Music is the crystallization of human culture, characterized by its diversity and complexity specific to human society. As an auditory art form, music essentially consists of a structured sequence of sounds unfolding over time. Every musical composition is composed of varying lengths of sounds and rests, with these sound events always combining in alternating patterns, forming the basis for repetition and development. The concept of metric structure refers to the repetitive organization of strong and weak beats within the sequence of sounds. However, due to the complexity of temporal organization in music, metric structure remains a challenging aspect in the study of music cognition. Metric structure is the temporal framework of music. It is not only the basis for composers to create music, but also the prerequisite for people to process musical aesthetics, musical emotion and musical meaning.
This study integrates predictive coding theory with the processing of metric structure, exploring how musical structural information is integrated and updated over time within a framework of prediction errors. Forming structured internal representations based on existing information is fundamental and prerequisite for perceiving musical metric structure. Once these structural representations are formed, listeners predict upcoming musical events. Upon the occurrence of a musical event, listeners assess whether it aligns with their predictions, integrate it into their existing musical context, and adjust their structural representations based on whether the expectation was met or violated, thereby enhancing future predictions. Therefore, prediction and integration are two indispensable stages in the brain's processing of metric structure. They are interdependent and mutually influential, reflecting an iterative process of autonomous metric structure processing in the brain. Hence, the proposed project explores the cognitive and neural mechanisms underlying the prediction and integration of metric structure, using behavioral experiments and electroencephalogram techniques.
Specifically, it includes the following four studies: (1) track the dynamic neural activity during the representation construction of metric structure and the prediction establishment as rhythmic sequences unfold, (2) explore how listeners use prediction errors to update the prediction of metric structure, (3) examine the neural mechanisms underlying the integration of multiple hierarchical metric structure at the phrase level and (4) investigate how listeners integrate nested metric structure according to long-distance dependency at the period level.
This study reveals the general cognitive mechanism of the processing of musical structure and lay the foundation for the construction of cognitive neural model of music. Firstly, based on predictive coding theory, this paper examines the neural responses preceding the occurrence of musical events by focusing on the anticipatory phase. It provides direct neural evidence for the pre-activation stage of expectation formation. Additionally, it distinguishes between the stages of expectation formation and expectation updating, investigating the role of prediction errors in updating metric structure expectations. The study explores how prediction errors regulate the latter stage, revealing the neural mechanisms underlying the updating of metric structure expectations. Secondly, we investigate how listeners integrate nested metric structure within long-duration musical units based on long-distance dependency relationships. It reveals the processing mechanisms of metric structure within complex musical organizations. Thirdly, through the design of musical materials, not only controls for the interference of psychoacoustic factors but also enhances ecological validity in our study.
In summary, within the framework of predictive coding theory, this paper focuses on the processing of metric structure, with four studies forming an integrated whole that collectively addresses the scientific questions regarding the cognitive mechanisms of musical metric structure. A robust theoretical foundation ensures the feasibility of the research. The experimental materials are adapted from real musical works but undergo rigorous manipulation and control, ensuring internal validity and ecological validity of the research outcomes. EEG allows for precise capture of real-time brain responses to each note change during the unfolding of music, making it a feasible and necessary method for exploring the brain mechanisms and dynamic processes involved in processing musical metric structure. This study not only contributes to revealing the nature of music structure cognition and laying the groundwork for constructing neural models of music cognition, but also provides objective evidence for music appreciation and aesthetics research, with promising potential applications in the field of music.

Blended teaching, a new teaching model that integrates network technology with traditional face-to-face teaching, has gradually developed into a new typical teaching method in higher education. However, the complexity of elements in the blended teaching environment and the diversity of the learning environment have created significant challenges for improving blended teaching. Cognitive engagement refers to learners’ mental efforts and the degree to which cognitive strategies are applied, which is the critical factor affecting learning performance in blended teaching. Improving students’ cognitive engagement is essential for advancing blended teaching and achieving an optimal effect. However, cognitive engagement has increased the difficulty of measurement and intervention because of its implicit nature. Furthermore, problems such as solid subjectivity and single modes in past evaluation methods make it challenging to clarify students’ cognitive engagement. A blended classroom has the unique characteristics of complex elements and diverse learning contexts, and the method of measuring students’ cognitive engagement differs from that used in a single teaching environment. In addition, the internal connection based on knowledge experience between the individual online and offline learning processes cannot be ignored. Students have specific knowledge preparation when they enter the offline classroom.
Our aim is to improve students’ cognitive engagement in blended classrooms. First, it was necessary to establish a scientific measurement index system of students’ cognitive engagement. The blended classroom stage (i.e., online or offline) and process (i.e., the internal connection of knowledge in online self-learning and subsequent offline face-to-face learning) are considered to establish effective strategies for improving cognitive engagement. The practical goal is to develop appropriate teaching strategies and a digital teaching tool to improve students’ cognitive engagement in actual blended classrooms.
