In real-life situations, facial expressions often change dynamically over time. An individual's interpretation of a facial expression may be influenced by its dynamic sequential context. While previous research has shown that simultaneously presented contextual information affects the perception of target expressions, little is known about the effect of sequential changes in facial expressions. The present research aims to systematically investigate how dynamic sequential context shapes the perception of current and past expressions, from both phenomenological and mechanistic perspectives, thereby providing new insights for facial expression processing.
Existing evidence suggests that when dynamic facial expressions transition from a past to a current expression, the perception of the current expression tends to shift in the opposite direction of the valence of the past expression (i.e., a contrast effect; Fang et al., 2021; Hsu & Young, 2004; Russell & Fehr, 1987). However, these studies have primarily used ambiguous current expressions (e.g., neutral faces) or morphed dynamic expressions. It remains unclear whether the past expression would still influence the perception of current expressions that convey clear emotional meaning (e.g., prototypical sad expressions) or when using authentic, human-performed dynamic expressions.
On the other hand, the dynamic sequential context might also influence how individuals perceive the past expression in a dynamic display. Previous research indicates that individuals tend to reconstruct past emotional experiences based on their current emotion state (Levine, 1997; Levine et al., 2018; Levine & Safer, 2002; Van Boven et al., 2009). Yet, little is known about whether similar phenomena occur in the perception of past expressions of a dynamic display (but see Fang et al., 2024). We propose that current expressions might serve as a “recall filter” for the reconstruction of past expressions, such that individuals use the emotional features of the current expression to interpret the past expression, leading to an assimilation effect.
Furthermore, the present research aims to examine the underlying mechanisms of these dynamic sequential effects. We hypothesize that the magnitude of the contrast and assimilation effects may be correlated with the attention individuals allocate to the emotional features of the past and current expressions, respectively. Specifically, we propose that increased attention to the emotional features of the past expression may strengthen the adaptation aftereffects, thereby enhancing the contrast effect in the perception of current expressions. Conversely, increased attention to the emotional features of the current expression may strengthen the "recall filter," leading to a stronger assimilation effect in the perception of the past expression. In addition, the dynamic sequential effects may be related to representational momentum, such that faster changing speeds might enhance representational momentum, thereby increasing the contrast effect when perceiving current expressions and the assimilation effect when perceiving past expressions.
To investigate these issues, the present research will consist of a series of studies. Study 1a will explore the generalizability of dynamic sequential effects on the perception of current expressions by manipulating the emotional ambiguity of current expressions and using both artificially synthesized and human-performed dynamic facial expressions. Studies 1b~1d will investigate the roles of adaptation aftereffects and representational momentum in the dynamic sequential effects when perceiving current expressions. Study 2a will examine how current expressions influence the perception of past expressions, using facial stimuli with varying emotional ambiguity and authenticity. Studies 2b~2d will examine the roles of recall filter and representational momentum in the dynamic sequential effects when perceiving past expressions. The findings of this research will contribute to our understanding of facial expression processing in ecological contexts and provide insights for AI-based dynamic facial expression recognition systems.

Spatial ability refers to the ability of individuals to recognize, encode, store, represent, decompose, combine and abstract objects or spatial figures in their minds, which is the cognitive foundation for understanding one's environment and solving problems. Building an accurate, convenient and effective assessment system of spatial ability is of great significance to the enhancement of STEM education and the quality of talent cultivation. Due to the complex, multi-dimensional and implicit nature of spatial ability, it is difficult to evaluate spatial ability via computer-based assessments. This study aims to accurately, effectively, and massively evaluate spatial ability by using multimodal learning analytics methods to explore the characteristic behavioral expressions of learners' spatial cognition, and by developing key technologies and tools for spatial ability stealth assessment based on video game environments.
This study further develops spatial ability assessment methods based on video games, building upon previous research. Unlike prior game-based assessments that primarily focus on post-hoc analysis of backend game data, this study innovates by deeply integrating gamified assessment with multimodal learning analytics to provide process-oriented evaluation and a comprehensive understanding of spatial abilities. Firstly, in the self-developed video game assessment tool, an evidence-centered design framework combined with Bayesian networks is creatively applied to identify and aggregate multimodal behavioral data that can infer learners' spatial ability levels. This approach leverages existing research and expert knowledge, enhancing the identifiability and interpretability of the assessment model. Secondly, a novel paradigm for spatial ability assessment driven by multi-source data is proposed, incorporating learner behavior data (observational data), game backend data (interaction data), and physiological signals during gameplay (contextual data). These data are aligned over time and incorporated into the inference system according to evidential rules, updating probabilistic predictions of spatial abilities to achieve automated assessment. Additionally, the cognitive mechanisms underlying the impact of video games on spatial abilities are explored using 3D puzzle games as a virtual environment for training and assessing spatial abilities. The xAPI standard is employed to automatically collect learner interaction data, enabling stealth assessment of spatial abilities and effectively mitigating issues such as test anxiety and the Hawthorne effect, while ensuring both internal and ecological validity. This lays a technical foundation for the future application and dissemination of gamified spatial ability assessment tools. Lastly, the study aims to achieve a holistic understanding of spatial abilities, covering different scales and cognitive processes through the assessment results, thereby enriching and expanding the concept and scope of spatial abilities, providing instrumental support for theoretical research in this field. Using an evidence-centered design paradigm, an original assessment tool is constructed to deepen our understanding of spatial abilities. Building on theoretical research and leveraging cutting-edge multimodal analysis techniques, the tool collects diverse physiological signals such as EEG and eye-tracking data during gameplay, along with operational logs and behavioral indicators from the game backend. Following a "proficiency-evidence-task" framework, variables at various levels and categories of spatial ability are summarized within a Bayesian network model, facilitating unobtrusive assessment and a comprehensive understanding of spatial abilities. The gamified assessment tool created in this study not only addresses limitations commonly found in traditional psychological tests, such as the lack of process data and susceptibility to social desirability biases, but also cleverly harnesses the appeal of video games as a mass medium, offering low-cost and wide-reaching advantages. Leveraging the convenient dissemination properties of social networks and internet platforms, gamified assessments can stimulate broad participation among players, thus collecting massive amounts of data, with sample sizes reaching thousands, tens of thousands, or even millions. Such large-scale data samples present unprecedented opportunities for group-level studies of cognitive abilities. In summary, the video game-based spatial ability assessment designed in this study holds promise for overcoming longstanding challenges in quantifying and evaluating spatial abilities, with significant implications for large-scale talent selection and development efforts, and the potential to bring about new breakthroughs in the field of spatial ability research.

