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ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

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    Conceptual Framework
    Information integration in dynamic visuomotor control: A Kalman filtering model-based framework
    CHEN Zhongting, ZHANG Ziyang, LIAN Yujing, GAO Tianze
    2026, 34 (9):  1489-1499.  doi: 10.3724/SP.J.1042.2026.1489
    Abstract ( 18 )   PDF (1041KB) ( 14 )   Peer Review Comments
    A central assumption in psychological research is that behavioral responses provide a direct and relatively stable readout of underlying mental processes. This assumption has supported the design of many stimulus-response paradigms, especially in trial-based perceptual and cognitive tasks. However, increasing evidence from dynamic visuomotor tasks suggests that the relationship between sensory input and behavioral output is not fixed. The same physical stimulus may give rise to different response patterns depending on task structure, response format, feedback availability, and temporal continuity. The present proposal addresses this fundamental issue by reconceptualizing the stimulus-response relationship as a dynamic mapping process rather than a static correspondence between a stimulus and an isolated response.
    The major innovation of this study is to introduce a Kalman filter model, grounded in a Bayesian framework, as a quantitative tool for decomposing dynamic visuomotor behavior into separable psychological components. In this framework, the response at a given moment is not treated as an independent reflection of the current stimulus. Instead, it is modeled as the result of recursive integration between current sensory input and the previous behavioral state. The Kalman gain provides a formal estimate of how strongly the system weights current visual information relative to prior response-based prediction. Thus, the model makes it possible to distinguish perceptual encoding noise from information-integration strategy, two components that are usually confounded in raw behavioral trajectories.
    This proposal advances existing work in three related ways. First, it extends continuous psychophysics by using dynamic tracking behavior to infer stable perceptual parameters. Rather than relying only on traditional trial-based discrimination thresholds, the study asks whether continuous manual and eye-movement tracking can recover comparable indices of visual sensitivity. Preliminary data suggest that parameters extracted from manual tracking are meaningfully associated with discrimination noise, whereas eye-tracking parameters show weaker correspondence, possibly due to peripheral processing and saccadic interruptions. This comparison provides a direct test of whether dynamic visuomotor tasks can serve as efficient, ecologically valid tools for perceptual measurement.
    Second, the study moves beyond descriptive modeling by experimentally manipulating the information sources that enter the integration process. Perceptual learning, feedback structure, feedback uncertainty, and trajectory predictability will be systematically varied to determine how they alter model-derived parameters. For example, if perceptual learning improves visual encoding, perceptual noise should decrease and the weight assigned to current sensory input should increase. Conversely, when the stimulus trajectory becomes more predictable, observers may rely more strongly on internally generated predictions or feedback-based priors, leading to a reduction in the Kalman gain. By testing these predictions, the study aims to clarify how perceptual input, feedback information, and prior expectation jointly determine dynamic visuomotor control.
    Third, the proposal links the behavioral model to neural measures. Steady-state visual evoked potentials will be used as neural markers of visual cortical processing during tracking and passive viewing. This design allows the study to ask whether perceptual parameters inferred from behavior correspond to measurable activity in visual cortex. In addition, changes in alpha-band oscillations will be examined to assess whether variations in information integration are accompanied by changes in cognitive resource allocation. This neural component is important because many existing Bayesian or Kalman-filter models of visuomotor behavior remain primarily behavioral; the present proposal explicitly tests whether model parameters have identifiable electrophysiological correlates.
    The theoretical contribution of the study lies in offering a unified account of why static trial-based tasks and dynamic continuous tasks may produce apparently different behavioral signatures. Rather than assuming a strict dissociation between perceptual judgment and action control, the proposed framework treats these differences as consequences of changing information weights under different task structures. In trial-based tasks, the previous response usually contains limited useful information about the current stimulus, so behavior is dominated by current sensory evidence. In continuous tracking tasks, however, the previous response rapidly accumulates stimulus-related information and becomes a reliable predictor of the current state. The apparent difference between static and dynamic tasks can therefore be explained by a shift in integration weights rather than by assuming entirely separate processing routes.
    Methodologically, this study may contribute to the development of faster and more adaptive perceptual assessment tools. Continuous tracking combined with model-based parameter estimation may provide efficient estimates of color sensitivity, stereoscopic sensitivity, and other perceptual capacities. Preliminary findings from color-vision assessment indicate that short segments of tracking data can classify color-vision deficits with high accuracy, suggesting practical value for rapid screening and individualized perceptual evaluation.
    Overall, this proposal aims to build a model-based framework for understanding dynamic visuomotor control. By combining continuous behavioral measurement, Bayesian modeling, experimental manipulation of information sources, and electrophysiological validation, the study seeks to clarify how sensory input, feedback, and prior prediction are integrated over time. Its core contribution is not simply to apply the Kalman filter to psychological data, but to use it as a theoretical and methodological bridge between stimulus input, internal processing, and behavioral response.
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    The effects and mechanisms of connectome-targeted fNIRS neurofeedback intervention for severe working memory decline
    HOU Xin
    2026, 34 (9):  1500-1513.  doi: 10.3724/SP.J.1042.2026.1500
    Abstract ( 10 )   PDF (1783KB) ( 6 )   Peer Review Comments
    Objective
    Working memory decline is a core feature of cognitive aging, with its essence lying in the systematic disruption of large-scale brain network coordination. Severe working memory decline significantly impacts older adults' quality of life and imposes substantial burdens on families and society. However, existing neuromodulation techniques face critical limitations: exogenous approaches (e.g., TMS, tDCS) are constrained to single-site interventions with limited precision, while endogenous neurofeedback based on EEG or fMRI, despite its promise, is hindered by high costs, poor portability, or insufficient spatial resolution for long-term application in older populations. Moreover, emerging network-based neurofeedback studies remain in early exploratory stages, with feedback signals typically simplified to single connectivity strengths or summations of network edges, failing to capture the holistic topology of multi-node dynamic coordination. Notably, no study has yet targeted the severely declined subgroup within the older population from a network connectivity perspective.
    To address these gaps, this study proposes a connectome-targeted fNIRS neurofeedback intervention framework for severe working memory decline, systematically investigating its intervention effects and cognitive transfer mechanisms.
    Study 1: Identification of Specific Network Connectivity Patterns and Target Setting
    This study first adopts an extreme groups approach to classify older adults into severely-declined and function-maintained groups based on working memory performance. Using multivariate pattern analysis (MVPA) based on representational similarity analysis (RSA), we identify network connectivity templates that optimally discriminate between the two groups during a working memory task. These templates serve as the core intervention targets, with the average connectivity pattern of the function-maintained group established as the regulatory goal. This data-driven target identification strategy provides a foundation for subsequent individualized intervention, shifting from experience-based target selection to evidence-based targeting.
