psychological Science    2021, 29 (11): 1948-1969.   DOI: 10.3724/SP.J.1042.2021.01948 Abstract （196）            Knowledge map    In the fields of psychology, education, and clinical science, researchers have devoted increasing attention to the intraindividual dynamics of behaviors, minds, and treatment effects over time, making personalized modeling a growing concern. Traditional cross-sectional and longitudinal studies only have a few measurement time points for each individual, which is not suitable for studying intraindividual dynamics. Intensive longitudinal design collects a set of measures from individuals at multiple time points with higher frequency over longer periods. With its strengths in more immediate, accurate, and authentic assessments, this design is more suitable to investigate the dynamics and mechanisms of intraindividual processes. With the development of mobile phones and other mobile devices, researchers can conveniently collect intensive longitudinal data for various aspects of psychology, including individual emotion, personality, cognition, and behavior patterns. The intensive longitudinal design has recently become one of the most prominent and promising approaches in psychological research, but most of these studies still relied on traditional analyzing methods. We first reviewed a conventional method of intensive longitudinal data analysis, the multilevel linear model (MLM), and discussed its limitations in analyzing intensive longitudinal data. We then introduced the principles, empirical applications, strengths, and weaknesses of two advanced modeling methods, dynamic structural equation model (DSEM) and group iterative multiple model estimation (GIMME). DSEM is a top-down approach of modeling intensive longitudinal data while GIMME is a bottom-up one, both being implemented in commonly used software. DSEM is one of the most promising methods for intensive longitudinal modeling and can be regarded as a multilevel extension of the dynamic factor model (DFM). It combines the strengths of various modeling approaches, including multilevel modeling, time-series modeling, structural equational model (SEM), and time-varying effects modeling (TVEM). GIMME is a dynamic network method initially proposed for functional magnetic resonance imaging (fMRI) data analysis and has recently been applied to intensive longitudinal data analysis. It combines individual- and group-level information to estimate network models at both levels, bridging nomothetic (population) and idiographic (individual) approaches to intensive longitudinal data analysis. By introducing these two advanced modeling methods, the current review can help deepen the understanding of the top-down approach and bottom-up approach and clarify their strengths and weaknesses in the intensive longitudinal data analysis. To help empirical researchers better understand the modeling of DSEM and GIMME and show the advantages of the two models compared with MLM, we provided a tutorial on how to analyze the intensive longitudinal data with the three models (i.e., MLM, DSEM, and GIMME), respectively. We presented the analyzing processes step by step and explained how to interpret the results of these models accordingly. By comparing the output results of the three models, the current review summarized the characteristics of each model. The corresponding Mplus and R codes were provided in the appendixes. Along with the three modeling methods mainly introduced in the current review, we also provided a general introduction of other common modeling methods in the intensive longitudinal data analysis. The current review summarized the popular models in the intensive longitudinal data analysis on their strengths and weaknesses and guided researchers to select suitable modeling methods in different situations. The current review contributes to the development and application of the advanced methods of intensive longitudinal data analysis and helps researchers better understand the dynamic process behind the intensive longitudinal data. Related Articles | Metrics

Select

The impact of trust in technology and trust in leadership on the adoption of new technology from employee's perspective XU Yi, LIU Yixuan Advances in Psychological Science    2021, 29 (10): 1711-1723.   DOI: 10.3724/SP.J.1042.2021.01711 Abstract （219）            Knowledge map    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. Related Articles | Metrics