Facing the complex and rapidly changing business environment, it is very important for leaders to learn to listen effectively if organizations want to respond quickly. Existing studies have not yet formed a unified definition of the concept of leader listening in the organizational context, and more of them deduced the impact of leader listening on employees’ work attitudes and interpersonal relationships theoretically. While few scholars have systematically conducted empirical studies on the diverse impacts of leader listening at different levels. Therefore, this study places listening in the leader-employee dual interaction context, clarifies the connotation dimension of leader listening, and explores a measurement scale suitable for Chinese organizational context. On this basis, based on the perspective of leadership effectiveness, we try to construct a theoretical framework of multi-level effect of leader listening through three closely related research, specifically exploring how leader listening affects the different psychological perceptions and behaviors of leaders themselves, teams, and employees, and systematically demonstrating the leadership effectiveness of listening at the three levels. This study clarifies the connotation dimension of leader listening, enriches the research level and theoretical perspective of leader listening, and deepens the theoretical understanding of the leadership effectiveness generated by listening.
First, this study explores the connotation dimension of leader listening in terms of both task and relationship, and develops a localized leader listening measurement scale. The lack of a unified view or validation of the basic issues of the connotation of leader listening and the measurement scale has hindered the development of leader listening research. The clarification of the connotation of leader listening in this study lays the foundation for the subsequent in-depth exploration of the phenomenon of leader listening and reveals its multi-level effect, and the localized measurement scale provides a reliable research tool for the subsequent study, thus deepening the theoretical study of leader listening.
Second, this study explores whether leader listening is “enabling” or “burdening” to leader performance from a dynamic perspective. Previous studies have placed more emphasis on the static characteristics of listening, and few studies have examined the double-edged effect of leader listening on the leaders themselves. Based on conservation resource theory, this study constructs a double-edged sword effect model of leader listening on leader job performance, and explores the role of daily self-reflection and daily emotional exhaustion in the relationship between leader listening and leader job performance through two paths: “resource gain” and “resource loss”. Through the two paths, we examined the “enabling” and “burdening” mechanisms induced by leader listening, so as to better verify the role of leader listening in leadership effectiveness at the leader level. This not only enriches the research findings on leader listening at the leader level, but also provides new perspectives for research in the field of leader listening.
Third, this study examines how team members perceive leader listening from a follower perspective. Previous scholars have examined the effects of leader listening on team performance and team learning, and little research has been conducted on the underlying mechanisms, and even more regrettably, little literature has focused on how team members perceive and evaluate leader listening behavior. This study examines the mechanism of leader listening on team followership behavior at the level of leader-team interaction, which not only cleverly explains the interesting and paradoxical phenomenon of leadership distance in organizations by revealing the inverted U-shape relationship between leadership accessibility and team followership behavior, but also further enriches our theoretical knowledge of the leadership effectiveness generated by listening at the team level by exploring the moderating role of team power distance.
Finally, this study analyses the authenticity of leader listening from the perspective of motivational attribution, and constructs a model of the mechanisms of leader listening on employee voice (silence). In leadership research, some scholars have been calling for a dialectical view of positive leadership behavior. Given the differences in listening levels, listening habits and listening skills, this study explores the types of leader listening based on leadership process attribution theory, and examines the dual effects of leader listening on employee voice and employee silence under positive and negative attribution perspectives. This not only enriches the theoretical perspectives of leader listening and provides a new research direction for subsequent studies, but also expands the understanding of the effect of leader listening from an employee perspective.

The symptoms and functional development of individuals at clinical high risk for psychosis (CHR-P) exhibit considerable heterogeneity. Clarifying the changes in symptoms and functioning in this population and their underlying relationships is beneficial for early prevention of psychosis. Previous empirical research has already explored changes in symptoms and functional outcomes in the CHR-P individuals, however, it remains unclear whether there have been recent advances in understanding the longitudinal changes in symptoms and functional outcomes or how symptoms in the CHR-P population may have different longitudinal impacts upon functional domains. Therefore, this study conducted an up-to-date meta-analysis to quantitatively analyze clinical symptoms and functional changes in CHR-P population. The goal was to update the longitudinal findings on clinical symptoms and functioning in CHR-P population, explore potential moderating variables, and clarify the longitudinal impact of CHR-P symptoms on functional outcomes. This study conducted a three-level meta-analysis of 54 studies to comprehensively examine the longitudinal changes in various clinical symptoms and functional outcome among CHR-P individuals and explore the predictive role of baseline symptoms on follow-up functional outcome. The results showed that the CHR-P individuals exhibited significant improvements over time in attenuated positive symptoms (Hedges’ g = -0.78, 95% CI = [-1.12, -0.45]), negative symptoms (Hedges’ g = -0.45, 95% CI = [-0.65, -0.25]), disorganized symptoms (Hedges’ g = -0.40, 95%CI = [-0.78, -0.02]), affective symptoms (Hedges’ g = -0.84, 95% CI = [-1.18, -0.49]), general symptoms (Hedges’ g = -0.43, 95% CI = [-0.71, -0.15]), global functioning (Hedges’ g = 0.47, 95% CI = [0.31, 0.62]), and role functioning (Hedges’ g = 0.28, 95% CI = [0.12, 0.45]), while social functioning (Hedges’ g = 0.66, 95% CI = [-0.08, 1.41]) did not significantly improve. Baseline negative symptoms significantly negatively predicted follow-up global functioning (r = -0.23; p = 0.003), social functioning (r = -0.45; p < 0.001), and role functioning (r = -0.40; p < 0.001). Baseline disorganized symptoms (r = -0.44; p < 0.001) and affect symptoms (r = -0.13; p = 0.010) significantly negatively predicted follow-up global functioning. Moderation analysis revealed that education level significantly moderated the longitudinal changes of attenuated positive