    Study 2: Closed-Loop Connectome-Targeted fNIRS Neurofeedback Intervention
    Building on the identified targets, we develop a closed-loop fNIRS neurofeedback training paradigm. Unlike previous studies that simplified feedback signals to single connectivity strengths or summations of network edges, this study employs the spatial similarity between an individual's real-time network connectivity pattern and the target pattern as the feedback index. This index preserves the complete topological structure of network nodes, capturing richer information about multi-node dynamic coordination. A three-group randomized controlled design (connectome-targeted feedback, traditional single-region feedback, and sham feedback) is implemented to systematically evaluate the intervention’s effectiveness, durability, and superiority over traditional single-region feedback approaches.
    Study 3: Cognitive Transfer Effects and Dual-Pathway Mechanisms
    Given that the targeted network serves not only working memory but also multiple other cognitive functions, regulation of this network may induce broad cognitive transfer effects. This study systematically examines transfer effects across four dimensions—transfer type (near vs. far), direction (positive vs. negative), strength (strong vs. weak), and durability (immediate vs. long-term). Furthermore, we propose and test a "dual-pathway transfer" theoretical framework: the "network direct-driven pathway"—directly improving cognitive functions closely associated with the target network, and the "working memory-mediated pathway"—indirectly facilitating broader cognitive functions through enhancement of working memory as a foundational cognitive process. Mediation analysis is employed to differentiate the relative contributions and interplay of these two pathways.
    Theoretical Framework and Innovation
    This study advances a three-level theoretical framework: the essence of working memory aging as large-scale network coordination imbalance, the pathway of endogenous self-regulation for network reshaping, and the mechanism of dual-pathway cognitive transfer. The core innovation lies in shifting intervention targets from single brain regions to large-scale network connectivity patterns, developing a spatially similarity-based feedback index that preserves topological information, and proposing a dual-pathway transfer framework that explains how connectome-targeted regulation produces cognitive gains. This paradigm holds promise for providing a safe, effective, and sustainable intervention for severe working memory decline, while offering a generalizable framework for broader network-targeted neuromodulation strategies.
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    Neural computations of ingroup-outgroup subjective value: Toward precise prediction and intervention in intergroup cooperation and conflict
    ZHANG Hejing, CHEN Yiming, MA Yina
    2026, 34 (9):  1514-1528.  doi: 10.3724/SP.J.1042.2026.1514
    Abstract ( 18 )   PDF (2005KB) ( 7 )   Peer Review Comments
    Intergroup bias, the tendency to favor the ingroup over the outgroup, is a pervasive feature of human social life and a major force shaping cooperation, exclusion, and conflict across group boundaries. Although prior research has documented robust ingroup favoritism and linked it to a range of psychological and neural processes, a core computational question remains unresolved: when people evaluate allocations, payoffs, or interests involving both ingroup and outgroup members, how do they subjectively weight these two sides, and how does this weighting shape intergroup behavior? Existing accounts have identified relevant motives, attitudes, and brain regions, but they have rarely specified the computational process by which ingroup and outgroup interests are differentially valued. The central idea is that intergroup bias arises, at least in part, because individuals do not assign equal subjective weight to ingroup and outgroup interests. Instead, the same or comparable payoffs may be valued differently depending on whether they benefit the ingroup or the outgroup. On this view, intergroup bias is not simply a descriptive tendency or a downstream behavioral pattern; it reflects a specific distortion in social value computation that may help explain why some individuals cooperate across group boundaries, whereas others shift toward parochial defense, exclusion, or aggression. Across five linked studies, the project will test whether individuals differ in how they weight ingroup and outgroup interests, formalize such variation in an Intergroup Reference Point Model, identify its neural basis, evaluate its predictive value for intergroup cooperation and conflict, and examine whether these computations can be modulated by intranasal oxytocin.
    Study 1 will test the project's basic behavioral claim: that individuals assign systematically different subjective weights to ingroup and outgroup interests, and that the magnitude of this weighting bias varies reliably across individuals. Using a large-sample design, participants will be assigned to minimal groups and will complete parallel interpersonal and intergroup decision tasks, including social value orientation measures, an intergroup prisoner's dilemma-maximizing difference task, an intergroup public goods game, and trait assessments. A central goal is to determine whether intergroup valuation can be dissociated from general interpersonal prosociality rather than treated as a simple extension of it. If supported, this distinction will be used to identify theoretically informative profiles, particularly egalitarians and parochial altruists. These groups are expected to be comparable in interpersonal prosociality while differing substantially in the relative value they assign to ingroup versus outgroup interests, thereby providing a basis for the subsequent computational and neuroimaging studies.
    Study 2 will develop the Intergroup Reference Point Model as an individualized computational account of intergroup valuation. Using participants drawn from the key valuation profiles identified in Study 1, followed by validation in a broader sample, the study will model subjective evaluations of ingroup-outgroup allocation options as a weighted combination of ingroup and outgroup interests. From this model, it will derive an angular reference-point parameter that captures each individual's preferred trade-off between ingroup and outgroup interests. The key question is whether intergroup bias can be formalized not simply as a categorical preference or observed choice tendency, but as a continuous and interpretable computational signature. By quantifying the relative weight assigned to ingroup and outgroup interests, the model will provide a continuous index of the degree to which a person privileges ingroup over outgroup interests, enable direct comparison between egalitarian and parochial-altruistic valuation under matched levels of interpersonal prosociality, and generate model-based parameters for the subsequent neural and behavioral analyses.
    Study 3 will examine the neural implementation of intergroup subjective value computation by integrating model-derived parameters with functional MRI. Using valuation profile as a group factor and model parameters as continuous indices of individual difference, the study will test where and how the brain represents the subjective weighting of ingroup and outgroup interests, computes their integrated subjective value, and tracks the distance between a given allocation and an individual's preferred intergroup trade-off. Analyses will combine ROI-based and whole-brain parametric modulation within a first-level GLM, multivoxel pattern analysis, DCM-based effective connectivity analysis, and graph-theoretical characterization of whole-brain functional networks. The main issue is whether intergroup value computation relies on a common neural mechanism or diverges between egalitarians and parochial altruists despite comparable interpersonal prosociality. In particular, the study will contrast an amygdala-centered, fairness-sensitive route with an orbitofrontal, strategically weighted route for intergroup value computation. This work is expected to provide a mechanistic neural framework for understanding how subjective value is computed when social interests are divided along group boundaries.