Select

Food labeling effects in marketing YANG Qiaoying, LIU Wumei, ZHANG Dong Advances in Psychological Science    2021, 29 (9): 1669-1683.   DOI: 10.3724/SP.J.1042.2021.01669 Abstract （565）            Knowledge map    As a tool to convey food-related information to consumers, food labels can effectively solve the problem of information asymmetry in food consumption. With the popularization of food labels in practice, more and more scholars have begun to pay attention to the impact of different food labels on consumer behavior. However, most of the existing studies focus on a single food label type and its effects, lacking of comparison and discussion on the effects of different food labels and their inherent mechanisms and boundary conditions. Based on this, this paper reviews the research on food labels in the field of marketing, which focuses on how different types of food labels affect individuals' cognition, emotion and behavior. Meanwhile, this paper introduces the regulatory orientation theory to explain the different effects of different food labels, and on this basis, a more integrated food label effect framework is constructed in this paper. Through combing the existing literature, the existing research on food labeling has roughly underwent three stages. The first stage began in the early 1980s. The demand for the nutritional value of food led to the attention and research on the nutrition label. The second stage started around 2000. Scholars mainly focus on labels that can convey information about food safety and quality. In the third stage, in the last decade, eco-environmental labels attracted more attention from consumers and scholars. Based on the different levels of information coverage, food labels can be divided into two types: product-level labels and ingredient-level labels. The product-level label refers to the label which is used to explain the overall characteristics and quality information of the food (including date label, health warning label, organic label, natural label, brand information, genetically modified organism label, eco label, and fair trade label). However, the ingredient-level label refers to the label that is used to display the specific nutritional information of the food (including nutrition facts panel, GDA label, low-fat label, health claim, traffic light label, health star rating, calorie menu label, shelf label). Further analysis and comparison showed that different types of food labels differ in influencing results, mechanism of action, and boundaries. Specifically, the product-level labels can arouse consumers' perceptions of safety, risk, and morality, and can effectively increase consumers' trust in products. At the same time, after purchasing products with such labels, consumers will show more food waste and repeated purchases. Ingredient-level labels, on the other hand, mainly affects consumers' perceptions of product health, as well as subsequent food choices and food intake. The theory of regulatory orientation helps to explain the different effects of the two types of food labels. The product-level labels more often initiate consumer preventive orientation, while the ingredient-level labels activate consumer promotion orientation. In addition, the two types of food labeling effects are driven by the halo effect, information processing, conceptual metaphor, social identity, attribute inference and other mechanisms. Besides, these effects are moderated by social demographic factors, individual differences, and product characteristics. On the one hand, combing and commenting on the effects of different food labels can provide reference for food manufacturers to carry out food marketing practices. On the other hand, through the construction of food label research framework in the field of marketing, it can point out the context and direction for marketing scholars to carry out empirical research on food label. Based on the overall framework of food labeling effects constructed in this paper, we propose that further research on the topic of food label can be carried out from following aspects in the future: (1) Expanding the behavioral results of ingredient-level labels; (2) Expanding the behavioral results of product-level labels; (3) Exploring the impact of different food label presentation forms on consumers; (4) Expanding the outer packaging labels and related research; and (5) Exploring the reversal mechanism of the negative effects of food labels. Related Articles | Metrics

Select

Attentional disengagement in autism spectrum disorders GAN Jiaqun, WANG Enguo Advances in Psychological Science    2022, 30 (1): 129-140.   DOI: 10.3724/SP.J.1042.2022.00129 Abstract （92）            Knowledge map    Autism spectrum disorder (ASD) is a kind of neurodevelopmental disorder that presents in childhood. Attentional disengagement is an essential part of the attentional orientation network. It refers to the process of disengaging attention from the original object to another one during an attention shift. In early childhood, the ability to engage and disengage attention is necessary for the development of social communication. Atypical patterns of disengagement may have adverse implications for emotional regulation, orienting, joint attention, and other abilities directly associated with socio-emotional functioning. The attentional disengagement suggests holds that early impairment of the attentional disengagement in ASD could substantially affect the development of the perception and cognitive capacity, which could then ultimately escalate into a number of impairments within the core clinical symptoms of ASD. Many studies have shown that high-risk infants (high-risk by virtue of having an older sibling diagnosed with ASD) who are later diagnosed with ASD have impairments in attentional disengagement at an early age, and some researchers believe that attentional disengagement deficits are an early behavioral marker for the diagnosis of autism in the future. Therefore, investigating the developmental characteristics and cognitive neural mechanisms of attentional disengagement in ASD individuals has practical significance for exploring the etiology, early diagnosis, and intervention of ASD. However, studies using the gap-overlap paradigm resulted in controversial findings regarding the abnormal attentional disengagement ability in individuals with ASD. Longitudinal studies of early high-risk infants had shown that individuals with ASD exhibited difficulty in attentional disengagement before being diagnosed. The ability of attentional disengagement in infancy could predict the development of ASD later in life. The more difficult the attentional disengagement, the more likely the development of ASD. Studies of older children, adolescents, and adults with ASD had yielded mixed results. Studies found slower, faster, and no group differences in the latency of attentional disengagement in individuals with ASD compared to those in the control groups. These inconsistent results may indicate that the attentional disengagement hypothesis cannot fully explain the development of attentional disengagement in ASD. Impairments in attentional disengagement for individuals with ASD may not emerge in the first year of life and may not continue into adulthood. This review summarizes the controversial research on the attentional disengagement of individuals with ASD in gap-overlap tasks and its relationship with the clinical symptoms of ASD. It is possible that the participants' characteristics, the research methods, and the stimulus properties are the main factors affecting attentional disengagement. This neural mechanism may involve the oculomotor nervous system, the frontal lobe, the parietal lobe, the cerebellum, and the corpus callosum. Additionally, based on previous studies, this paper proposes several possible trajectories for the development of attentional disengagement in individuals with ASD, that enrich the theoretical research in ASD. Finally, the future research directions of attentional disengagement in ASD are prospected. The evidence of inconsistent results in attentional disengagement in ASD individuals also underscores the importance of future research aimed at establishing whether there is an ASD-specific disengagement impairment. Future research should focus on research of brain mechanisms to map the trajectory of attentional disengagement in ASD over time. It is not only necessary to conduct a prospective longitudinal study on high-risk infants with ASD but also to consider the effects of participant characteristics, the heterogeneity of ASD, stimulus properties, and variability of measurement indexes and research methods on the results of attentional disengagement comprehensively, as well as further clarify its role in the early prediction and recognition of autism. Furthermore, other behavioral manifestations or early risk markers should be combined for comprehensive diagnosis and identification, so as to develop targeted early intervention strategies and programs for high-risk infants. Related Articles | Metrics