Specifically, in Study 1, a quantitative representation model of cognitive engagement in a blended classroom will be constructed by collecting multimodal data of learners in actual blended classrooms at different stages and integrating text and video analyses, eye tracking and psychometric indicators. Grey relational analysis and the entropy method will be used to calculate the weights of the evaluation index system. Study 2 will explore strategies for improving students’ cognitive engagement in blended classrooms from the perspectives of learning resources and instructional strategies. A series of empirical studies will be carried out to identify strategies for improving learners’ cognitive engagement in online, offline, and blended classroom processes. The roles of learning resources and generative learning strategies will be investigated in the online stage. Based on the process perspective of the internal connection between online self-learning and offline face-to-face learning stages, we will explore how to combine student problem-generating and teacher problem-scaffolding strategies effectively. We will also learn how to provide teacher feedback to learners with different cognitive levels. The effects of teacher feedback and student reflection on cognitive engagement will be investigated in an offline classroom setting. Thus, practical strategies for improving cognitive engagement can be developed according to the gold standard from empirical research. In Study 3, the effective teaching strategies proposed in the second study will be verified by tracking them in an actual blended classroom. Simultaneously, collaborative changes in instructional strategies and students’ cognitive structures can be obtained through a longitudinal cognitive network analysis, which will help form empirical evidence in the teaching environment. For Study 4, based on the research above, using multimodal data in the blended classroom and applying the neural network algorithm, we will establish a cognitive engagement classifier. By combining the trigger mechanism of instructional intervention strategies and classifier, the study will form a teaching assistant tool with the function of cognitive engagement identification and intervention strategy reminders in the blended classroom. We intend to utilize the cross-integration of information technology and empirical and applied research in educational psychology to realize an effective combination of scientific research and practical applications in blended teaching.

The concept of successful aging at work (SAW) offers new insights for organizations seeking to develop their elderly human resources. Extant studies have yet to conceptualize SAW clearly, neglected the influence of intergenerational factors, and little attention was paid to its outcome variable. To address these research gaps, we plan to conduct three correlated studies: Study 1 plans to draw on the perspective of intergenerational interaction and re-explore the concept and dimensions of SAW. We build a five-dimensions model of successful aging at work (i.e. career development, continuous goal engagement, work attachment, caring for younger generations, positive emotions maintaining), which is helpful for the development of a measurement scale for successful ageing at work that adapted to the Chinese context. By doing that, we will provide a new measurement for the empirical studies and move this line of research forward.
Study 2 plans to examine the relationship between intergenerational knowledge sharing and SAW, and further reveal the dual-path mediation mechanism of career adaptability and age-diverse friendship. Based on the perspectives of person-fit theory, we propose that intergenerational knowledge sharing, a way of intergenerational interaction between young and older employees, could help older employees maintain a continuous individual-environment fit (i.e. career adaptability and age-diverse friendship), which in turn can have a positive impact on successful aging at work. Moreover, the Actor-partner interdependence model (APIM) was constructed to test the actor and partner effects of intergenerational knowledge sharing on SAW. By doing that, we reveal the influence of intergenerational knowledge sharing on successful aging at work through career adaptability and age-diverse friendship, with the moderating effect of age-inclusive human resource practices in the above relationship, which provides new insights to the research on the mechanism of successful aging at work.
Although study 2 extend the research on the antecedents of successful aging at work, we know little about how older workers’ successful aging at work exert an influence their younger coworkers. Study 3 thus draw on the cognitive appraisal theory of stress to investigate the double-edged sword effect of SAW on younger employees’ work engagement. More specially, we propose that, on the one hand, older employees who have successfully aged can serve as role models for younger employees, prompting them to develop challenging stress perceptions, and then increase their work engagement. On the other hand, older workers who have successfully aged occupy higher positions and have more resources, which may lead to threatening stress perceptions among younger employees, which in turn has a negative impact on the work engagement of younger employees. In addition, we propose the moderating effect of learning goal orientation on the relationship between successful aging and self-efficacy. Employees with a high learning goal orientation are oriented towards personal development and skill acquisition, and they are more likely to challenge these older employees because they contribute to the achievement of their learning and growth goals. Age stereotypes is proposed to moderate the relationship between successful aging and relative deprivation at work. Based on the theory of stress cognitive assessment, we argue that younger employees with strong stereotypes about older employees are more likely to conduct a threat assessment of older employees who have successfully aged at work, because older employees occupy organizational resources and encroach the development of younger employee, resulting in a stronger sense of relative deprivation.
In conclusion, we plan to develop new scale of successful aging at work by drawing on a novel perspective-intergenerational interaction perspective. Then, we will elucidate the impact of intergenerational knowledge sharing on SAW and its underlying mechanisms. Finally, we plan to extend the literature on successful aging at work by examining its influence on their younger coworkers’ work engagement. Overall, we extend the investigation of the consequences of SAW, thereby contributing to the theoretical framework of SAW and promoting organizational diversity management.

The advancement of digital technologies has promoted online work. Online work has progressively emerged as a prevalent work pattern. Although physical separation from the organization grants employees a high degree of autonomy, it erodes leaders’ sense of control and engenders uncertainties. How leaders maintain control in virtuality and thus maintain managerial effectiveness has become a hot research topic recently. An act that leaders may engage to strengthen control in an online context is online territorial behavior. Little research has integrated “online work” with “territorial behavior,” which limits the understanding of online control and manifestations of online leader territorial behavior.
Research on online territorial behavior is in its infancy. This research highlights leaders’ manifestations of perceived possessiveness over their employees in an online context. This research proposes four studies to: (1) conceptualize and develop a measurement for online leader territorial behavior; (2) discuss the association between online work and online leader territorial behavior; (3) explore the impacts of online leader territorial behavior on employee and leader outcomes and the mechanisms underlying those impacts; and (4) highlight how digital human resource management weakens the negative impact and strengthens the positive impact of online leader territorial behavior. The four studies contribute to a clearer understanding of the conceptualization, measurement, antecedents, and consequences of online leader territorial behavior.
This research contributes to the existing literature in several ways. Studying leader territorial behavior in an online context expands its conceptualization and application. The online leader territorial behavior measurement to be developed can be used for drawing theoretical and empirical links with other variables. By extending leader territoriality expression from physical to virtual spaces, this research echoes recent studies on e-leadership.