    Study 4 will test whether intergroup subjective value computation and its neural correlates predict behavior across intergroup contexts that vary in antagonistic intensity. If proposed intergroup valuation is a behaviorally meaningful computational process, then its parameters and neural signatures should predict which individuals cooperate across group boundaries and which shift toward parochial cooperation, punitive hostility, or aggression as conflict escalates. To test this, the study will use a set of interactive paradigms spanning lower- to higher-conflict contexts and examine whether individuals who assign more balanced subjective value to ingroup and outgroup interests show greater cross-group cooperation, whereas those who assign disproportionately greater weight to ingroup interests show stronger parochial cooperation, greater willingness to punish outgroup members, and more aggressive responding as antagonism increases. At the neural level, the study will ask whether these behavioral tendencies are supported by distinguishable processing routes: one relying more heavily on fairness-sensitive affective valuation and the other relying more heavily on strategically weighted intergroup value computation. In this way, the project will move from computational and neural characterization to behavioral prediction.
    Study 5 will introduce a neurobiological manipulation by testing whether intranasal oxytocin can recalibrate intergroup subjective value and, through that route, alter cooperation and conflict across group boundaries. Building on the computational and neural framework established in the preceding studies, the study will examine three possibilities: whether oxytocin promotes a more balanced valuation of ingroup and outgroup interests, selectively attenuates biased valuation in parochial altruists, or instead amplifies defensive intergroup responding under threat. Crucially, the study will not assume that oxytocin exerts a uniform prosocial effect. Instead, it will test whether oxytocin's impact depends on pre-existing valuation style and social context. By treating oxytocin as a probe of the plasticity of the Intergroup Reference Point, the project will extend the framework from explanation and prediction to intervention.
    Overall, this proposal seeks to establish an integrated account of intergroup bias by explaining how people assign subjective weight to ingroup and outgroup interests, how this value computation is implemented in the brain, how it shapes cooperation and conflict, and how it may be modulated through oxytocin-based intervention. Across five studies, the project will move from identifying individual differences in intergroup valuation, to formalizing them computationally, to specifying their neural mechanisms, behavioral consequences, and biological malleability. The proposed work is expected to provide a quantitative and neurally grounded framework for understanding, predicting, and potentially regulating cooperation and conflict across group boundaries.
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    Shaping mechanisms and evidence-based governance pathways of bystander defending behaviors in school bullying
    LI Huanhuan, YAN Kai
    2026, 34 (9):  1529-1543.  doi: 10.3724/SP.J.1042.2026.1529
    Abstract ( 19 )   PDF (713KB) ( 23 )   Peer Review Comments
    While school bullying is widely recognized as a pervasive group phenomenon, bystanders’ defending behaviors play a critical role in terminating bullying events and mitigating the long-term psychological harm to victims. However, previous research has predominantly relied on static, linear analytic approaches to identify isolated predictors of defending behaviors. These traditional approaches fail to elucidate the dynamic, intrinsic psychological processes underlying why bystanders make divergent behavioral choices in complex, real-world contexts. To address this critical theoretical and methodological gap, the present study shifts from a static trait perspective to a dynamic situational interaction perspective. It aims to construct a novel Three-Stage Psychological Decision-Making Framework to systematically reveal the shaping mechanisms of bystander defending behaviors and develop evidence-based, systemic governance pathways.
    The proposed framework conceptualizes bystander defending not as an automatic prosocial reflex, but as a complex, dynamic decision-making process influenced by multi-level factors.
    (1) Situational Cue Processing Stage: Bystanders quickly process environmental cues to determine the necessity of intervention, generating an "initial defending intention."
    (2) Psychological Calculation Stage: Bystanders engage in deep cognitive processing, systematically weighing perceived social risks (e.g., peer rejection, retaliation), potential benefits (e.g., moral satisfaction, social status enhancement), and their own defending self-efficacy. This calculative stage modifies, strengthens, or inhibits the initial intention, transforming it into a "stable defending intention."
    (3) Behavioral Selection and Implementation Stage: Based on the stable intention, bystanders strategically select specific behavioral outputs—opting for direct defending (e.g., confronting the bully), indirect defending (e.g., comforting the victim, reporting to teachers), or passive bystanding. Crucially, the model posits that individual psychological traits and school environmental factors (such as classroom anti-bullying norms) act as boundary conditions. Rather than merely predicting behavior directly, they alter the relative weights of risk, benefit, and efficacy during the psychological calculation stage.
    To empirically validate this framework and develop targeted interventions, this study employs a cutting-edge, multi-method approach across three interconnected sub-studies.
    Study 1: Factor Identification and Configuration Analysis. Utilizing a mixed-methods approach, Study 1 integrates machine learning algorithms (Support Vector Machine, Random Forest, XGBoost) and SHAP value analysis with qualitative in-depth interviews to accurately identify the average marginal contributions of individual, interpersonal, and environmental factors. Furthermore, fuzzy-set Qualitative Comparative Analysis (fsQCA) is applied to uncover the asymmetric, configurational pathways through which these multi-level factors jointly shape defending behaviors, breaking away from traditional isolated variable analyses.
    Study 2: Experimental and Computational Modeling. Study 2 transitions from establishing variable relationships to precise process explanation. Studies 2a-2d utilize traditional scenario-based experiments, employing highly controlled, text-only situational descriptions to isolate and test the effects of specific cues—particularly the severity of the bullying incident itself—alongside risk-benefit assessments and self-efficacy. Building upon this empirical foundation, Study 2e introduces cognitive computational modeling. By constructing a specific utility function that mathematically integrates perceived social risk, potential benefit, and self-efficacy, and utilizing a multinomial logit model, this study maps subjective utility values to exact behavioral choice probabilities.
    Study 3: Intervention Simulation and Field Validation. To bridge theoretical modeling and practical application, Study 3 designs and tests evidence-based intervention pathways. Study 3a employs Agent-Based Modeling (ABM) to simulate the dynamic interactions among heterogeneous student agents within a virtual school environment. By manipulating individual and environmental parameters, the ABM identifies the most potent and risk-controllable intervention strategies, significantly reducing real-world trial-and-error costs. Guided by the simulation results, Study 3b implements a quasi-experimental longitudinal field intervention. Utilizing multi-source data (self-reports, peer nominations, and teacher nominations), it rigorously evaluates the effectiveness of optimized school climate interventions in promoting bystander defending and reducing overall bullying prevalence.