Select

From solicitation to responses: Managers' roles in employee voice behavior chain SHI Lixiaoyun, ZHU Yue, DUAN Jinyun Advances in Psychological Science    2022, 30 (1): 206-215.   DOI: 10.3724/SP.J.1042.2022.00206 Abstract （91）            Knowledge map    As an important form of employee engagement in organizational decision-making, voice has an unignorable influence on employees themselves, managers and organizations. Since Hirschman raised the concept of voice, scholars have contributed to exploring the antecedents and consequences of employee voice, managerial evaluation and endorsement, as well as voice solicitation. Moreover, accumulating attention has been paid to the managerial roles in promoting employee voice and amplifying the positive effects of voice. However, research on managerial roles in voice remains unsystematic and leaves lots of issues to be addressed. Based on Input-Process-Output (IPO) model, we summarize managerial roles in the voice process, as well as their premises and outcomes. At first, we integrate voice solicitation, employee voice, and managers' reactions toward voice (i.e., managerial endorsement and voice/voicer evaluation) into a comprehensive concept, entitled as voice behavior chain. Voice behavior chain reflects the Process component of the IPO model and portraits managerial roles in the voice contexts. In the voice behavior chain, as initiators, managers consult employees about their work-related ideas and opinions, which can facilitate employee voice. Then, as reactors, managers would decide whether to endorse employee voice or not, and meanwhile, they also evaluate voice and corresponding voicers. However, it is notable that voice behavior chain could be incomplete; that is, except for employee voice, neither of the managerial roles is a must to constitute the chain. The order of the elements within the behavior chain may also change under certain circumstances. Moreover, we provide fine-grained arguments about the distinctiveness and connections between voice solicitation and employee voice and between managerial endorsement and voice/voicer evaluation. We also conclude that in the IPO model, voice behavior chain shares similar Input and Output components (i.e., similar antecedents and consequences), which are across individual-level, team-level and organizational-level. Regarding the Input component, managers' affective states, openness toward change, motivations, self-regulatory resources, self-efficacy, and face threat directly influence their performance in voice behavior chain. Managerial endorsement and evaluation also depend on employees' communication skills, credibility and expertise. Team-level (e.g., power distance, leader-member exchange, goal consistency and the involvement of third party) and organizational-level factors (e.g., change climate and voice norms) can also activate voice behavior chain. Since researchers tend to adopt self-regulatory theory or image/ego threat theory to trace the sources of voice behavior chain, we call for further studies to consider other potential theoretical explanations and input factors, such as managers' attributions. Regarding the Output component, voice behavior chain may have effects on individual perceived organizational support, organizational commitment, performance and promotion. It also serves as a catalyze to promote team performance and problem-solving skills and to build organizational creative climate. However, the managerial roles cannot be limited in strengthening the effects of employee voice. For instance, managerial solicitation may induce managers' emotional exhaustion if their proactive behaviors aren't followed by employee voice and support. Drawing on the IPO model in the voice context, we emphasize the managerial roles in voice behavior chain. Thereby this study comprehensively incorporates the existing literature, identifies important issues neglected by previous research and sheds light on future studies. Researchers need to be more scrupulous of the application of the related theory when conducting cross-cultural research. Also, the constructs and dimensions of the behavior elements can be explored furtherly, and we are looking forward to more objective measurements of managerial roles. Last but not least, the research on managerial roles in voice behavior chain can be enriched by investigating spillover effects and corresponding mechanisms. We hope this review can make theoretical and practical contributions by integrating those scattered topics and guiding managers to facilitate and respond to employee voice efficiently. Related Articles | Metrics