This research provides a systematic framework for understanding online leader territorial behavior. This research compares offline and online leader territorial behaviors. This research delineates offline and online “marking” and “defending” and proposes four sets of online leader territorial behavior: hands-on marking, hands-on defending, delegated marking, and delegated defending. This conceptualization deepens the understanding of leader territoriality expression in an online context and encourages future research to explore online leader control manifestations and particular territorial behaviors in various contexts.
This research highlights that employee online work can give rise to online leader territorial behavior. Addressing leader cognitive and emotional changes when shifting from the offline to the online context, this research identifies leader uncertainty perceptions and fear of territory loss as mechanisms underlying the relationship between employee online work and online leader territorial behavior. This provides a nuanced perspective for exploring the origins of territorial behavior and broadens knowledge about leader behaviors in an online context.
This research proposes a double-edged-sword effect of online leader territorial behavior. Online leader territorial behavior enhances leader work-related motivation by satisfying leader need for control in virtuality. However, online leader territorial behavior leads to employee deviance by depriving employees of autonomy in virtuality. Attending to the effects of online leader territorial behavior on employees and leaders simultaneously should illuminate nuanced research directions for future online leader control research.
This research proposes digital human resource management as an influential moderator for the outcomes of online leader territorial behavior, highlighting the importance of digital management systems in online control and promoting an integration of leadership and human resource management research. Discussing the interactive effect of digital human resource management and online leader territorial behavior on employee and leader outcomes broadens the insights into how the positive impact of online leader territorial behavior can be leveraged and the negative impact of online leader territorial behavior can be mitigated. Organizations are advised to proactively adopt relevant digital management systems to improve the efficiency of online management and organizational functioning.

Given the widespread application of artificial intelligence (AI) technologies in workplaces, there has been a rapid increase in literature exploring AI-related themes. Scholars are increasingly focused on understanding how these applications influence employee behaviors and psychology. However, consensus on the direction, boundaries, and extent of these effects remains elusive. To address this issue, this paper conducts a meticulous review and selection of literature published from January 2017 to July 2023. A meta-analysis is performed on the 64 literatures (N = 150) to advance knowledge in three main areas: (1) Explore the strength and direction of the relationship between AI application and employees’ positive behaviors and psychological effects, as well as their negative behaviors and psychological effects. This aims to clarify the inconsistent conclusions and fill gaps in quantitative integration. (2) Based on the Job Demands-Resources model, this paper delineates the theoretical rationale underlying the impact of AI on employees’ behavior and psychology within an organizational context, upon its integration as a new technology, and elucidate specific pathways of its effects. (3) Investigate whether the effects of AI application on employee behavior and psychology are potentially influenced by the type of AI application, industry context, and measurement methods. Endeavor to furnish a clearer and more comprehensive overview of the correlation between AI and employee outcomes, thereby providing a theoretical foundation for tailored AI advantages in practical settings and methodological designs for subsequent empirical research in academia.
The result finds that: (1) The application of AI in the workplace exhibits a “double-edged sword” effect, which can enrich employees' psychological resources as technical support and stimulate positive behaviors, may also threaten employees to consume psychological resources and cause negative behaviors. (2) The relationships between AI application and employee behaviors/psychological effects vary under different AI types. Assisted and augmented AI enhance employee job satisfaction by reducing task costs, thereby increasing work engagement, creativity, and productivity. Such abundance in work resources contributes to an uplift in employees' job satisfaction and happiness. Consequently, when employees experience greater job involvement, there is a notable increase in creativity and productivity. However, managerial and autonomous AI types, despite improving efficiency and autonomy to some extent, introduce stress due to their supervisory and controlling attributes, suppressing positive work experiences and fostering negative psychological states. (3) Variations in AI application effects on employee behaviors and psychological effects across different industry types are evident. Employees in labor-intensive industries, with structured work environments and lower occupational skills, perceive more negative effects from AI. Conversely, employees in knowledge-intensive industries benefit from more flexible and autonomous work environments enhanced by AI, demonstrating stronger abilities in receiving, learning, and adapting to new information and technologies. (4) The relationship between AI application and employee behavior, as well as psychological impacts, varies depending on diverse measurement of AI application. Studies using subjective evaluations tend to reveal more negative impacts of AI on employee behaviors and psychological effects compared to those using objective measurement methods.
This study has made several theoretical contributions: (1) Systematically integrate and evaluate the fragmented research conclusions on the effects of AI application on employee behaviors and psychology, synthesizing empirical findings and responding to calls in the literature for understanding the personal impacts of automation technologies. (2) Within the framework of Job Demands-Resources model, this paper elucidates the diverse impacts of different types of AI application on employee behavior and psychology, expands the influencing factors that could augment the positive results of AI application, and further validates the concerns regarding potential adverse consequences. (3) Enrich the boundary conditions in the relationship between workplace AI application and employee behavior and psychology. This paper explores the moderating effects of the type of AI application, industry context, and measurement methods, responding to the scholarly calls for further examination of moderating variables of AI application affecting employee experience, thereby offering new insights for inconsistent research conclusions in the academic literature. Beyond theoretical advancements, the results of this study provide guidance for organizations to scientifically adjust the management strategies of AI, accurately direct employees perceptions, and effectively maximize its value.

Large quantities of suboptimal food, which contain defects but are perfectly safe for consumption, are wasted. Commercializing suboptimal food has become an important strategy in reducing food waste. However, suboptimal food marketing intervention strategies are numerous and complex. There is a lack of logical sorting out and clear classification of these strategies, and the current experimental results on the effectiveness of marketing intervention strategies for suboptimal food have exhibited inconsistency.