symptoms (β = 0.46, F (1,21) = 5.01, p = 0.036) and negative symptoms (β = 0.32, F (1,19) = 4.40, p = 0.050). Moreover, age (β = -0.15, F (1,11) = 8.49, p = 0.014), proportion of attenuated positive symptoms (APS) subgroup (β = -0.05, F (1,7) = 28.75, p = 0.001), and proportion of brief limited intermittent psychotic symptoms (BLIPS) subgroup (β = 0.19, F (1,7) = 21.28, p = 0.002) significantly moderated the longitudinal changes in affective symptoms. The present meta-analysis provides us a more thorough review of the longitudinal development of clinical outcomes in the CHR-P population, which is crucial for understanding precise prognostic trajectories and providing more targeted clinical services. Moreover, it also reviewed the predictive role of baseline symptoms across different dimensions on specific follow-up functional outcomes in the CHR-P population. These findings suggest that persistence of negative symptoms and the social function impairments are the core features of CHR-P individuals. It is necessary to target at negative symptoms in CHR-P individuals for improving their social functioning. The present findings also reveal the significant moderating effects of age, education level, and CHR-P subtypes on functional changes, providing empirical evidence for the potential underlying reasons behind the heterogeneity in symptom and functional changes in the CHR-P population. In conclusion, in the CHR-P population, attenuated positive symptoms and affective symptoms exhibit greater improvement over time, while negative symptoms, disorganized symptoms, general symptoms, global functioning, and role functioning exhibit less improvement. Social functioning does not improve over time, indicating that symptom improvement generally surpasses functional improvement. Baseline negative symptoms significantly predict social functioning at follow-up, suggesting that negative symptoms and social functioning should be key areas of focus during the early stages of psychosis. These findings highlight the importance of developing interventions targeting negative symptoms and enhance social functioning.

The concept of “cognitive map” was first proposed by Tolman, who believed that cognitive map is the systematic knowledge organization of cross-domain behavior and is the basis of mental function. The key structure of cognitive map is thought to be the hippocampus, in which place cells fire when the organism is in a specific location, different place cells fire at different locations, and grid cells fire at multiple locations in physical space, providing precise spatial coordinates and the ability to represent vector relationships and distances between different locations. Place cells and grid cells provide the biological basis for the cognitive map of physical space.
Although researchers have reached a consensus on physical space mapping and its mechanisms, it remains to be confirmed whether abstract information can be represented in the form of cognitive spatial maps. Researchers have studied on different species (rats, monkeys, bats, or humans) in which different information (perception, memory, concept, or social) is constructed as cognitive spatial maps from different levels of study (cellular or brain regions). These studies suggest that cognitive space mapping has the nature of cross-information domains, and can process different levels of abstraction and types of cognitive information to form cognitive maps. However, it remains to be clarified whether these studies can support and corroborate each other. In addition, the current exploration of the mechanism of cognitive space mapping is still limited to the stage of analogy with physical space, and there is lack of in-depth exploration of the real mechanism of cognitive space mapping.
According to the degree and type of abstraction of information, cognitive space is divided into perceptual space, episodic memory space, conceptual space and social space. These findings support and corroborate each other, validating Tolman's conceptualization of a cross-domain cognitive map.
This paper also focuses on the neural mechanisms of cognitive space mapping, including the hippocampus and its coordination with other brain regions. On the one hand, the hippocampus abstracts and generalizes the hidden structure of cognitive space, which helps to generate distributed location maps and bundle stimuli with hidden contextual structures. On the other hand, the hippocampus works in concert with other brain regions. Specifically, the hippocampus integrates information from the sensory cortex. The information exchange between the hippocampus and the medial prefrontal cortex supports efficient and flexible cognitive spatial mapping. The functional synergies between the hippocampus and orbitofrontal cortex form different levels of information representation.
Finally, in view of the existing problems and limitations of existing studies, such as the possibility of other neural computation and encoding modalities in place and grid cells, the inability of cognitive space to reflect the precision and hierarchy of information, and the inability to track between the micro and macroscopic studies of cognitive space mapping, future research can deepen the understanding of cognitive space mapping and its underlying mechanisms from four aspects. First, future research should consider the possibility and rationality of applying the hypothesis of predictive cognitive map to cognitive space mapping, and explore how common computational and coding schemes for cognitive space mapping can compete and cooperate with models such as successor representation. Second, it is unclear whether place cells and grid cells at different scales in the hippocampus can support the representation of cognitive information with different precision and hierarchy. The information representation of the hippocampus can be explored by designing research paradigms for different cognitive spaces, improving the accuracy of fMRI signals, and exploring the gradient topological features of the hippocampal axis. Thirdly, more attention should be paid to the cognitive spatial mapping process of normal people, and attempts should be made to explore the activities and neural oscillations of spatial cells in the process of cognitive spatial mapping by using cellular recording, intracranial EEG recording, and magnetoencephalography and other technologies. Fourth, a comprehensive analogy between cognitive spatial mapping and physical spatial mapping should be made to clarify the commonality and specific mechanism of the two, and the overall research framework of cognitive mapping should be constructed.

In recent years, many studies have shown that the use of turn-by-turn navigation may lead to a decrease in an individual's spatial memory, and that long-term use may ultimately impair an individual's spatial navigation ability. Most current navigation software uses turn-by-turn navigation, such as Google Maps. In this paper, we systematically sort out the research on the effects of turn-by-turn navigation aids on spatial memory. The current research can be categorized into two aspects: comparing turn-by-turn navigation aids with paper maps, aiming to explore the impact of turn-by-turn navigation aids on spatial knowledge acquisition; and designing new navigation aids different from turn-by-turn navigation to improve and innovate the traditional turn-by-turn navigation.