Select

Discriminating the concepts of goal and its influence on decision-making HE Jiamei, JIN Lei Advances in Psychological Science    2021, 29 (8): 1410-1419.   DOI: 10.3724/SP.J.1042.2021.01410 Abstract （266）            Knowledge map    Decision-making is the process that individuals evaluate and make a choice among multiple alternatives, in order to maximize their possible benefits. Individuals usually think about and predict what may happen in the future and set reasonable goal to guide their decisions. The goal is not only the reason why the decision-maker makes their choice, but also the ideal result that the decision-maker anticipates to achieve. Individual decision making under the guidance of a goal involves two mental processes, such as setting a goal and accomplishing the subsequent decision task. Based on goal's content, motivational function, and other features, previous studies believe that the goal is a future result that individuals anticipate to achieve and it has motivational effects on their cognition and behavior. However, only one future result that individuals intended to achieve was discussed in previous researches. Usually, individuals want to attain multiple goals at the same time, potential conflict of interest among these future results might exist. So, in the first step, individuals have to make a choice among these future results that they wish to achieve at the same time. The one that is chosen would possess motivation power to arouse individuals' cognitive process and behavior. Therefore, goal is the winner of the competition for motivation. On the basis of the evaluation of their desirability and feasibility, specific future results are selected as the goal and endowed with motivation by the decision-maker. Behavior habits, personality traits, and life experience affect individuals' goal setting. For example, high construal level helps decision makers to recognize better their goal and focus their attention on decision scheme that is conducive to the goal's achievement. Among decision-makers with high trait self-control, a temptation has lower subjective value, compared with the goal. Individuals with high trait self-control would have less intention to approach the temptation and might experience fewer conflicts between the goal and the temptation. The asymmetric effect between temptation and goal caused by the successful experience in executing self-control has changed the value of the future results assigned by the decision-maker. The cues related to the temptation can activate the goal while they inhibit the temptation among individuals with high trait self-control. The goal can influence decision-making by changing the decision-maker's attitude and selective attention towards decision scheme in accomplishing subsequent decision tasks. In the process of achieving a goal, decision-makers are more likely to overestimate the positive emotions related to the goal. Therefore, positive anticipation would be endowed on the decision scheme that is benefit for the goal. The activated goal can drive selective attention and allocate attention resources to benefit the goal achievement. Moreover, selective attention increases the time duration that the decision-maker pays their attention on its corresponding decision scheme. It further helps the decision maker to improve its subjective value estimation. In the future, it is necessary to investigate how an unconscious goal weakens the effect that it has on negative consequences and how to measure the two mental processes directly. Related Articles | Metrics

Select

Empathy interventions for individuals with autism spectrum disorders: Giving full play to strengths or making up for weaknesses? HUO Chao, LI Zuoshan, MENG Jing Advances in Psychological Science    2021, 29 (5): 849-863.   DOI: 10.3724/SP.J.1042.2021.00849 Abstract （66）            Knowledge map    The empathy deficits in individuals with autism spectrum disorders (ASD) may cause their social interaction barriers. Therefore, it’s very important to intervene the empathy ability of individuals with ASD. Some researchers proposed the empathy method of “making up for weaknesses”, indicating a direct intervention for the empathy deficits of individuals with ASD, including the theory of mind (TOM) intervention, the perspective-taking intervention, the intervention of facial expression cognition, and so on. However, others thought although individuals with ASD had empathy deficits, their systematic capability was excellent. Therefore, the empathy ability of individuals with ASD should be improved through their innate advantage of systematic capability, called the empathy method of “giving full play to strengths”, mainly including the lego therapy, the serious games intervention and the island-based intervention based on systemizing theory. All of the methods mentioned above had their own advantages and disadvantages. Finally, the reflection and prospect on the problems existing in the field of empathy interventions for individuals with ASD were put forward. Related Articles | Metrics