This study adopted a meta-analysis approach to review empirical research on marketing intervention strategies for suboptimal food. Based on the Elaboration Likelihood Model (ELM), suboptimal food marketing intervention strategies were classified into two categories: cognitive-oriented and affective-oriented, with cognitive-oriented marketing intervention strategies including “price promotion”, “emphasizing other value attributes” and “ugly labeling”; and affective-oriented marketing intervention strategies including “anthropomorphism”, “sustainability appeals” and “boosting self-esteem”. It assessed the effectiveness of cognitive-oriented and affective-oriented marketing intervention strategies in influencing consumer evaluation and purchase intention toward suboptimal food. Additionally, the study aimed to identify variables that may impact the effectiveness of these strategies. In total, 32 relevant primary studies were included, comprising 94 effect sizes from 57 independent samples. This study used Comprehensive Meta-Analysis 3.0 for data processing and selected Cohen's d as the effect size, with higher values indicating better intervention effects. Result showed that both cognitive-oriented and affective-oriented marketing intervention strategies could effectively improve consumers’ positive evaluation and willingness to purchase suboptimal food with a moderate level of efficacy. Moreover, affective-oriented strategies demonstrated a better intervention effect compared to cognitive-oriented ones. This study further systematically evaluated and compared the effectiveness of six specific strategies on food evaluation and purchase. In terms of food evaluation, the intervention effect of “emphasizing other value attributes” was the strongest. In terms of food purchasing, “anthropomorphism” was significantly more effective than “emphasizing other value attributes,” “sustainability appeals,” and “price promotion,” but not significantly different from “ugly labeling” and “boosting self-esteem.” Additionally, factors such as suboptimal food characteristics, marketing features, and customer characteristics were found to moderate the effectiveness of these interventions. Regarding suboptimal food characteristics, cognitive-oriented marketing intervention strategies had a significantly greater effect on farm produce than on processed food. Regarding marketing characteristics, strategies that use images were more effective than those that use text; marketing intervention strategies were more effective when suboptimal food was displayed alone compared to being displayed alongside superior food; and marketing intervention strategies were more effective in farmers' markets compared to supermarkets. Regarding customer characteristics, affective-oriented marketing intervention strategies were significantly more effective for female consumers than for male consumers; cognitive-oriented marketing intervention strategies were significantly more effective for younger customers than for older customers; and suboptimal food marketing intervention strategies were significantly more effective in collectivist cultures compared to individualist ones.
The theoretical significance of this study was reflected in three aspects: (1) Current research on suboptimal food marketing intervention strategies lacks systematic review and classification, and experimental results on their effectiveness are inconsistent. This study, based on the ELM, classified suboptimal food marketing intervention strategies into cognitive-oriented and affective-oriented categories and assessed their effectiveness. (2) This study systematically evaluated and compared the effectiveness of six specific strategies on food evaluation and purchase. (3) This study also proposed an integrated model to analyze the boundary conditions for the effectiveness of marketing intervention strategies for suboptimal food, which broadened the horizon for future research on suboptimal food. Practically, this study helped food companies' marketing departments and governments to adopt targeted suboptimal food marketing intervention strategies. For example, food companies can set up a separate display area for suboptimal food, allowing consumers to focus more on these food; at the same time, the characteristics of different retail environments should be taken into account when choosing sales channels, and supply suboptimal food to farmers' markets can enhance their sales and promotional effectiveness.
Currently, most empirical articles on suboptimal food marketing intervention strategies focus on food with suboptimal appearance and less on food nearing its expiration date. In fact, there are many food nearing its expiration date on the market, such as bread and yogurt near the shelf life. Future research can increase the attention to this type of food and target feasible marketing intervention strategies. In addition, the psychological mechanisms of suboptimal food marketing intervention strategies have been less explored, and future research could further explore the mediating mechanisms in the relationship between suboptimal food marketing intervention strategies and consumer purchase.

Language is very informative. Using language to communicate allows communicators to communicate efficiently. Communicators could form predictive cognition based on speech information. The predictive cognition is not invariant on the semantic processing. For instance, there are also some occasional misunderstandings during communication. Thus, in order to better understand the meaning of language in the process of sentence comprehension, it is necessary to combine more information beyond semantics, which we called external-linguistic information, such as the speaker's body posture, voice, social status, and scene. Consequently, extra-linguistic information about the speaker's attitudes and emotional states might be expected to play an important role in language comprehension. Which means the notion of word meaning or lexical meaning in a sentence can be understood in different ways. It can be argued that meaning is constructed online each and every time a word is used, by combining extra-linguistic information and contextual cues. The combination of language with different external information produces different meanings, which may lead to deviations in the listener's semantic understanding, that is, ambiguity. In fact, there are many situations in every-day life where pragmatic information about the speaker is relevant for language comprehension, and may specifically influence the interpretation of a speaker's intended utterance meaning. This phenomenon has attracted extensive attention from researchers. Studies found that listeners rely on semantics to understand external information, and at the same time combine external information to understand semantics, and the two links are closely integrated. In linguistics, this combination of individual cognition difference and external information is called context predictive processing. Context predictive processing refers to the effective prediction to form predictive cognition before lexical semantic processing. There are two main factors influencing the formation of predictive cognition: prior knowledge and contextual information. The two factors contribute to predictive cognition and promote each other. It has been widely acknowledged that context predictive processing can affect semantic processing. In the specialization of language research into individual disciplines, and under the umbrella of two-stage theories that suggest pragmatic language processing follows semantic and syntactic processing, pragmatic language processing has often been investigated separately and independently from other aspects of language. In recent years, however, the view of pragmatics as a separable aspect of language processing has begun to change. Multiple studies show that context predictive processing affects the neurophysiological correlates of semantic processing. This paper combs the relevant studies on the formation of cognition by context predictive processing and finds that, in the process of semantic understanding, people's cognition formed by context predictive processing is not the invariant cognition formed by the initial state, but the dynamic processing process that is constantly adjusted with the increase of information received. However, most of the previous studies rarely included the influencing factor of the change of cognition in predictive processing. In order to better investigate the influence of the change of cognition on the semantic processing, this paper reviews the theories that can explain the formation of predictive cognition and explores the neural mechanism of the formation of predictive cognition. Firstly, This paper reviews the influence of predictive cognition on semantic processing and how predictive processing forms predictive cognition. Then, This paper makes clear the influencing factors of predictive cognition formation, that is, the role of prior knowledge and contextual information on predictive cognition formation. Next, this paper focuses on the theoretical explanation of predictive processing and verifies each other with the neural mechanism of predictive cognition formation. It is an attempt to illustrate the dynamic nature of predictive cognition. At last, the possible research directions of dynamic cognition in predictive processing are prospected: from the aspects of gender, the time of presentation of contextual information, the arousal of contextual information and the control of independent variables.