    This research fundamentally shifts the paradigm of bystander defending research from factor identification to process modeling. By quantifying the psychological calculation process through advanced modeling and simulating interventions via ABM, it offers robust, evidence-based insights for policymakers and educators. It highlights the necessity of developing comprehensive school bullying governance models that prioritize ecological and structural environmental enhancements over mere individual behavioral correction.
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    The crowdsourced online mental health services: Quality identification, influencing mechanisms, and intervention strategies
    KUANG Lini
    2026, 34 (9):  1544-1555.  doi: 10.3724/SP.J.1042.2026.1544
    Abstract ( 9 )   PDF (504KB) ( 6 )   Peer Review Comments
    Crowdsourced online mental health services have emerged as an important complement to traditional one-to-one counseling by allowing individuals with psychological concerns to post questions and receive responses from multiple service providers. This model helps meet patients’ diverse needs, improves response efficiency, and partly alleviates the imbalance between the rising demand for mental health support and the limited supply of professional services. However, because the entry threshold for service providers is relatively low, the quality of psychological advice on such platforms varies greatly. Low-quality responses may mislead patients, delay appropriate treatment, and undermine platform development. Existing studies have mainly examined online health services from the patient perspective or focused on one-to-one service settings, while limited attention has been paid to how service quality is identified, influenced, and improved in crowdsourced online mental health services from the service-provider perspective. To address this gap, this study develops an integrated framework based on the logic of “identification-influence-intervention.”
    First, this study proposes an automated approach to identifying service quality in crowdsourced online mental health services. Drawing on counseling theory, it conceptualizes service quality as a multidimensional construct. High-quality psychological responses should help patients clarify their problems, understand the causes and consequences of their distress, and develop feasible coping strategies. In online text-based interactions, responses also include opening and closing components that establish rapport and complete communication. Accordingly, this study develops a five-dimensional evaluation framework for expert assessment. Compared with prior studies that rely on fragmented or inconsistent expert criteria, this framework offers a basis for measuring psychological service quality. Building on this framework, the study further proposes a multi-task learning model to predict service quality automatically. In addition to the main task of predicting response quality, the model incorporates emotional matching and informational matching as auxiliary tasks. Emotional matching captures the extent to which a response fits the help-seeker’s emotional needs, while informational matching reflects how accurately and comprehensively the response addresses the patient’s problem and context. Jointly modeling these related tasks can improve both predictive accuracy and theoretical interpretability.
    Second, this study investigates how the professional level of the first service provider affects the participation behavior of subsequent providers. In crowdsourced online mental health services, the first respondent serves as an observable peer whose status and response quality provide important signals to later participants. Based on peer effects theory and expectancy-value theory, this study argues that the first provider’s professional level may generate two competing mechanisms. On the one hand, a highly professional first provider may create a competition-suppression effect. Because patients face substantial information asymmetry and may rely on professional signals when selecting or rewarding answers, later providers may perceive a lower probability of success and become less willing to participate. On the other hand, a highly professional first provider may generate a social-influence effect by signaling that the patient’s problem is serious or worthy of attention, thereby encouraging additional providers to contribute. The study further examines the moderating roles of monetary reward and patient psychological distress. Higher monetary rewards may increase the perceived value of participation and weaken the discouraging effect of competition. In contrast, higher psychological distress may strengthen the perceived advantage of the first high-level provider, making subsequent providers more cautious about participation.
    Third, this study explores how providers’ own experience and peer experience jointly shape the dynamic improvement of service quality. Based on learning theory, the study argues that providers can improve later responses by reflecting on prior service experiences. However, the benefits of self-experience may exhibit diminishing marginal returns as additional experience provides less new information and may lead to routine response patterns. The study also distinguishes between successful and failed experiences, proposing that successful experiences may have a stronger positive effect because they reinforce effective practices and enhance self-efficacy. Meanwhile, the public visibility of responses enables providers to learn from peers. By observing others’ high-quality responses, providers may absorb useful knowledge and improve their own service quality. Nevertheless, peer experience may also show diminishing returns due to information redundancy and cognitive overload. Furthermore, the study proposes a complementary relationship between self-experience and peer experience: providers with richer personal experience may better integrate knowledge gained from peers, thereby amplifying the positive effect of peer learning.
    This study contributes to the literature in three ways. It develops a theory-based framework for identifying online mental health service quality, enriches research on service-provider behavior in crowdsourced health platforms, and extends the understanding of service quality improvement by treating quality as a dynamic process shaped by individual and peer learning. Practically, the findings can help platforms design automated quality evaluation tools, optimize participation incentives, manage peer influence, and develop targeted strategies to improve the quality of crowdsourced online mental health services.
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    Dancing with AI teammates: The formation and influence mechanism of job crafting in human-AI teams from the team adaptation perspective
    WANG Lei, QIAN Caixuan, XU Jie
    2026, 34 (9):  1556-1576.  doi: 10.3724/SP.J.1042.2026.1556
    Abstract ( 11 )   PDF (751KB) ( 10 )   Peer Review Comments
    Against the backdrop of a workplace where AI teammates and human employees increasingly work side by side (Hillebrand et al., 2025), this research draws on team adaptation theory and develops a context-assessment-crafting-adaptation framework to systematically examine human-AI team job crafting. Specifically, by conceptualizing the theoretical meaning of human-AI team job crafting and unpacking its cross-level process of dynamic emergence, this research explores the formation mechanisms, consequences, and boundary conditions of job crafting at both the individual and collective levels in human-AI teams.
    First, Study 1 explores how the meaning of job crafting is extended in Human-AI collaboration contexts. Prior research has mainly drawn on role theory to conceptualize job crafting as employees’ bottom-up physical or cognitive changes to the task and relational boundaries of their work (Wrzesniewski & Dutton, 2001), or on the job demands-resources model to define it as employees’ self-initiated changes to job demands and job resources according to their own abilities and needs (Tims & Bakker, 2010). However, as AI teammates enter teams and participate in task execution, decision-making processes, and even idea generation, the work functions, task demands, and resources available to human employees and their teams are significantly transformed (Raisch & Fomina, 2025). At the same time, the “algorithmic black box” nature of AI teammates can further complicate within-team collaboration (Jussupow et al., 2021), which has important implications for the efficiency of human-AI teamwork. Accordingly, extending the micro-level concept of individual job crafting and clarifying the conceptualization of human-AI team job crafting can enhance the theoretical understanding of job crafting in human-AI collaboration contexts.