Select

EMMN varies with deviant-standard stimulus pair type and emotion type: Evidence from a meta-analysis study ZENG Xianqing, XU Bing, SUN Bo, YE Jiantong, FU Shimin Advances in Psychological Science    2021, 29 (7): 1163-1178.   DOI: 10.3724/SP.J.1042.2021.01163 Abstract （53）            Knowledge map    The automatic detection of facial emotion changes is crucial for survival. Numerous studies using event-related potential (ERP) technique have found that the amplitude of emotion-related visual mismatch negativity (EMMN) could be used to test the automatic processing of facial emotion. Previous studies suggested that deviant - standard stimulus (D-S) pair (different/same) and emotion type (negative/non- negative) might modulate the EMMN effect, however, the evidence so far was mixed. Therefore, we conducted a meta-analysis to analyze the findings of 35 studies (involving 721 healthy participants) on EMMN. Results showed that: (1) EMMN effects emerged at both the early- (0~200 ms) and late- (200~400 ms) stages, demonstrating that infrequently presented deviant stimulus elicited more negative ERPs at both the early- and late-stages. This suggests that EMMN reflects the probability effect of early- and late- stages emotion-related ERP components; (2) the type of D-S pair moderated the EMMN effect at the early- but not the late-stages. Specifically, the EMMN effect of different D-S pairs was significantly larger than that of the same D-S pairs at the early-stage; (3) in the studies of same D-S pairs, the evidence between equiprobable and non-equiprobable paradigm showed no significant differences in EMMN at both stages; (4) a negative bias was found in both early and late EMMN, i.e., the EMMN elicited by the angry, fearful, angry faces was significantly larger than that of happy faces. These results indicate that the EMMN effect is affected by experimental manipulations such as D-S pair type and emotion type. Related Articles | Metrics

Select

Cognitive neural mechanisms underlying the impact of oxytocin on fear acquisition and extinction FENG Pan, YANG Ke, FENG Tingyong Advances in Psychological Science    2022, 30 (2): 365-374.   DOI: 10.3724/SP.J.1042.2022.00365 Abstract （167）            Knowledge map    Fear is a biologically adaptive response to environmental threats, and fear learning plays a key role in adaptive function. However, maladaptive fear learning underlies emotional disorders, such as anxiety and posttraumatic stress disorder (PTSD). Together with the development of cognitive neuroscience and the integration of multidisciplinary research, the study on the cognitive neural mechanism of fear has become a hot topic in the field of emotion. Using the classical fear conditioning paradigm, researchers have identified the brain circuits of fear learning and extinction. Specifically, extensive imaging researches have revealed several key regions involved in fear acquisition, including the amygdala, insula, dorsal anterior cingulate (dACC) and thalamus. Moreover, the amygdala, hippocampus and ventromedial prefrontal cortex (vmPFC) served key roles in fear memory consolidation and reconsolidation, and the amygdala, hippocampus, vmPFC, and dACC are required for fear extinction. Cumulative evidence has suggested that oxytocin plays a crucial role in the process of fear acquisition, fear consolidation and fear extinction. Therefore, firstly, we summarized the paradigms of fear acquisition and fear extinction as well as the cognitive neural mechanisms of fear acquisition and fear extinction based on the fingdings of corresponding meta-analyses. Secondly, we focused on the cognitive neural mechanisms underlying the impact of oxytocin on fear acquisition and fear extinction. Next, we summarized the neurobiological circuits of oxytocin influence on fear emotion processing. Finally, we prospected the future researches on the cognitive neural mechanisms underlying the impact of oxytocin on fear processing. The present study sheds insights into the cognitive neural mechanisms underlying the impact of oxytocin on fear processing. Moreover, the present study provides an potential treatment for the fear-related disorders. Oxytocin has been shown to facilitate fear acquisition as it affects brain activity in several regions including amygdala, prefrontal cortex, anterior cingulate gyrus, insula and hippocampus, as well as the functional connectivity between them. Oxytocin also enhances fear extinction by regulating amygdala and medial prefrontal cortex activity, as well as enhancing the functional connectivity between prefrontal cortex and amygdala. Furthermore, oxytocin can regulate the activity of the amygdala, anterior cingulate gyrus, insula, hippocampus, vmPFC and other fear-related brain regions, thus affecting the processes of fear acquisition and extinction. Specifically, cumulative evidence has indicated that intranasal oxytocin attenuates amygdala (hyper)activity and enhances functional coupling of the amygdala with the vmPFC and hippocampus, resulting in increased top-down control over the fear response. In addition, intranasal oxytocin has also been found to attenuate amygdala—brainstem connectivity and to change activity and connectivity in nodes of the salience network (i.e., insula and dACC). Furthermore, oxytocin administration may enhance social behavior through modulating the hypothalamus—pituitary—adrenal (HPA) axis and autonomic nervous system (ANS) function, which may provide a potential treatment for the fear-related disorders. However, it should be noted that the dose, time and location of oxytocin injection might have different effects on the processes of fear acquisition and extinction. Future studies should focus on gender differences, neural network underlying the impact of oxytocin on fear consolidation and reconsolidation and the pathological study examining oxytocin effect on fear emotion processing to better reveal the cognitive neural mechanisms underlying the impact of oxytocin on. Related Articles | Metrics