Faces and gaze direction are crucial social cues in interpersonal interactions. Investigating how infants, particularly newborns, process these cues enhances our understanding of the origins and development of human social abilities. A review of existing literature shows that neonates (0~28 days old) generally prefer human faces and direct gaze, while infants around 3 months old begin to follow gaze direction. Brain imaging studies reveal that infants older than 3 months exhibit neural responses to faces and gaze processing similar to those of adults.
Newborns demonstrate a preference for human faces from birth. They exhibit this bias regardless of whether presented with real faces, sketched faces, or face-like patterns with just three dots and a contour. Event-related potential (ERP) studies indicate that infants aged 3 to 6 months show brain responses similar to adults. Specifically, infants exhibit a stronger amplitude and shorter latency of N290 towards faces compared to objects, and a larger P400 amplitude when viewing familiar faces, with increased responses to inverted faces compared to upright faces and front faces compared to side faces. These infant ERP components resemble the adult face-specific N170 component. Functional magnetic resonance imaging (fMRI) studies show that at 5 months of age, infants display stronger activation in the fusiform gyrus, occipital cortex, superior temporal sulcus, and medial prefrontal cortex when viewing faces compared to natural scenes. Moreover, 5-month-old infants exhibit a face-selective region in the fusiform gyrus that is functionally and anatomically similar to that in adults. Furthermore, infant face processing shows a right hemisphere dominance (or left visual field bias) similar to that seen in adults.
Newborns also show a preference for direct gaze, looking longer at faces with direct eye gaze compared to faces with averted gaze. They demonstrate a familiarity effect (longer gaze durations during face recognition tasks for familiar faces compared to unfamiliar ones) only for faces with direct gaze. Additionally, infants aged three months and older display gaze-following behavior, with faster and more accurate saccades towards targets in the direction of others' gaze. Gaze following develops gradually from 3 to 12 months, with 6 months being a critical period for development. ERP studies reveal that direct gaze faces elicit a stronger N290 amplitude in 4-month-old infants compared to averted gaze faces, with direct gaze also triggering brain activity resembling that of a smile: a notable increase in brain electrical power observed in bilateral posterior temporal regions and the left frontal region. In a gaze-following study, congruent targets evoked weaker positive slow wave (PSW) amplitudes in 4-month-old infants compared to incongruent targets, a component reflecting familiarity with the stimulus. Additionally, functional near-infrared spectroscopy (fNIRS) studies found that 5-month-old infants showed significant activation of the left dorsolateral prefrontal lobe when performing the "gaze cue-target" task, whereas 7- to 12-month-old infants showed activation of the right dorsolateral prefrontal lobe, reflecting the developmental shift from exogenous to endogenous processing of gaze.
Cognitive neuroscience theories on newborns' facial preference include Johnson's two-process model and Turati's "non-specificity" theory. Johnson's model suggests faces are distinct stimuli, indicating an innate human ability to recognize faces. Turati's theory argues that newborns' facial preference stems from a preference for certain non-specific visual structures, such as the "top-heavy" configuration (more elements in the upper part than the lower part) and "inner-outer match" (alignment of internal geometric shapes with external contours, such as both being inverted triangles). Both theories acknowledge newborns' innate preference for specific visual stimuli, differing on whether faces are inherently special stimuli. The cognitive neural theory on infants' processing of gaze direction, proposed by Johnson's research group, includes the fast-track modulator model. This model posits that gaze direction information is processed through two pathways: a slow cortical pathway involving regions such as the superior temporal sulcus and fusiform gyrus, and a fast sub-cortical pathway involving regions like the superior colliculus and amygdala. The sub-cortical fast pathway is fully functional at birth, enabling newborns to quickly identify direct gaze cues and gradually activating broader cortical pathways during development, allowing for the processing of more complex gaze information.
In summary, research on infant face perception and gaze direction indicates that: 1) Newborns prefer faces; 2) Newborns prefer direct gaze; 3) Infants engage in gaze following, enhanced by prior direct eye contact experiences; 4) Infants' neural responses and brain networks for processing faces and gaze resemble those of adults. Prior studies have focused on whether facial and gaze are distinct stimuli for newborns, providing compelling exclusive and neuroimaging evidence. Future research should consider isolating the physical properties of faces and gazes more completely. Regarding experimental techniques, magnetoencephalography (MEG) is a promising technology, as its multi-channel high-density capabilities can resolve neural responses in very small subcortical nuclei.

Frugality, a longstanding virtue deeply embedded in the fabric of Chinese culture, finds itself at the forefront of societal discourse in the ever-evolving landscape of the modern material economy. While traditionally lauded for its positive impact on individual and communal well-being, contemporary scrutiny begs the question: does frugality always harmonize with ethical principles? This article endeavors to provide a comprehensive exploration of this inquiry, shedding light on the multifaceted effects of frugality on prosocial behaviors.