Turn-by-turn navigation gives instructions based on turn points and the route is completely predefined (Mazurkiewicz et al., 2023). Instructions typically contain turn direction and distance information, sometimes only steering information is provided, a typical turn-by-turn instruction is e.g. “turn left in 300 meters”. Turn-by-turn navigation aids are characterized by their implementation on mobile devices using GPS technology, and are therefore capable of updating an individual's current position in real time. This is in contrast to paper maps, which are used as traditional navigation aids. Despite the differences between the findings, turn-by-turn navigation aids are either inferior to paper maps or direct experience in terms of spatial knowledge acquisition, or they are not significantly different from them. When researchers found that spatial knowledge gained using GPS-based 2D mobile maps was poor compared to using paper maps or utilizing direct experience, they hoped to use new technologies to compensate for the shortcomings of turn-by-turn navigational aids.AR maps are exactly that: maps that display virtual routes based on the real world. However, there is still controversy about the advantages and disadvantages of AR maps versus 2D moving maps in terms of spatial knowledge acquisition. In conclusion, despite some discrepancies and contradictions in the results of related studies, we conclude from our review of the existing literature that turn-by-turn navigation may impair specific aspects of an individual's spatial memory, such as landmark knowledge, route knowledge, or global spatial knowledge. However, this conclusion still needs to be further validated by more longitudinal studies as most of the current studies adopt a cross-sectional design.
Therefore, how to improve the turn-by-turn navigation system is especially important. We can improve the command information. For example, landmark-based navigation is exactly adding significant landmark information to the navigation instructions. Research suggests that landmark navigation may be able to match turn-by-turn navigation in terms of navigation efficiency, but this still needs to be supported by more research. Landmark navigation may outperform turn-by-turn navigation in terms of spatial knowledge acquisition. It is possible to adapt the way individuals receive information by augmenting sensory perceptions other than vision to facilitate the formation of spatial memory. For example, sensory augmentation-based GPS navigation systems utilize a 3D spatial audio system (3D spatial audio system), similar to an auditory compass, to allow individuals to reach a destination without explicit instructions, an approach that encourages individuals to actively participate in spatial navigation. Research has shown that auditory compass navigation stimulates more exploratory behavior and creates more accurate cognitive maps than turn-by-turn navigation aids (Clemenson et al., 2021). The acquisition of spatial knowledge can be facilitated by having the navigation system return the individual's freedom to explore the environment and plan routes. One study designed a Potential Route Area Navigation (PRA) with an interface based on a dynamic potential route area that contains all possible routes that the individual is willing to accept, and the individual is free to choose and change the route. Results show that spatial knowledge acquisition and user experience are significantly improved when navigating with PRA compared to traditional turn-by-turn navigation aids represented by Google Maps (Huang et al., 2022). Augmented reality can also be utilized to improve turn-by-turn navigation. Quadcopter-Projected In-Situ Navigation (QPSN) improves the ability of individuals to observe real-world points of interest by presenting navigation commands directly in the environment using augmented reality with a projector quadcopter, thereby improving their ability to observe real-world points of interest (Knierim et al., 2018). In addition, a spatial knowledge test is imposed several times during navigation to consolidate the individual's spatial memory, based on the test effect and the pretest effect. Design game modes specifically designed to exercise spatial navigation skills. Finally, provide adaptive services to meet the diverse needs of individuals to help develop a sense of independent navigation and avoid over-reliance on mobile assistive devices.
Future research should focus on the following aspects: improving the measurement of spatial knowledge in large-scale environments; exploring the neural mechanisms by which turn-by-turn navigation impairs spatial memory, although most behavioral experiments have shown that turn-by-turn navigation is detrimental to the acquisition of spatial knowledge, the neurophysiological evidence for which is still limited; and focusing on the effects of individual difference factors (e.g., gender, directionality, level of spatial anxiety, and perspective taking ability) in order to establish a more comprehensive explanatory mechanism. comprehensive explanatory mechanisms. Finally, the new generation of navigation systems should improve the efficiency of wayfinding while taking into account the acquisition of individual spatial knowledge.

The prevalence of anxiety and depression has escalated, prompting the current study to investigate the antecedents and coping strategies for these conditions in an era of digital media. A theoretical framework grounded in affective events theory and social comparison theory is built to elucidate the relationships between social comparison and anxiety and depression, acknowledging that such relationships are contingent upon the influences of the social media environment. This review unveils that negative social comparison (upward comparison and downward assimilation comparison) exerts a deleterious impact on anxiety and depression, with social networking applications catalyzing these adverse effects. Conversely, emotional comparison (i.e., social comparison of emotions) and downward contrast comparison are positively associated with alleviated anxiety and depression, as online health communities fostered a supportive milieu for emotional comparison, thereby helping to mitigate these conditions. This study extends social comparison theory in the realm of emotion and identifies the affordance of online health communities for coping with anxiety and depression. The implications for the principles of design, management, and operation of such communities are further discussed.
Previous research on the relationship between social comparison and anxiety/depression has yielded divergent findings. Some studies have identified social comparison as a paramount factor in initiating, perpetuating, and exacerbating anxiety and depression. Conversely, others have demonstrated that emotional comparison may alleviate stress and anxiety. Unfavorable comparisons with others across various dimensions, such as interpersonal relationships, social status, abilities, accomplishments, careers, income, and appearance, can precipitate psychological disorders like anxiety and depression. However, emotional comparison contributes cognitive clarity, empathic comfort, prevention, and learning, proving to be a coping mechanism for individuals experiencing negative emotions like anxiety in threatening situations. By delineating the distinct subtypes of social comparison, this review elucidates, to some extent, the seemingly complex and contradictory findings in the extant literature on the relationship between social comparison and anxiety and depression, as well as the internal logic behind the dual impact of social comparison on anxiety and depression.
Previous studies have underscored the markedly distinct role of online media environments in shaping the relationship between social comparison and anxiety/depression. On one hand, social networking platforms have expanded the scope of comparisons, diversified the targets of comparison, and increased the accessibility of social comparison information; consequently, the frequency of social comparisons has substantially escalated. Moreover, the editability of information on social networking platforms, the selective presentation of users, and the positive bias of self-presentation (i.e., individuals showcasing their best selves, exaggerating their self-importance, overstating their accomplishments and enjoyment of life, blatantly exhibiting, and even selectively displaying or altering photographs to enhance their appearance) exacerbate the deleterious impact of upward social comparisons, which can provoke anxiety and depression. On the other hand, the characteristics of online health communities (i.e., anonymity, homogeneity, normative, social, and on-demand availability) provide a conducive environment for emotional communication and social comparison, thereby facilitating the amelioration of anxiety and depression.
