Min Huang

To study the different mechanisms of understanding figurative language in a speaker’s native language and their second language, this study investigated how scientific metaphors in Chinese and English are electrophysiologically processed via event-related potential experimentation. Compared with the metaphors from daily life or in literary works, scientific metaphors tend to involve both a more complicated context structure and a distinct knowledge-inferencing process. During the N400 time window, English scientific metaphors elicited more negative N400s than Chinese ones at the parietal region. In the late positive component time window, English scientific metaphors elicited less positive LPCs than Chinese ones at the parietal region, and larger late negativities encompassing smaller areas of the brain. The findings might indicate that for late unbalanced bilingual speakers, L2 scientific metaphor comprehension requires more effort in information retrieval or access to the non-literal route. Altogether, the possible findings are that non-native and non-dominant language processing involves decreased automaticity of cognitive mechanisms, and decreased sensitivity to the levels of conventionality of metaphoric meanings.

Building on the literature on optimal distinctiveness, this study explores the differential impacts of normative versus strategic corporate social responsibility (CSR) practices on social evaluations. We argue that normative CSR, which entails conformity in the scope of CSR, mitigates negative social evaluations, whereas strategic CSR, which emphasizes differentiation in CSR practices, enhances positive social evaluations. Additionally, we examine the moderating factors influencing these relationships. Using the case of corporate social activities of Chinese listed firms during the period from 2006 to 2019, our results show that the mitigating effect of normative CSR on negative evaluations is more pronounced in firms with higher reputational risk. Conversely, the relationship between strategic CSR and positive social evaluations is stronger among firms with lower levels of peers' strategic CSR. Furthermore, we demonstrate that negative and positive social evaluations serve as mechanisms through which normative and strategic CSR, respectively, influence market outcomes. These findings contribute to the optimal distinctiveness theory and advance research on CSR strategy.

This study examines the influence of Confucianism on corporate poverty alleviation (PA) participation. We argue that firms in regions with more Confucian temples are more likely to participate in government-initiated PA programs because Confucianism emphasizes common social welfare. This positive relationship is stronger for firms with chief executive officers born in Confucian regions and for firms that are under high media pressure, as the trade-off between social welfare and firm interest is in favor of Confucianism. Using a sample of Chinese-listed firms, we find evidence supporting our arguments. Moreover, we find consistent evidence that firms influenced by Confucianism are motivated to pay long-term attention to PA activities and improve corporate PA efficiency, highlighting the substantive role of Confucianism in a PA campaign. Our study contributes to the business ethics literature by introducing Confucianism into research on corporate social activities and provides practical implications.

This study seeks to shed light on the effect of star CEOs on the environmental, social, and governance (ESG) performance of Chinese firms. Relying on the theoretical perspective of role identity and role constraints, we analyze data from 1222 Chinese firms listed on the Shanghai and Shenzhen Stock Exchanges from 2006 to 2019. The results analyzed using the ordinary least squares estimate method reveal a positive effect of star CEOs' extreme confidence and legitimacy pressure mechanisms on ESG performance. We also find that this positive relationship is negatively moderated by higher organizational slack but strengthened by a lower level of regional marketization, as the degree of firms' dependence on external stakeholders' resources differs significantly. Additionally, this study finds that ESG activities are less likely to be associated with firm performance if star CEOs pursue them to satisfy their need for star status. These findings supplement the early tests of the influence mechanism of star CEOs on organization-level results and expand the context discussion of role identity and role constraints theory in the Chinese institutional environment.

The role of the two hemispheres in processing metaphoric language is controversial. In order to complement current debates, the current divided visual field study introduced scientific metaphors as novel metaphors, presenting orientation mapping from the specific and familiar domains to the abstract and unfamiliar domains, to examine hemispheric asymmetry in metaphoric processing. Twenty-four Chinese native speakers from science disciplines took part in the experiment. The participants were presented with four types of Chinese word pairs: scientific metaphors, conventional metaphors, literal word pairs, and unrelated word pairs. The first word in each pair was presented centrally, and the second was presented to the left visual field or the right visual field. Event-related potentials were recorded when participants read the target words and judged whether words in each pair were related. The data demonstrated that both hemispheres were involved at the initial stage of metaphor processing, but the right hemisphere took a more privileged role. The significant activation of the left hemisphere for scientific metaphoric processing supports the fine-coarse coding hypothesis. During right-visual-field presentation, the left hemisphere, responsible for the processing of closely related domains, has to integrate the loosely associated domains of scientific metaphor, which greatly increased cognitive taxes. Moreover, the data of late positive components revealed different hemispheric activation between scientific metaphors and conventional metaphors. Compared with literal pairs, conventional metaphors elicited significantly higher LPCs during right visual field presentation, while the scientific metaphor elicited significantly lower LPCs during left visual field presentation. These results suggest different processing mechanisms between novel metaphors and conventional metaphors and the special role of the right hemisphere in novel metaphoric processing at the later mapping stage.

With the rapid development of science and technology, UAVs have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm, Gradient Boosting Decision Tree algorithm, K Nearest Neighbor algorithm, and Decision Tree algorithm. Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.

Reinforcement learning relies on the reward prediction error (RPE) signals conveyed by the midbrain dopamine system. Previous studies showed that dopamine plays an important role in both positive and negative reinforcement. However, whether various reinforcement processes will induce distinct learning signals is still unclear. In a probabilistic learning task, we examined RPE signals in different reinforcement types using an electrophysiology index, namely, the feedback-related negativity (FRN). Ninety-four participants were randomly assigned into four groups: base (no money incentive), positive reinforcement (presentation of money rewards), negative reinforcement (removal of money losses), and combined reinforcement (money rewards and removal of money losses) groups. In addition, in order to evaluate the engagement of emotional activity in the different reinforcement processes, Positive and Negative Affect Schedule-Expanded Form (PANAS-X) scales were applied before and after the experiment to detect the emotional changes. The results showed that there was no difference between groups in the dopamine-related learning bias. However, compared to the other three groups, negative reinforcement elicited smaller FRN (the difference-wave measure) during the learning, stronger positive affect and joviality, and less fatigue after the learning, in which the difference between the negative and positive reinforcement groups was smaller. The results indicated that pure avoidance motivation may induce distinct emotional fluctuations, which influence the feedback processing.

Firms worldwide are increasingly required to disclose their carbon emissions due to the environmental damage associated with climate change. Because there has been no previous literature focusing on the determinants of corporate carbon disclosure integrating environmental legitimacy and green innovation, the present study attempted to develop an original framework to fill the research gap. This study explored the influence of environmental legitimacy on corporate carbon disclosure, and investigated the role of green innovation as a mediator. With the samples of Carbon Disclosure Project in China from 2008 to 2012, the results demonstrate that environmental legitimacy significantly negatively influences the likelihood of corporate carbon disclosure, and that green process innovation mediates the relationship, while green product innovation has no significant mediating effect. It means that environmental legitimacy not only directly affects the likelihood of corporate carbon disclosure, but also indirectly affects it via green process innovation. Hence, companies must increase both informal and formal mechanisms, i.e., external environmental legitimacy and internal green process innovation, to engage in carbon information disclosure and ensure sustainability.