In general, although the definition of frugality may vary due to disciplinary differences, it generally has the following three common points: First, people practice frugality to adapt to the current living environment or to create greater development space for the future, which is an adaptive development strategy based on resource usage and allocation. Second, the most direct manifestation of frugality is the economical use of financial resources (e.g., money), but individuals who practice frugality are not limited to being frugal only with financial resources. People can exhibit frugal tendencies in clothing, food, housing, transportation, and other aspects, such as saving food, reducing fuel consumption, staying in budget hotels, and renting housing. Third, frugality emphasizes the cautious allocation of resources and seeks to maximize benefits through methods such as saving, reducing expenses, and seeking cost-effective transactions. At its core, frugality encompasses a spectrum of behaviors and attitudes aimed at resource conservation and prudent expenditure. Its roots in Chinese tradition have fostered a societal ethos valuing moderation and restraint in material consumption. However, the ethical implications of frugality extend beyond mere resource management, touching upon broader themes of altruism, morality, and social cohesion.
Central to the discourse surrounding frugality is its dual-edged nature, characterized by both beneficial and detrimental consequences for prosocial behaviors. On one hand, frugality is heralded for its ability to cultivate self-control, foster social bonds, and encourage cooperation among individuals. By instilling habits of moderation and self-discipline, frugality may catalyze personal growth and communal harmony. Furthermore, frugal individuals may exhibit a heightened sense of empathy and interconnectedness with their fellow community members, thereby promoting acts of generosity and altruism. Conversely, the pursuit of frugality is not devoid of its pitfalls. In striving to minimize expenditure and maximize utility, individuals may become overly preoccupied with self-interest and cost considerations, potentially compromising their ethical compass. Moreover, an excessive focus on frugality may lead to a reduction in perceptions of human nature, viewing interpersonal interactions through the lens of transactional exchanges rather than genuine empathy and compassion.
To elucidate the underlying mechanisms driving these dual-edged effects, various psychological theories offer valuable insights. The theory of resource conservation posits that frugality stems from an innate propensity to conserve resources for future contingencies, reflecting an adaptive evolutionary strategy. Similarly, self-control theory suggests that frugal behaviors are governed by cognitive processes aimed at regulating impulses and desires in the pursuit of long-term goals. Furthermore, social exchange theory highlights the role of reciprocity and trust in shaping prosocial behaviors, underscoring the importance of social connections forged through frugality.
There exist numerous avenues for further exploration and research within the realm of frugality and its impact on prosocial behavior. Future studies could seek to delve deeper into the moral dimensions of frugality, examining its implications for ethical decision-making and moral reasoning. Cross-cultural investigations can offer valuable insights into the universality of frugality and its cultural variations across diverse societies. Additionally, efforts to identify the commonalities and differences in the role of frugality across various forms of prosocial behavior can enhance our understanding of its underlying mechanisms. Moreover, the development of a theoretical framework elucidating the boundary conditions can provide a roadmap for future research endeavors. By integrating perspectives from moral psychology, behavioral economics, and social neuroscience, scholars can advance our understanding of the complex interplay between frugality and prosocial behavior.
In conclusion, the exploration of frugality concept represents a multifaceted endeavor with far-reaching implications for individual behavior and societal well-being. By unraveling the dual-edged effects of frugality on prosocial behaviors and elucidating the underlying psychological mechanisms, researchers can pave the way for the integration and development of frugality psychology and prosocial behavior research, ultimately contributing to the promotion of social harmony and altruism in an ever-changing world.

As globalization advances, individuals may develop an increased awareness and concern for the entire human race. “Identification with all humanity (IWAH)” captures this psychological trait, encompassing the recognition of all humans as part of one’s ingroup and fostering care for every member of the global human family. Building on the clarification of the concept and measurement of IWAH, our current work aims to explore the relationships between IWAH and prosocial psychology and behaviors across diverse domains. Additionally, we seek to uncover the psychological mechanisms underlying them. Our goal is to provide researchers and policymakers with a deeper understanding of IWAH and its effects, fostering global cooperation to address pressing challenges and build a more peaceful, prosperous and sustainable world.
The concept of IWAH was proposed by McFarland and colleagues, encompassing two dimensions: bond and concern. The bond dimension implies a sense of social membership, indicating a cognitive and emotional connection with the humanity. The concern dimension refers to a willingness to help and care for others and a sense of responsibility towards the human well-being. Given the comprehensiveness of the concept and the effectiveness and reliability of its measurement tools, IWAH has attracted widespread attention across the globe.
Taking the social identity theory and common ingroup identity model, previous research has found that IWAH had significant effects on prosocial psychology and behaviors across diverse domains, including intergroup interactions, environmental sustainability, and public health crises responses. Regarding intergroup interactions, previous studies have found that IWAH promoted individuals' willingness to help others, fosters close relationships with ethnically diverse partners, increases intergroup forgiveness, and enhances concern for global equity and global justice. Caring for the welfare of humanity as a whole may motivate individuals to take environmental issues seriously and adopt behaviors that promote environmental sustainability. Research on IWAH and pro-environmental behaviors has confirmed a robust positive correlation between them. Prior research on responses to public health crises has revealed a positive relationship with IWAH and proactive health behaviors, such as maintaining physical distance and wearing masks. It is noteworthy that the contextual activation of IWAH does not significantly influence health behaviors during pandemics, and the two dimensions of IWAH have distinct roles in responding to public health crises.