The review delves into the intricate mechanisms of anxiety and depression within the within the digital media era. It elucidates the intrinsic link between anxiety/depression and social comparison as well as the affordances of online health communities. Furthermore, it conducts a comprehensive exploration of emotional comparison, which has the potential to advance social comparison theory within the emotional realm and broaden the scope of emotional comparison theory in the context of internet-based healthcare. The discussion of the bi-directional effects of social comparison on anxiety and depression underscores the self-reinforcing spiral of individual negative emotions, a notable consideration when addressing the emotional experiences of anxious and depressed groups.
Given the pervasive, disseminated, and developmental affective states, coupled with the distinctive social comparison proclivity exhibited by anxiety-depression cohorts, it is imperative to investigate the emotional adversities (emanating from social interactions) of stigmatized groups through the theoretical lens of intergroup emotions. The ubiquity of self-disclosure, extensive accessibility, and traceability of information facilitated by online communities present opportunities to ameliorate mental health outcomes or manage emotional preoccupations. Subsequent empirical inquiries should delve into the efficacy of online communities in the identification, diagnostic processes, and therapeutic modalities for anxiety and depressive disorders, with particular emphasis on the delineation of online and offline domains, as well as the trade-off between the dichotomous effects of social comparison in digital spheres.

Motivation deficits are a common symptom of depression, often leading to abnormal effort-related reward processing in individuals with depression. Understanding the cognitive neural mechanisms of the willingness to expend cognitive or physical effort to obtain rewards is essential for helping patients recover their social functioning. However, research in this area is currently hindered due to a lack of appropriate methods for determining the roles of cognitive and physical effort expenditure in motivation deficits. In recent years, computational modeling, such as reinforcement learning models, drift diffusion models, cost-benefit optimization models and models with utility discounting functions, has been applied to this field and proved to be a highly promising method of exploring the potential mechanisms underlying effort-related behaviors in depression. Unlike traditional methods, computational modeling allows for a trial-by-trial analysis of behavioral data, providing a more precise and objective assessment of motivational variables. Studies using computational modeling approaches found that, compared with healthy controls, individuals with depression exhibited lower willingness to expend physical and cognitive effort, and abnormal effort-related behaviors in depressed patients were associated with altered neural activity of the prefrontal cortex, striatum, cingulate gyrus and insula. Combining models of utility discounting functions and functional Magnetic Resonance Imaging (fMRI) method, studies in healthy populations showed that model results of encoding of subjective value for both physical and cognitive effort costs were linked to neural activity in brain regions such as the ventromedial prefrontal cortex, anterior cingulate gyrus, ventral striatum and dorsolateral prefrontal cortex. Studies utilizing reinforcement learning models and fMRI method in healthy populations found that specific brain regions, such as the ventromedial prefrontal cortex and anterior insula, were involved in encoding reward and effort prediction errors during physical effort tasks, and the fronto-parietal network was involved in encoding effort prediction errors during cognitive effort tasks. Studies using computational models (i.e., models of utility discounting functions and drift diffusion models) and non-invasive brain stimulation techniques in healthy populations found that neural activity in the dorsolateral prefrontal cortex was related to increased cognitive effort sensitivity, and neural activity in the dorsomedial prefrontal cortex was related to increased physical effort sensitivity. And a recent study combining models of utility discounting functions, transcranial magnetic stimulation (TMS) and electroencephalography (EEG) techniques to explore the role of the left dorsolateral prefrontal cortex in depressed patients showed that, depressed patients with enhanced left dorsolateral prefrontal cortex activation exhibited reduced sensitivity to effort, along with increased amplitudes of the P300 wave to effort-related information and increased amplitudes of the contingent negative variation and stimulus-preceding negativity to reward outcomes. These findings suggest that motivation deficits in patients with depression occur in both physical and cognitive effort domains and highlight the application potential of combining computational modeling approaches with cognitive neuroscience techniques to uncover the shared and divergent mechanisms underlying abnormal physical and cognitive effort-based motivation in depression. Future research combining computational models and cognitive neuroscience techniques could help further dissect the complex cognitive processes and neural foundations of effort-related behaviors, hereby not only providing a more nuanced understanding of the underlying causes of motivation deficits but also helping personalize treatment in individuals with depression.

Post-traumatic stress disorder (PTSD) represents a significant global health burden due to its highly distressing and disabling nature. Networked psychological interventions have gained prominence in the treatment of PTSD, primarily owing to their accessibility and anonymity. Despite their widespread application, a comprehensive systematic review of these interventions remains absent from the literature. This study seeks to fill this gap by providing a thorough overview of the methodologies and therapeutic outcomes associated with networked psychological interventions for PTSD, with a particular focus on both “top-down” and “bottom-up” approaches.
To elucidate the psychological mechanisms underlying these interventions, this study introduces a dual mechanism model for networked PTSD interventions: a cognitive-behavioral therapy (CBT) model based on cognitive restructuring and a cognitive bias modification (CBM) model based on cognitive distortions. Furthermore, this study examines the factors influencing the efficacy of networked interventions for PTSD, thereby laying the groundwork for future research.