    Second, Study 2 uncovers the mechanisms underlying the emergence of employee job crafting, while Study 3 further explores the formation mechanisms of team job crafting in human-AI teams. As a key means by which employees and teams respond to environmental changes and develop competitive advantages, job crafting has been shown to have a positive impact on work engagement and job satisfaction (Tims et al., 2013). However, in human-AI collaboration contexts, it remains unclear how human employees and their teams engage in job crafting; that is, the preceding mechanisms of job crafting are still not well-understood. This gap is particularly significant considering that enhancing the efficiency of human-AI collaboration has become a central issue in artificial intelligence research (Brynjolfsson, 2022), and that human-AI teams are increasingly emerging as novel units of task execution and decision-making in organizations (Zercher et al., 2025). Examining the antecedents of human employees’ job crafting and then extending the analysis to the pathways through which human-AI team job crafting develops can thus enrich current research on job crafting.
    Finally, Study 4 focuses on human-AI teams to unpack the dynamic emergence of team job crafting, along with its outcomes and boundary conditions. Team-level job crafting is not merely the aggregation of identical job-crafting efforts by individual team members; instead, it is an implicit process of collective effort carried out by the team as a whole (Tims et al., 2013). By delineating the pathway through which human employees’ job crafting evolves into human-AI team job crafting, Study 4 contributes to a more in-depth understanding of the team job-crafting process. Moreover, prior research has demonstrated that team job crafting is positively correlated with team members’ work engagement and team performance (McClelland et al., 2014; Tims et al., 2013). However, whether human-AI team job crafting similarly boosts human-AI team performance, as well as the boundary conditions under which such effects occur, remains under-explored (Siemon et al., 2025; Zercher et al., 2025). Indeed, although AI teammates may improve team operating efficiency, they may also weaken team cohesion and trust (Baird & Maruping, 2021). Accordingly, Study 4 focuses on the team-level outcomes and boundary conditions of human-AI team job crafting, providing theoretical insights that can guide job-crafting practices in human-AI teams. By shifting attention from dyadic human-AI interaction to human-AI teams as adaptive work systems, this research advances job crafting theory, extends team adaptation theory to AI-enabled team contexts, and offers a micro-level account of organizational AI transformation.
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    From algorithmic “mind-reading” to linguistic “mind-winning”: The linguistic mechanisms of promotional text in nudging AI recommendation
    LU Changbao, ZHENG Yaxin, LI Lieyu
    2026, 34 (9):  1577-1589.  doi: 10.3724/SP.J.1042.2026.1577
    Abstract ( 11 )   PDF (651KB) ( 2 )   Peer Review Comments
    In the contemporary digital economy, Artificial Intelligence (AI) recommendation systems have emerged as a pivotal tool for enhancing decision-making efficiency and driving sales growth. By leveraging big data and machine learning, these systems evoke a sense of “cognitive surprise” in user, where the algorithm seemingly understands the user better than they understand themselves. However, this high degree of personalization often triggers a “privacy-personalization paradox”, leading to algorithm aversion, privacy concerns, and “filter bubbles”. While existing research has predominantly focused on optimizing algorithmic accuracy or interface design, the linguistic dimension—specifically how promotional language can serve as a choice architecture to nudge consumer behavior—remains under-explored. This research introduces the innovative concept of “Promotional Text Nudging” to address the systemic challenges of AI recommendations, such as the cold-start problem and privacy-induced resistance. Drawing upon Nudge Theory and the research paradigm of conditional clauses in promotion decision-making, the study constructs a comprehensive “User-Product-Context-Language” analysis framework. It posits that promotional language, characterized by conditional restrictions and material incentives, can transform the rigid logic of algorithms into persuasive narratives that convey corporate benevolence and induce an emotional “hot state” in consumers.
    The research is structured into four progressive sub-studies that track the transition from external linguistic stimuli to internal psychological drivers. First, the study investigates the completeness of promotional conditional clauses and its impact on recommendation attention. Utilizing Cognitive Resource Theory, it explores how the “If... then...” structure serves as a linguistic nudge. By explicitly stating the conditions for obtaining benefits, a complete conditional clause shifts the user’s limited cognitive resources from privacy risk assessment toward gain-oriented processing. This transition enhances promotional involvement and mitigates privacy concerns, ultimately maintaining sustained attention on the recommended content. Second, the research explores the self-relevance of promotional text and its role in reinforcing product preferences. This section examines how linguistic cues—such as the choice of grammatical subjects—induce an “illusion of fit”. In the low-context environment of AI recommendations, self-relevant language acts as a bridge, connecting the product’s objective attributes with the consumer’s subjective goals. This process of meaning construction transforms a digital recommendation into a personalized self-reward, particularly for hedonic products where emotional resonance is paramount. Third, the study focuses on the functional division of key promotional language regarding emotional arousal and intention guidance. Based on Self-Determination Theory, it argues that while AI recommendations often limit autonomy, a well-structured promotional choice framework allows users to internalize external incentives. Conditional language facilitates the perception of corporate benevolence and autonomy, while incentive language triggers prospective emotions such as hope and joy. Together, these mechanisms guide the user from mere recognition of the recommendation to an active intention to accept it. Fourth, the research adopts a holistic perspective on multimodal linguistic cues and contextual adaptation. It investigates how auxiliary promotional words, language-product-context alignment, and visual presentation (paralinguistics) synergistically drive instantaneous decisions. By applying Dual Coding Theory, the study reveals that the integration of vivid sensory language with optimized visual layouts enhances processing fluency. This heightened fluency fosters swift trust, allowing consumers to make intuitive, high-speed evaluations of the recommendation’s legitimacy and value. In conclusion, this research systematically elucidates the linguistic mechanisms through which promotional texts mitigate negative user responses and enhance AI recommendation effectiveness. Theoretically, it contributes to the intersection of linguistics and intelligent recommendation by integrating nudge theory with psycholinguistic principles to construct an “Attention-Sensemaking-Motivation-Decision” framework. This framework effectively bridges the gap between algorithmic logic and humanistic persuasion, offering a new path for linguistic empowerment in AI applications. Practically, the study provides a structured knowledge base for platforms to optimize recommendation texts, enabling them to move beyond mere “mind-reading” toward a more empathetic and effective “mind-winning” strategy. These insights are particularly valuable for resolving cold-start issues, weakening algorithm aversion, and fostering a more autonomous and positive digital consumption experience.