According to the theory of planned behavior, the theory of psychological distance, the norm activation model, and existing studies, we proposed that the positive effects of IWAH operated through multiple factors such as relevance, responsibility, obligation, and emotional processes. Relevance, which refers to how people appraise and relate themselves to others and social issues, the stronger the relevance, the closer the psychological distance. The bond to humanity increases individuals' perceived relevance to others and social issues, heightening the urgency and necessity to assist those nearby and address problems, thereby promoting prosocial attitude and behaviors. Identifying as part of humanity motivates individuals to prioritize the interests of humanity as a whole, which can inspire a sense of responsibility towards social issues and lead to prosocial behaviors. Similarly, IWAH may deepen an individual's understanding of interdependence with others and the larger environment, enhancing the sense of obligation to safeguard the interests of humanity, and thus promote prosocial psychology and behaviors. IWAH enhances individuals’ perceptions of humans globally as highly similar to themselves, fostering sympathy and empathy for victims and thereby promoting prosocial psychology and behaviors. In addition, individuals' negative emotional responses to actions harmful to human interests and positive emotional responses to actions defending these interests may mediate the relationship between IWAH and pro-sociality.
However, the interaction among the four psychological constructs mentioned above is complex, necessitating the exploration of a comprehensive psychological mechanism model. Future research should investigate the evolutionary and biological mechanisms underlying the impacts of IWAH on prosocial psychology and behavior, as well as the boundary conditions of those effects. Additionally, it is highly recommended to probe how this relationship evolves in the era of artificial intelligence and the cultural variations within the context of globalization.

Currently, environmental issues, such as air pollution, the depletion of natural resources, climate change, and the use of hazardous materials, have become increasingly severe. In response to these challenges, the Chinese government has established ambitious goals, aiming to achieve a carbon peak by 2030 and carbon neutrality by 2060. This initiative urges enterprises to actively take on the social responsibility of green development, prioritizing environmental protection alongside economic pursuits. However, many enterprises encounter obstacles in the process of green development. One key to overcoming these obstacles is enhancing employee green creativity, the antecedent variables of which have been extensively explored by scholars. Yet, current research on employee green creativity remains fragmented, and a systematic understanding of the inducing factors and models of green creativity in academics is lacking. Therefore, we comprehensively review the concept definition and measurement of green creativity using the literature methodology recommended by PRISMA. We aim to investigate how to stimulate employee green creativity and contribute to enriching the literature on green creativity.
Employee green creativity refers to the development of new ideas about green products, green services, green processes, or green practices that are judged to be original, novel, and useful. This is not only a form of creativity but also a kind of creativity that is focused on addressing stakeholders' environmental concerns. Specifically, both green creativity and traditional creativity commonly emphasize the originality, novelty, and practicality of ideas. However, significant differences exist between these concepts in their manifestation, objectives, antecedent variables, and requirements for employees’ qualities. Some studies regard employee green creativity as the output of their green efforts, while others view it as their ability. We adopt the former perspective, defining employee green creativity more objectively and reasonably. Additionally, we distinguish between green innovation and green creativity. While green innovation involves implementing new ideas, green creativity is primarily concerned with generating these ideas. In other words, green creativity serves as the foundation for green innovation.
Then, we identify that individual factors (motivation, cognition, emotion, attitude, ability, and behavior) and contextual factors (leadership, vision and strategy, management practice, and comprehensive strength) constitute the inducers of employee green creativity. The joint effects of these two factors can be characterized by two models: the “situation → individual” driving path model and the person-situation interaction model. Currently, research primarily focuses on the driving path model while paying little attention to the interaction model. The former emphasizes how contextual factors shape employee green creativity by continually stimulating intrinsic green motivation, influencing green cognition, generating green-related emotions, and altering previous attitudes and behaviors, thereby positively or negatively influencing green creativity. Drawing upon self-determination theory, social cognition theory, affective events theory and attitude change theory, existing studies explain how contextual factors act on individual factors to stimulate employee green creativity. Future studies can further compare the explanatory power of different theories, explore additional theoretical perspectives (such as situational intensity theory), and investigate new driving paths between individual and contextual factors (such as green leadership and workplace status). The latter mode focuses on the interaction between individual and contextual factors, exploring the process of fostering employee green creativity. Due to the lack of research on this interaction model, we introduce the competence activation model and motivated information processing theory as a foundational explanatory framework for inducing employee green creativity. Additionally, it is crucial to recognize the substitution effect of this interaction. When certain factors cannot be satisfied, other alternative factors can also enhance green creativity.
Finally, future research on green creativity should first aim to redefine green creativity and develop a psychological measure to systematically explain how employees generate green ideas through cognitive processes ignored by scholars. Second, traditional workplace culture may prove more effective in fostering green creativity among Chinese employees. Third, the stimulating mechanisms of team green creativity play a pivotal role in addressing environmental protection issues and can effectively guide enterprise green development. Finally, both academia and industry need to not only explore the dynamic attributes of green creativity but also be aware of the moral licensing effect of green creativity that may accompany it. Attention to maintaining this competitive advantage is crucial for ensuring the effectiveness of green creativity goals.

Green consumption is not only an individual’s consumption habit, but also an important social issue closely related to the future development of the society. Despite the continuous efforts from the marketers and governments, an issue that has received much attention from marketers and scholars is the gap between the purchase intentions for these products and the actual purchases of them. To better understand how to minimize this intention-behavior gap, research in marketing and social psychology has extensively focused on the drivers of green consumption.
Previous research on green consumption motivation was based on the biophilia hypothesis, which demonstrated that protecting the natural environment was the basic value orientation and psychological motivation. However, consumption choice is not made in vacuum, but rather an act of meaning and often conveys information about the consumer, such as their preference, and social standing. In this vein, green product choices express not only price and quality preference, but also consumers’ values and social identity. Recent research has also illustrated that it is not environmental protection itself that determines whether people engage in green consumption, but the self-concept associated with it. Hence, it is vital to differentiate the various psychological motivations ’underlying the green consumption.