The "top-down" intervention approach focuses on modifying patients' cognitive patterns and structures through higher-order cognitive processes, emphasizing cognitive reconstruction. Early networked interventions, grounded in the first and second waves of behavioral therapies, concentrated on cognition and behavior, incorporating modules such as self-exposure and cognitive reassessment. Technological advancements have since enhanced these interventions, broadening their content and form, and integrating therapies that combine habituation with psychological processing of traumatic events. Recently, interventions based on third-wave behavioral therapies, such as Acceptance and Commitment Therapy (ACT) and Mindfulness, have been developed, emphasizing present-moment awareness and acceptance of inner experiences. Among various networked psychological interventions, networked CBT remains the most prevalent and efficacious. "Top-down" psychological interventions are based on cognitive control theory, which emphasizes cognitive control and the regulation of emotions and behaviors by higher brain functions. The mechanism of action involves helping individuals form more positive and realistic cognitions by identifying and challenging negative automatic thoughts, thereby improving mood and behavior. The dual-mechanism model posits that "top-down" interventions may mitigate PTSD symptoms by reducing trauma-related negative cognitions.
Conversely, “bottom-up” interventions involve direct manipulation of stimuli and responses without altering cognitive structures or thought patterns, focusing on the restoration of dysfunctional cognitions. Research on networked CBM for PTSD has predominantly addressed attentional and interpretation biases, with less emphasis on memory bias modification. Networked interpretation bias modification has demonstrated efficacy in treating PTSD, while the effectiveness of attentional bias modification remains debated. "Bottom-up" psychological interventions are based on sensory processing theory and affective processing theory, which propose that sensory input and affective processes can directly influence the brain's higher functions and behavioral responses. The dual-mechanism model hypothesizes that "bottom-up" interventions may alleviate PTSD symptoms by reducing negative cognitive biases, including attentional and interpretation biases.
The effectiveness of networked PTSD interventions is influenced by numerous factors, including individual characteristics (e.g., age, educational background, trauma severity) and intervention-specific factors (e.g., therapist support availability, intervention dosage, modality). The interplay of these factors can significantly determine the overall success of the intervention, necessitating a nuanced understanding of how different elements contribute to therapeutic outcomes.
Future research should explore the integration of advanced technologies such as chatbots and virtual simulation tools to provide immediate support and personalized interventions. These technologies can offer real-time feedback and tailored therapeutic content, thereby enhancing the comprehensiveness and effectiveness of digital interventions. Moreover, the use of artificial intelligence and machine learning algorithms can enable the development of adaptive interventions that dynamically adjust to the patient's progress and needs, further improving outcomes.
In conclusion, networked psychological interventions hold significant promise for the treatment of PTSD, offering accessible and effective therapeutic options. By providing a comprehensive overview of the methodologies and therapeutic outcomes, this study contributes to the growing body of knowledge on digital mental health interventions. Future research should continue to refine these interventions, leveraging advanced technologies and deepening our understanding of the underlying psychological mechanisms to enhance their efficacy and practical applicability.

Intergenerational cooperation refers to the behavior where individuals sacrifice their present interests for the benefit of future others. It is a decision-making process that necessitates decision-makers to weigh the value of their current interests against the interests of future generations, ultimately making decisions that contribute to sustainable development. To encourage individuals, societies, and nations to adopt sustainable intergenerational cooperation practices, it is crucial to deeply understand the psychological mechanisms that influence people's willingness to forego short-term benefits in favor of intergenerational cooperation.
This study examines the distinctions between intertemporal decision-making, social decision-making, and the role of both option attributes and the choice process in these areas. It demonstrates that intergenerational cooperation encompasses not only the assessment of value in intertemporal and social decision-making but also highlights its distinctive characteristics in the choice stage. By examining the role of decision-making mechanisms, we highlight the significance of temporal and social discounting in the value assessment phase of intergenerational cooperation. Individuals tend to prefer immediate (albeit smaller) payoffs compared to larger delayed payoffs, indicating a tendency toward temporal discounting. Additionally, individuals tend to underestimate the returns to others (especially future generations) and overestimate personal returns, reflecting the phenomenon of social discounting. This dual psychological discounting in the temporal and social domains presents a significant challenge to intergenerational cooperation.
Despite the fact that existing literature typically considers temporal and social discounting separately, their combined impact on intergenerational cooperation has not been adequately addressed. The interaction between these two dimensions constitutes a core, complex, and multidimensional discounting mechanism in intergenerational decision-making. Moreover, their influence on ultimate value choices is nonlinear; the transition from value assessment to final decision-making is subject to complex regulation by dynamic mechanisms that remain underexplored. Contrary to discounting theories that posit that increased temporal and social distance leads to personal benefit maximization, empirical evidence unveils a more nuanced reality. In contexts involving the welfare and survival of future generations, the expected degree of discounting does not intensify, suggesting the presence of intricate dynamic mechanisms that encourage balanced decision-making and underscore the potential for intergenerational cooperation.
However, there is a notable lack of in-depth research on the motivational mechanisms driving intergenerational cooperation across temporal and social dimensions. This gap hinders a clear explanation of the observed reduction in discount rates in specific contexts, thereby limiting our understanding of the motivational mechanisms behind intergenerational decision-making and the precise formulation of strategies to promote intergenerational cooperation. To bridge this gap, we integrate temporal and social discounting into a unified decision theory, analyzing two pivotal mechanisms during the value assessment phase of intergenerational cooperation: the interaction mechanism of discounting, which examines how temporal and social discounting influence each other and jointly shape intergenerational decision-making, and the internal motivational mechanism, which explores how complex dynamic regulation impacts final intergenerational cooperation decisions.
By elucidating the interaction patterns between discounting in temporal and social dimensions and exploring their underlying dynamic mechanisms, we propose the following strategies to foster intergenerational cooperation: establishing "mental accounts" and "imagining future generations" to balance short-term and long-term interests in resource allocation, regulate the influence of internal and external factors, and quantify the extent of individual sacrifice for intergenerational goals. We have formulated the "Discount-Dynamic-Nudge" model to encapsulate these insights.
Expanding upon the "Mental Accounting" strategy, we advocate for a transformative approach that inspires individuals to allocate resources thoughtfully, prioritizing the welfare of future generations. This approach aims to mitigate temporal discounting, the tendency to prioritize immediate gains over long-term benefits. By embracing "Intergenerational Mental Accounting," we encourage individuals to reframe their decision-making processes, giving greater consideration to the future consequences of their choices. By introducing the concept of "Imagining Future Generations," we cultivate a profound sense of responsibility and kinship with those yet unborn, fostering empathy that mitigates social discounting. We delve into the policy and societal implications of this mindset, emphasizing the urgency for strategies that reconcile individual actions with collective long-term aspirations. This shift in mindset, coupled with deepened intergenerational identification, aligns individual decisions with broader, long-term objectives, fostering a more sustainable and equitable future for all.