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    The impact of generative AI personalized recommendations on tourists’ travel decision-making
    SONG Xiaoxiao, LING Xiaodie, GU Huimin, MA Shuang
    2026, 34 (9):  1590-1605.  doi: 10.3724/SP.J.1042.2026.1590
    Abstract ( 19 )   PDF (759KB) ( 10 )   Peer Review Comments
    Generative Artificial Intelligence personalized recommendations play a critical role in tourism information search. How to leverage GAI personalized recommendations to reshape the information search experience and enhance decision-making efficiency has become an urgent issue for the tourism industry. Grounded in information search-related theories and the 5A model, this study systematically investigates the effects of different types of GAI personalized recommendations namely proactive versus reactive recommendations, recommendation with high versus low adaptive algorithms and recommendations provided by GAI alone versus human-GAI collaboration on tourists’ information search behaviors and decision-making across three pre-travel stages: travel need recognition, itinerary planning, and booking decision-making.
    The theoretical contributions of this study are structured as follows. First, based on the core characteristics of users’ information search behaviors across different pre-travel stages, this study proposes a stage-based classification of GAI-driven personalized recommendations. Most existing studies treat the pre-travel stage as a single holistic phase, overlooking heterogeneity in users’ needs, motivations, and decision tasks across stages. This study therefore adopts the 5A model and refines the pre-travel stage into three sequential phases: travel need recognition, itinerary planning, and booking decision-making. These stages exhibit a progressive relationship, in which need recognition provides foundational information for itinerary planning, and itinerary planning, in turn, enables booking decisions. Accordingly, this study proposes stage-specific types of GAI personalized recommendations and emphasizes the matching relationship between recommendation types and stage-specific search tasks and informational needs, thereby extending the application of the 5A model in tourism information search research.
    Second, this study develops a theoretical framework explaining how different types of GAI personalized recommendations influence tourism information search behaviors and decision-making across the three pre-travel stages, thereby enriching and advancing the theoretical system of tourism information search research. The dynamic interaction mechanisms of GAI, heterogeneous user characteristics, and diverse tourism contexts introduce uncertainty into the effects and pathways through which personalized recommendations influence decision behaviors. By systematically examining how different recommendation types operate across pre-travel stages, this study fills a gap in the literature on GAI-enabled tourism information search and highlights the importance of understanding GAI personalization from an information search theory perspective.
    Third, this study reveals the underlying mechanisms and boundary conditions through which different types of GAI personalized recommendations influence users’ psychological processes and decision-making behaviors across pre-travel stages, thereby advancing research on the integration of personalized tourism marketing and AI technologies. Existing studies primarily focus on the overall effectiveness or single-dimensional attributes of personalized recommendations, with limited attention to the heterogeneous mechanisms of different recommendation types. By incorporating stage-specific task characteristics, such as cognitive load, perceived risk, emotional engagement, and information needs, this study explains how different GAI-driven recommendation types affect decision-making through psychological mechanisms including creativity, trust, and perceived information comprehensiveness. This contributes to a more comprehensive understanding of the integration between GAI and personalized marketing and addresses gaps in the classification and mechanism analysis of GAI recommendation types.
    This study also offers significant practical implications. Although GAI is increasingly integrated into tourism information search and personalized recommendation systems, tourism enterprises still face challenges such as overly uniform recommendation strategies, inaccurate user need identification, and poor alignment between recommendation content and search tasks. By classifying GAI recommendations according to pre-travel stages and emphasizing the matching between recommendation types and task characteristics, this study provides actionable insights for optimizing information search experiences and improving recommendation efficiency.
    Specifically, during the travel need recognition stage, users are more sensitive to the attractiveness and inspirational value of information. Enterprises should therefore adopt more creative and exploratory recommendation strategies to stimulate travel interest and demand. During the itinerary planning stage, users focus more on information integration, comparative evaluation, and comprehensibility; thus, recommendations should emphasize interpretability and comparability of travel plans. During the booking decision stage, users prioritize trustworthiness, transparency, and risk controllability. At this point, high-reliability human-GAI collaborative recommendations can enhance decision confidence and improve conversion outcomes. However, for standardized, low-risk products such as attraction tickets, autonomous GAI recommendations may provide superior user experiences due to faster response and smoother interaction.
    Finally, this study provides practical guidance for the design of GAI-based tourism recommendation systems. The findings demonstrate that different recommendation types influence user psychology and decision-making through mechanisms. Therefore, system developers should enhance the capability of identifying users’ information search stages and dynamically adjust recommendation logic and information presentation accordingly. In addition, recommendation systems should strengthen process transparency and interpretability by presenting recommendation rationales, reference information, generative logic, and key constraints. This can improve users’ understanding of recommendations and reduce concerns regarding AI hallucinations and algorithmic “black-box” effects. Furthermore, incorporating human-GAI collaborative recommendation and process visualization can enhance user trust and further improve the effectiveness of GAI personalized recommendation systems in tourism contexts.
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    Research Method
    A cognitive-computational framework for studying help-seeking decision-making
    LUO Haocheng, DU Wei, WEI Lianting, ZHOU Xiaolin, GAO Xiaoxue
    2026, 34 (9):  1606-1628.  doi: 10.3724/SP.J.1042.2026.1606
    Abstract ( 12 )   PDF (909KB) ( 15 )   Peer Review Comments
    When faced with challenging problems, individuals need to weigh the pros and cons to decide whether and from whom to seek help in order to obtain assistance from others. Such help-seeking decision-making constitutes a crucial foundation for human cooperation and adaptation. However, fragmented and non-quantitative research perspectives and methods in previous studies have hindered the construction of a systematic knowledge framework and limited the quantitative analysis of the trade-offs and integration of core cognitive components. Consequently, the cognitive-computational and neural mechanisms underlying help-seeking decisions remain poorly understood. From an integrated and quantitative perspective, this review proposes a cognitive-computational framework for studying help-seeking decision-making.
    First, this review synthesizes previous theoretical and empirical studies to address the fragmentation of existing research on help-seeking. Although previous studies have identified a variety of factors related to help seeking, these factors have often been examined separately, making it difficult to construct a systematic account of help-seeking decision-making. To address this issue, the present review summarizes three stages of the help-seeking process—need perception, interpersonal request, and reciprocal exchange—and distills three corresponding core cognitive components: perceived help-seeking benefit, anticipated social rejection cost, and anticipated reciprocity anxiety cost. On this basis, the review further proposes three key aspects for studying help-seeking decision-making: decision generation, dynamic adjustment, and the moderating role of dyadic interaction features.