Compared to general consumption behavior, green consumption is altruistic and high cost, allowing individuals to construct their moral self and social self through green choices. Based on moral regulation theory and high cost signal theory, we propose that people have dual psychological routes when engaging in green consumption—moral self-construal and social self-construal. Specifically, Consumers with moral self-construal utilize green consumption as a way to compensate to eliminate the moral dissonance caused by the contradiction between reality and ideal moral self. They are influenced by several psychological factors related to morality such as moral emotions, moral consciousness, and moral identity. Green consumers with social self-construal make green choices to satisfy the symbolic representation motivation, such as status, power, reputation, and wealth characteristics. In this manner, they are also influenced by some psychological factors related to status and power such as status seeking, sense of power, and face awareness.
Furthermore, we argue that individuals adjust and integrate their self-constructed psychological paths in dynamic consumption scenarios, leading to differentiated green consumption intentions and behavioral consequences. Based on the dual path model, consumers can flexibly switch between the dual paths according to consumption scenarios, product types, advertising information, and other situational factors. They can also simultaneously promote the dual motivation of moral and social self-construal in a single consumption scenario. At the same time, the green consumption behavior model shaped by the dual psychological dynamic path can also reshape consumers’ green consumption motivation and have spillover effects on subsequent consumption behavior.
To summarize, this research enhances our understanding of the different roles of intrapersonal and interpersonal factors that influence green consumption in a comprehensive and dynamic perspective. More generally, our findings also add to a growing body of research pointing to a link between identity and consumers’ tendency to engage in green consumption behavior.

Cognitive modeling has gained widespread application in psychological research, providing a robust framework for understanding complex cognitive processes. These models are instrumental in elucidating how mental functions such as memory, attention, and decision-making work. A critical aspect of cognitive modeling is model comparison, which involves selecting the most appropriate model for describing the behavior data and latent variable inference. The choice of the best model is crucial as it directly influences the validity and reliability of the research findings.
Selecting the best-fitting model often requires careful consideration. Researchers must balance the fit of the models to the data, ensuring that they avoid both overfitting and underfitting. Overfitting occurs when a model describes random error or noise instead of the underlying data structure, while underfitting happens when a model is too simplistic and fails to capture the data's complexity. Additionally, researchers must evaluate the complexity of the parameter data and the mathematical forms involved. This complexity can affect the model's interpretability and the ease with which it can be applied to new data sets.
This article categorizes and introduces three major classes of model comparison metrics commonly used in cognitive modeling: goodness-of-fit metrics, cross-validation-based metrics, and marginal likelihood-based metrics. Each class of metrics offers distinct advantages and is suitable for different types of data and research questions.
Goodness-of-fit metrics are straightforward and intuitive, providing a direct measure of how well a model fits the observed data. Examples include mean squared error (MSE), coefficient of determination (R²), and receiver operating characteristic (ROC) curves.
Cross-validation-based metrics provide a robust means of assessing model performance by partitioning the data into training and testing sets. This approach helps mitigate the risk of overfitting, as the model's performance is evaluated on unseen data. Common cross-validation metrics include the Akaike Information Criterion (AIC) and the Deviance Information Criterion (DIC).
Marginal likelihood-based metrics are grounded in Bayesian statistics and offer a probabilistic measure of model fit. These metrics evaluate the probability of the observed data given the model, integrating over all possible parameter values. This integration accounts for model uncertainty and complexity, providing a comprehensive measure of model performance. The marginal likelihood can be challenging to compute directly, but various approximations, such as the Bayesian Information Criterion (BIC) and Laplace approximation, are available.
The article delves into the computation methods and the pros and cons of each metric, providing practical implementations in R using data from the orthogonal Go/No-Go paradigm. This paradigm is commonly used in cognitive research to study motivation and reinforcement learning, making it an ideal example for illustrating model comparison techniques. By applying these metrics to real-world data, the article offers valuable insights into their practical utility and limitations.
Based on this foundation, the article identifies suitable contexts for each metric, helping researchers choose the most appropriate method for their specific needs. For instance, goodness-of-fit metrics are ideal for initial model evaluation and exploratory analysis, while cross-validation-based metrics are more suitable for model selection in predictive modeling. Marginal likelihood-based metrics, with their Bayesian underpinnings, are particularly useful in confirmatory analysis and complex hierarchical models.
The article also discusses new approaches such as model averaging, which combines multiple models to account for model uncertainty. Model averaging provides a weighted average of the predictions from different models, offering a more robust and reliable estimate than any single model. This approach can be particularly beneficial in complex cognitive modeling scenarios where multiple models may capture different aspects of the data.
In summary, this article provides a comprehensive overview of model comparison metrics in cognitive modeling, highlighting their computation methods, advantages, and practical applications. By offering detailed guidance on choosing and implementing these metrics, the article aims to enhance the rigor and robustness of cognitive modeling research.
Model comparison involves considering not only the fit of the models to the data (balancing overfitting and underfitting) but also the complexity of the parameter data and mathematical forms. This article categorizes and introduces three major classes of model comparison metrics commonly used in cognitive modeling, including: goodness-of-fit metrics (such as mean squared error, coefficient of determination, and ROC curves), cross-validation-based metrics (such as AIC, DIC), and marginal likelihood-based metrics. The computation methods and pros and cons of each metric are discussed, along with practical implementations in R using data from the orthogonal Go/No-Go paradigm. Based on this foundation, the article identifies the suitable contexts for each metric and discusses new approaches such as model averaging in model comparison.