In conclusion, we advocate for a lifespan perspective to deepen our understanding of the relationship between intergenerational cooperation discounts and dynamic mechanisms. We recommend a comprehensive approach to measuring intergenerational cooperation that integrates discounting mechanisms, dynamic models, and direct assessments of intergenerational cooperation levels, thereby advancing the theory and practice of intergenerational cooperation.

As artificial intelligence (AI), emotional algorithm, and anthropomorphic features rapidly evolve, a new paradigm of human-machine interaction is emerging, characterized by AI ecosystem functioning increasingly as autonomous collaborators rather than mere tools. Central to this transformation is the concept of social presence, which mediates human cognition, emotions, and behaviors toward technology. Traditionally, social presence refers to the sense of being with another entity; within AI context, it extends to how machines are perceived as relational entities capable of engaging in social and emotional exchanges. This study defines the concept, scope, and boundaries of social presence within the evolving landscape of human-machine relationships, spanning Human-Computer Interaction (HCI) to Human-Robot Interaction (HRI) and, more recently, Human-AI Interaction (HAII). These shifts highlight the transition from viewing machines as passive assistants to engaging with them as active partners within social dynamics.
The study aims to redefine social presence in this context by exploring its influence on cognitive, emotional, and behavioral responses to AI. It addresses three core questions: What drives humans to perceive machines as human-like? How do emotional connections with machines form? What behavioral patterns do humans exhibit towards these entities? By addressing these questions, the study uncovers the psychological mechanisms that enable humans to form quasi-social interactions with non-human agents, often blurring the lines between social and artificial actors. Understanding these dynamics is crucial as AI becomes increasingly integrated into everyday life, influencing not only how we interact with technology but also how we perceive its role in our social fabric.
To address these questions, the study develops an integrative theoretical framework that positions anthropomorphism as a precursor, individual factors as moderators, and cognitive, emotional, and behavioral attitudes as outcomes, with social presence serving as a central mediator. Anthropomorphism, defined as attributing human-like qualities to non-human agents, initiates the experience of social presence by making AI systems appear more relatable and human-like. Individual factors further modulate how users perceive and interact with AI, highlighting the complex interplay of personal and contextual elements. This framework illustrates how these factors combine to shape cognitive trust, emotional attachment, and behavioral engagement, offering a comprehensive understanding of new human-machine relationships.
The findings demonstrate that social presence significantly impacts cognition, emotion, and behavior in human-machine interactions. Cognitively, social presence enhances perceptions of AI’s trustworthiness and reliability, reducing perceived risks and uncertainties. Social presence provides a psychological foundation for users to rely on AI for decision-making, mitigating concerns about AI’s competence and reliability. Emotionally, social presence fosters warmth and empathy, deepening emotional bonds between humans and machines. This emotional engagement reflects a growing acceptance of AI as relational entities capable of fulfilling social and emotional roles traditionally reserved for humans, such as offering support. Behaviorally, AI systems that emulate social cues and emotional responses encourage greater acceptance, proactive adoption, and value co-creation.
This research establishes a robust theoretical foundation for understanding the psychological dynamics of new human-machine relationships, emphasizing the transformative role of social presence. It calls for further exploration of anthropomorphism, individual differences, and social presence in immersive digital environments, including virtual spaces such as the metaverse. The study underscores the imperative to address ethical considerations associated with highly anthropomorphized AI, including risks of emotional manipulation, privacy erosion, and over-reliance on AI for social fulfillment. Moreover, the rise of superintelligent AI and advanced emotional algorithms may fundamentally reshape human-machine dynamics, shifting power balances and raising complex questions about control, agency, and social norms. As machines develop their own “machine social psychology,” existing theories of social presence may be challenged, necessitating new research into these evolving dynamics. The study also emphasizes the evolving concept of social presence in the metaverse, where real-time, multimodal interactions with AI-generated avatars will expand the boundaries of human experience. Finally, increasing levels of anthropomorphism could blur the lines between humans and machines, fostering deep emotional attachments and challenging traditional theories like the uncanny valley. Future research should consider generational differences in attitudes towards AI, particularly how younger generations, referred to as the AI-Integrated Generation, may exhibit greater inclusivity, familiarity, and acceptance of human-AI interactions, thereby redefining social presence and reshaping the landscape of human-machine coexistence.

Pro-environmental behavior refers to an action that can minimize negative influences on the natural world and enhance the environment. A large number of pro-environmental behavior measurement tools have been developed, involving scales, individual behavior paradigms, and games. Self-administered scales are the most common measures of pro-environmental behavior, and most of the existing scales built the dimensions based on the behavioral domain, i.e., the scenario in which the behavior occurs, with different scales consisting of different dimensions. The five most frequently cited domains are conservation, transportation, waste disposal, consumption, and social citizenship behaviors.
When using the established scales, a number of studies selected items from the full scales for cultural appropriateness or time-saving. The selected items varied between researchers, reflecting the low standardization of pro-environmental behavior measures. Compared to the established scales, contextual questionnaires are more suitable for experimental research because the pro-environmental behaviors they measure are more amenable to change. However, contextual questionnaires are even less standardized than scales, which often vary depending on the research purpose and cultural adaption.
Behavior paradigms of individual level included laboratory paradigms and field experiments. The indicators of pro-environmental behaviors in field experiments need to be site-specific; researchers could directly observe the pro-environmental behaviors or the behavioral results. The key to designing laboratory behavioral paradigms of pro-environmental behavior is to extract the core behavioral components that reflect individuals’ environmental tendencies and then simulate these core components in the laboratory setting. The existing behavioral paradigms vary widely across behavioral domains; even within the same domain, there is a lack of universally accepted behavioral paradigms. The main issue with individual behavioral paradigms is that the pro-environmental behaviors measured are domain-specific. Consequently, whether results obtained in one domain can be generalized to other domains remains open to discussion. Additionally, even when measuring pro-environmental behavior within the same domain, the behavioral costs associated with different measurement methods vary, reducing the comparability of the results.