    Second, this review further extends and applies rational and bounded rationality decision theories to the study of help-seeking decision-making, thereby constructing a systematic set of candidate cognitive-computational model hypotheses based on the three key aspects. These model hypotheses include rational models, bounded rational heuristic models, and hybrid models combining the two. Rational models assume that individuals weigh and integrate different cognitive components, including perceived benefits and anticipated costs, in a relatively systematic manner, so that help-seeking decisions are generated and dynamically adjusted through value-based trade-offs, integration, and social learning. In contrast, bounded rational heuristic models assume that, under conditions such as uncertainty, limited information, and cognitive constraints, individuals may actively ignore part of the available information and rely on simpler decision rules or more limited processing strategies, thereby making decisions more quickly and with lower cognitive demands, and in some cases even more accurately. In addition, this review proposes hybrid models that combine rational and bounded rational heuristic strategies, thereby extending existing accounts of how help-seeking strategies may shift between these two modes and suggesting that rational and bounded rational heuristic processes are better understood not as a static dichotomy, but as dynamically interacting processes within individuals.
    Third, based on the proposed cognitive-computational model hypotheses, this review outlines key scientific questions and methodological prospects for future research on the cognitive-computational and neural mechanisms underlying help-seeking decision-making. Future research needs to clarify when individuals rely on rational or bounded rational heuristic strategies, how core cognitive components are represented, weighed, and integrated to generate and adjust help-seeking decisions, and how dyadic interaction features and other individual difference factors shape these processes. Methodologically, future studies should develop interactive paradigms that can repeatedly elicit and quantitatively measure help-seeking decisions, while collecting behavioral data to test, compare, and refine rational models, bounded rational heuristic models, and hybrid models, and to combine computational modeling with neuroimaging methods such as fMRI, EEG, and MEG to reveal the cognitive-computational and neural mechanisms underlying help-seeking decision-making. In addition, future studies could incorporate relevant individual-difference indicators, thereby providing a quantitative basis for the precise identification of related problems and for targeted intervention.
    Overall, this review addresses the fragmentation and non-quantitative nature of previous help-seeking research by proposing an integrated cognitive-computational framework. It summarizes three stages of the help-seeking process, distills the corresponding core cognitive components, constructs a systematic set of candidate cognitive-computational model hypotheses, and outlines key scientific questions and methodological prospects for future research. Together, these contributions provide a foundation for future research on the cognitive-computational and neural mechanisms underlying help-seeking decision-making and related domains.
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    Time scales in intensive longitudinal data: Modeling, comparison and application of discrete time and continuous time
    SANG Jieyu, LIU Yuan, WEI Dongtao
    2026, 34 (9):  1629-1645.  doi: 10.3724/SP.J.1042.2026.1629
    Abstract ( 8 )   PDF (1848KB) ( 8 )   Peer Review Comments
    In recent years, dynamic research has become an important topic in longitudinal studies, focusing on the temporal changes of psychological constructs across repeated measurements. Dynamic effects typically include autoregressive and cross-lagged effects, emphasizing the dependence of individual’s current state on their past states. With advances in data collecting techniques, dynamic effects could be investigated through intensive longitudinal data (ILD), which obtains data through rapid and frequent assessments.
    The ILD framework typically employs two time scales for research design and data analyses: discrete time (DT) and continuous time (CT). These two approaches differ substantially in their theoretical assumptions and modeling approaches. DT models are based on the assumption of equal time intervals, analyzing equally spaced dynamic processes. This can limit their use when observations are not equally spaced. In contrast, CT models are based on an underlying continuous process, allowing for unequal time intervals and enabling the modeling of dynamic effects across different time intervals within a cohesive framework. Therefore, CT models offer greater flexibility regarding time intervals, making it necessary to compare and organize the two approaches within a unified framework.
    Based on this background, the present study uses ILD and takes a first-order autoregressive model as an example to systematically compare DT and CT modeling frameworks. We begin by reviewing classical models within both the DT and CT frameworks separately, including the autoregressive model, multilevel autoregressive model, and (residual) dynamic equation models. Next, we compare the construction of models between DT and CT, establishing a transformation relationship between the two approaches. Additionally, we address a crucial issue: the time interval. This includes defining how the time interval is determined and how to manage unequal intervals within each framework.
    In addition, we use an emotional dataset from an experience sampling method study as an empirical example to demonstrate the modeling procedures under both approaches. Our findings show that, before data analysis, DT models require imputation of missing time points within equally spaced intervals, whereas CT models use the original observations. As a result, the number of usable time points differs the two approaches. In terms of parameter estimation, the two models produce marginally identical results for trend parameters and dynamic parameters (i.e., autoregressive and cross-lagged effects). For the present dataset, although the assumption of equal time intervals is not met, the imputation procedure in the DT model performs well, and the two models yield comparable results.
    In conclusion, we provide several practical recommendations. First, it is essential to determine whether higher-order dynamic effects are defined. The parameters in CT models can more accurately represent such sophisticated dynamic processes. Second, model selection should be guided by the research design and the characteristics of the data. When data is collected at random time intervals or has significant missing values, CT models are more suitable. In contrast, DT models can serve as a more parsimonious alternative when the violation of the equal-interval assumption is not severe. Third, the choice of time intervals should align with the sampling frequency. In this case, CT models offer greater flexibility in rescaling time. Finally, for better clarity, results from both CT and DT models should be presented on a DT scale, allowing for a clearer understanding of the dynamic parameters’ practical implications.
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    Regular Articles
    Safety learning and anxiety disorders: A three-stage neural mechanism framework
    DONG Zhanpeng, ZHANG Jie, ZHANG Yarui, LEI Yi
    2026, 34 (9):  1646-1662.  doi: 10.3724/SP.J.1042.2026.1646
    Abstract ( 9 )   PDF (2063KB) ( 8 )   Peer Review Comments
    Safety learning refers to an adaptive psychological mechanism through which individuals identify safety signals that predict the absence of threat, thereby inhibiting fear responses and facilitating effective adaptation to complex environments. Impairments in this mechanism have been closely linked to a wide range of anxiety and related disorders. However, existing research has predominantly interpreted the pathogenesis of anxiety from the perspectives of fear learning or fear extinction, which has proven insufficient to fully account for core clinical features such as difficulties in safety discrimination, diminished positive emotional experience, and failures in fear inhibition. In light of these limitations, the present article systematically reviews evidence from animal models, human behavioral studies, and neuroimaging research, and proposes a three-stage neural framework of safety learning—comprising safety perception and evaluation, safety acquisition, and safety expression—to more comprehensively characterize the dynamic processes of safety learning and their abnormalities in anxiety disorders.
