Against the scourge of the COVID-19 pandemic, college students’ learning engagement has become a key issue in universities and society. Guided by the theories of existential positive psychology and social perception, we explored the positive effect of a growth mindset on learning engagement during the COVID-19 pandemic. A total of 1,040 college students from universities in Henan Province of China effectively completed online questionnaires. The results showed that growth mindset was positively related to learning engagement and negatively associated with perceived (...) COVID-19 event strength and perceived stress; perceived COVID-19 event strength was positively related to perceived stress, while perceived COVID-19 event strength and perceived stress were negatively associated with learning engagement. Growth mindset affected learning engagement through three indirect paths: the mediating role of perceived COVID-19 event strength, the mediating role of perceived stress, and the serial mediating role of both perceived COVID-19 event strength and perceived stress. The results indicated that the growth mindset could contribute to college students’ learning engagement through the roles of perceived COVID-19 event strength and perceived stress during the COVID-19 pandemic. This study advances the understanding of the mechanism underlying the relationship between growth mindset and college students’ learning engagement during the COVID-19 pandemic. Furthermore, the findings of the study have important implications for promoting college students’ learning engagement during the pandemic. (shrink)

With the growth of online commerce, companies have created virtual communities where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature (...) has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs. (shrink)

Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time (...) series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components. Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries. (shrink)

Information on age-related differences in affective meanings of words is widely used by researchers to study emotions, word recognition, attention, memory, and text-based sentiment analysis. To date, no Chinese affective norms for older adults are available although Chinese as a spoken language has the largest population in the world. This article presents the first large-scale age-related affective norms for 2,061 four-character Chinese words (AANC). Each word in this database has rating values in the four dimensions, namely, valence, arousal, dominance, and (...) familiarity. We found that older adults tended to perceive positive words as more arousing and less controllable and evaluate negative words as less arousing and more controllable than younger adults did. This indicates that the positivity effect is reliable for older adults who show a processing bias toward positive vs. negative words. Our AANC database supplies valuable information for researchers to study how emotional characteristics of words influence the cognitive processes and how this influence evolves with age. This age-related difference study on affective norms not only provides a tool for cognitive science, gerontology, and psychology in experimental studies but also serves as a valuable resource for affective analysis in various natural language processing applications. (shrink)

In order to systematically evaluate perceptual anticipation between experts and non-experts for different kinds of combat sports, we needed to perform a comprehensive assessment. In this systematic review and meta-analysis, we searched four English-language and three Chinese-language databases that used expert/non-expert research paradigms, to explore perceptual anticipation in combat sports. We employed a random effects model for pooled analyses using the inverse variance method. We included 27 eligible studies involving 233 datasets in this meta-analysis. We observed large effect sizes for (...) the differences between experts and non-experts in both response accuracy and reaction time. We also observed substantial differences between experts and non-experts in the mean duration of visual fixations per trial, but not in the visual fixation duration. Taken together, high-level combat athletes have more advantages in perceptual anticipation than lower-level athletes, showing faster and more accurate responses when facing the opponent's attacks, as well as focusing on fewer points of visual fixations than novice athletes. Different types of combat sports and stimulus presentations affect perceptual anticipation abilities to varying extents in relation to outcome measures, with more pronounced expertise in a stimulus that is closer to real-world situations.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021226343, PROSPERO CRD42021226343. (shrink)

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative “shrinkage” or “small variance” priors while extending prior work on the general cross-lagged panel model. Using a panel dataset of national income and subjective well-being we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each (...) other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood. This is useful because, second, these priors increase model parsimony and the stability of estimates and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern. (shrink)