The above measures mostly assess individual pro-environmental behavior, while games can measure group pro-environmental behaviors. Resource dilemmas and public goods games are the two most commonly used types of games to measure environment-relevant behaviors. However, the measurement results of the game paradigm can only reflect the pro-environmental tendency when confronted with the conflict between environmental protection and short-term economic interests.
To sum up, the current pro-environmental behavior measurement suffers from low standardization and limited generalizability of measurement results. On the one hand, different tools may measure distinctly different pro-environmental behaviors. The pro-environmental behaviors measured by different tools are not homogeneous or comparable. However, researchers often treat the measurement results from these tools as interchangeable, which hinders the replicability and comparability of study findings. On the other hand, most existing measurements are confined to specific behavioral domains, thereby limiting the generalizability of findings across other domains and restricting practical applications.
The core reason for the aforementioned issues lies in the current high reliance of pro-environmental behavior measurement methods on behavioral domains, coupled with a lack of focus on behavioral attributes, which are defining and distinguishing characteristics of behaviors. This tendency can easily lead to non-equivalence among different measurement tools in terms of fundamental behavioral characteristics. Moreover, differences across domains may not just involve changes in behavioral scenarios but also variations in behavioral attributes themselves.
Therefore, the selection of measurement tools should be based on behavioral attributes. When measuring pro-environmental behavior holistically, researchers should first identify the intended behavioral attributes. It is crucial to follow a “from general to specific” logic, starting with an assessment of the general tendencies of pro-environmental behavior under these attributes, and subsequently measuring specific behaviors within the same attribute. And then “from general to specific”, initially assessing the general tendencies of pro-environmental behavior under these attributes before measuring specific behaviors within the same attribute. When intending to measure specific domain pro-environmental behaviors, researchers must ensure that the measurement tools produce results that align in behavioral attributes with actual behaviors in that domain. In defining variables, results at the overall level can be defined as “pro-environmental behavior,” but it is crucial to specify the behavioral attributes measured, such as pro-environmental behaviors conflicting with economic interests. Studies focusing solely on specific domain pro-environmental behaviors should not directly define variables as “pro-environmental behavior,”" but rather concentrate on the domain and define variables in conjunction with the study’s objectives.
To fundamentally address the standardization of pro-environmental behavior measurement and enhance the generalizability of results, it is imperative to promptly develop standardized scales and behavioral paradigms for pro-environmental behavior. Rigorous reliability and validity testing across diverse samples is essential, with corresponding domain-specific authentic behaviors serving as criterion variables.

In 2023, Asparouhov and Muthén proposed a new framework for structural equation modeling called the penalized structural equation modeling (PSEM). The penalized alignment method exemplifies the utilization of PSEM within the field of measurement invariance testing. The penalized alignment method inherits the benefits of multiple-group exploratory factor analysis, such as estimating cross-loadings, and the benefits of alignment optimization method which uses a component loss function allowing for a certain amount of noninvariant parameters to exist in the model. It also adopts advantages from Bayesian structural equation modeling, such as setting alignment prior distributions for model parameters and testing approximate measurement invariance.
At the same time, this method overcomes limitations of multiple-group exploratory factor analysis, including the need for strong invariance, and low tolerance for errors when using targeted rotation methods. It also addresses shortcomings of the alignment optimization method, such as the ease of mis-specifications when setting reference groups for latent factor means in multiple-factor models and its inability to be applied to multiple-indicators multiple-causes (MIMIC) models. Additionally, it resolves issues with Bayesian structural equation modeling related to measurement invariance testing, such as the requirement for parameter priors to follow a normal distribution, the need for sensitivity analysis, and slower computations. Due to these integrated features, the penalized alignment method has clear advantages over traditional measurement invariance testing methods and holds great promise for future applications.
To illustrate the application of penalized alignment method, a study on work values among college students is employed as an example to showcase the utilization of the penalized alignment method for conducting measurement invariance testing and multiple-group analysis. Multiple-group CFA, CFA-based penalized alignment, and ESEM-based penalized alignment models are used to fit the data.
The results show that the analysis using the CFA-based penalized alignment method outperforms that of traditional multiple-group CFA. Firstly, the CFA-based penalized alignment model holds weak measurement invariance, whereas the traditional multiple-group CFA using WLSMV estimation rejects the strong invariance model, and the weak invariance model cannot be identified. This leads to a significant discrepancy between the multiple-group CFA model and the real data. Secondly, the penalized alignment method not only yields approximate measurement invariance diagnosis for all model parameters but also estimates the latent factor mean parameters, allowing for direct multiple-group comparisons. In contrast, the chi-square likelihood-ratio test in traditional multiple-group CFA rejects the strong invariance assumption, precluding direct multiple-group comparisons of latent factor means.
The results also indicate that the CFA-based penalized alignment method and ESEM-based penalized alignment method are generally consistent, but the ESEM-based penalized alignment method appears to better reflect the real data. First, the ranking of latent factor means provided by the two models across the four institutional type groups is identical for both latent factors, but the absolute differences in the latent factor means among groups vary, and there are slight differences in the significance of pairwise Z-tests. Second, since the two models are not nested, the CFA-based penalized alignment model has a chi-square value of 1403.827 with 32 degrees of freedom, while the ESEM-based penalized alignment model has a chi-square value of 1219.664 with 16 degrees of freedom. The large difference in chi-square values suggests that the ESEM-based penalized alignment method may perform better. Third, regarding the results of approximate measurement invariance tests, the CFA-based penalized alignment model inherently ignores the estimation of cross-loadings, whereas the approximate measurement invariance test results from the ESEM-based penalized alignment method indicate that three of these ignored cross-loadings do not satisfy approximate measurement invariance. Estimating cross-loadings with ESEM-based penalized alignment model can better reflect the real data.
In summary, for the data from the work values scale of university students in this research example, using the ESEM-based penalized alignment model should be the better choice.