Select

Neural mechanism underlying the perception of crowd facial emotions HE Weiqi, LI Shuaixia, ZHAO Dongfang Advances in Psychological Science    2021, 29 (5): 761-772.   DOI: 10.3724/SP.J.1042.2021.00761 Abstract （65）            Knowledge map    How to reveal the cognitive neural correlates underlying emotional face processing has always been the popular topic for psychology and social neuroscience. Previous studies mainly used single facial expressions as the stimuli to induce and present emotions, but there is still a lack of attention and the investigation for the crowd emotion. Thus, the present project plans to reveal the neural correlates underlying the processing of crowd emotional faces through the combination of behavioral, ERP, fMRI, and TMS experiments, in which the emotional information (valence and intensity), face direction (frontal, lateral and inverse view), integrity (partial presentation, complete presentation) and spatial frequency (complete, high frequency, low frequency) will be investigated to reveal the temporal dynamics and brain activation patterns of the processing of crowd facial expressions. Finally, our work will be expected to beneficially serve the comprehensive understanding of the nature of the perception and recognition of crowd emotion, which is also of practical significance towards updating social interactions. Related Articles | Metrics

Select

Wisdom in old age CHEN Haobin, WANG Fengyan Advances in Psychological Science    2021, 29 (5): 885-893.   DOI: 10.3724/SP.J.1042.2021.00885 Abstract （302）            Knowledge map    Wisdom is a general psychological quality that integrates morality and intelligence. It is learned from life experiences, serves as an important symbol of successful aging, and is the ideal endpoint of human development. Research has shown that self-reported measurements, such as the three-dimensional wisdom scale (3D-WS), self-assessed wisdom scale (SAWS), and the adult self-transcendence inventory (ASTI), and performance measures such as the Berlin wisdom paradigm (BWP) and wise reasoning (WR), perform well in the assessment of older adults’ wisdom. The development of wisdom in old age is influenced by internal factors such as openness, self-reflection, emotion regulation, and personality growth, as well as external factors such as education level, critical life experiences, and the social environment. In older adults, wisdom obtained from life experience improves well-being and life satisfaction, and reduces social alienation, loneliness, and depression. Future research should develop multi-faceted and integrated tools for the evaluation of older adults’ wisdom, to further investigate the predictive factors, effects, and internal mechanisms of wisdom in old age, and to explore the intervention and cultivating strategies of older adults’ wisdom in the community care services. Related Articles | Metrics

Select

How does massive information affect intertemporal choice? A theoretical perspective based on attentional resources LI Aimei, CHE Jingshang, LIU Nan, SUN Hailong, ZHOU Wei Advances in Psychological Science    2021, 29 (9): 1521-1533.   DOI: 10.3724/SP.J.1042.2021.01521 Abstract （73）            Knowledge map    People are prone to short-sighted and unable to make high-quality decisions when too little information is available. At the meantime, too much information also backfires which go against far-sighted decisions. The literature has indicated too much information expends attentional resources which are detrimental for making far-sighted choice. However, the underlying mechanism of how massive information affects intertemporal decision-making remains unclear. Based on the theoretical perspective of attentional resources, we propose that too much information exacerbate the consumption of attentional resources which leads to a preference for short-sighted choices and two reasons account for that. On the one hand, the massive information presented to us captures a lot of attention resources which lead to too little attention resources left for future relevant event. As a result, people fail to simulate and predict future accurately, and form less intention for the future. On the other hand, when there is too much information to process, people are more concerned about the urgency of the situation and are reluctant to plan for the long-term future. Meanwhile, we assume future-orientation buffers the adverse effect of massive information on intertemporal choice, by focusing individuals on long term benefits. The research shed light on how, why, and when massive information influence intertemporal choice, and provide theoretical and empirical insights for nudging foresighted decision in a massive information environment. Related Articles | Metrics