    According to this framework, safety learning can be divided into three stages: (1) Safety perception and evaluation: At this stage, individuals detect external cues and rapidly evaluate their safety value. This process primarily relies on functional networks involving the thalamus, the sensory cortices, the insula, and the amygdala, which jointly support the early analysis of whether a stimulus signals safety. (2) Safety acquisition comprises two interrelated sub-processes: The first is the formation and maintenance of safety associations, whereby stable links between conditioned stimuli and non-threatening outcomes are established through prediction-error-driven learning. The second is the acquisition of positive affect, whereby repeated exposure to safe outcomes confers positive emotional value to safety cues. This stage is supported by midbrain-striatal dopaminergic circuits that facilitate both safety association learning and positive emotional experience, while the long-term consolidation of safety associations depends on coordinated interactions between the hippocampus and the prefrontal cortex, providing a foundation for the subsequent retrieval and utilization of safety memories.(3) Safety expression, which refers to the effective use of learned safety signals to inhibit fear responses. The neural mechanisms underlying this stage involve interactions among limbic regions (such as the hippocampus and amygdala), the prefrontal cortex, and temporal cortical areas. These regions jointly support the context-dependent retrieval of safety memories, thereby enabling the suppression of fear-related behavior.
    Building on this framework, the article further delineates stage-specific neural deficits in individuals with anxiety and related disorders. During the perception and evaluation stage, these individuals exhibit exaggerated threat perception and insufficient top-down cognitive regulation. During the acquisition stage, they show reduced efficiency in prediction error coding and attenuated positive emotional experience associated with safety signals. During the expression stage, impairments are evident in the retrieval of safety memories and in the inhibition of fear responses. Each of these behavioral abnormalities is accompanied by corresponding dysfunctions in specific brain regions and neural circuits.
    Finally, the article highlights several directions for future research and clinical application. First, future studies should strengthen the use of conditioned inhibition paradigms in investigations of safety learning in anxious populations. To enhance ecological validity and translational relevance, researchers are encouraged to incorporate naturalistic stimuli with semantic or social attributes and to combine multimodal neural measurements with multivariate analytical approaches to systematically examine neural representations of safety learning and recall in anxiety. Second, given the pervasive deficit in positive emotional experience during safety learning among anxious individuals, future interventions should place greater emphasis on enhancing the encoding of safety value and positive emotional experience. Third, at the level of clinical translation, the proposed framework underscores the dynamic, multi-stage nature of safety learning, suggesting that intervention strategies should be informed by underlying neural mechanisms and developmental stages, and implemented in a staged and targeted manner to achieve more precise and effective treatment outcomes.
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    The animacy detectors: The embeddedness of animacy perception
    ZHAO Peiqiong, DONG Yanxin, CHEN Guanchu, WEN Junsheng, YIN Jun, CHEN Wei, DONG Da
    2026, 34 (9):  1663-1683.  doi: 10.3724/SP.J.1042.2026.1663
    Abstract ( 10 )   PDF (2144KB) ( 8 )   Peer Review Comments
    Whether animacy perception is embedded as an innate cognitive mechanism shaped by evolutionary adaptation is a central question in contemporary research on biological motion. The influential life detector hypothesis (LDH) has considerably advanced our understanding of how non-configural dynamic cues, such as gravity-congruent foot movements, trigger the perception of animacy. Nevertheless, this hypothesis is constrained by two substantive theoretical limitations. First, the term "life" carries an unfortunate biological connotation; the cognitive system is in fact attuned to the perceptual cues of "animacy" rather than to life as an objective biological property. Second, and more critically, LDH focuses exclusively on dynamic cues and thus fails to account for the robust animacy perception elicited by static cues, including visual features such as faces, postures, and gestures, as well as non-visual static signals like odors, tastes, and pheromones. To address these limitations, this article extends the LDH framework by proposing and substantiating the animacy detectors hypothesis (ADH).
    We argue that cognitive agents possess an embedded core cognitive module—a sensory filter highly sensitive to the intrinsic features of living organisms. This "animacy detector" participates in the primary processing of all animacy-related information, responding not only to non-configural, local dynamic cues (e.g., coherent foot movements aligned with gravity) but also to static cues and other life-like stimuli. Operating exclusively at the most fundamental level of the animacy-processing hierarchy, it performs the core functions of detection, attentional orienting, and screening, thereby furnishing the most basic and primordial cognitive foundation for higher-level social cognition and its development. As a cross-species functional module embedded in the brain, the animacy detector is characterized by innateness and spontaneous self-activation, while also being amenable to further development, specialization, and recombination with other functional modules through postnatal learning.
    The central theoretical contribution of this article resides in articulating ADH as a distinct cognitive architecture rather than merely describing a hardwired mechanism. Drawing upon the “core knowledge” framework, we define the animacy detector in terms of its embeddedness, which encompasses four interrelated properties: (1) Innateness and early ontogenetic emergence, the mechanism is pre-existing and can be spontaneously activated in neonates and visually naïve animals without prior learning; (2) Domain specificity, it functions as a dedicated input analyzer for animacy cues, exhibiting a degree of informational encapsulation during the initial detection phase; (3) Functional embeddedness, the module does not operate in isolation but instead acts as a front-end, rapid sensory filter that detects, selects, and directs attention toward potential animate targets, thereby supplying the necessary input for downstream specialized systems such as face perception and biological motion processing; (4) Evolutionary conservation, its widespread presence across diverse species implies a highly conserved underlying neural substrate.
    Evidence from ontogeny, comparative psychology, and evolutionary biology provides robust empirical support for the animacy detector as a cross-species embedded cognitive module. Human neonates and visually inexperienced animals display spontaneous preferences for faces, self-propelled motion, and biological motion patterns early in life. These parallel cross-species findings, together with the involvement of homologous neural structures, collectively attest to the innate, early-emerging, evolutionarily ancient, and domain-general nature of this mechanism. Concurrently, research on subject-related and environmental factors further corroborates the functional embeddedness of the animacy detector. Variables such as experience, age-related maturation, level of awareness, cross-modal interactions, social engagement, and gravitational shaping all exert modulatory effects on animacy detection capabilities, indicating that this module is deeply integrated into the social cognitive system during ontogeny and exhibits both plasticity and adaptive fine-tuning.
    Notwithstanding the multiple lines of evidence supporting the plausibility of an embedded ADH, challenges to the hypothesis remain, particularly with respect to the non-replicability of animacy preference findings. At the same time, this framework delineates several critical directions for future inquiry. It is imperative to disentangle the respective contributions of genetic inheritance and environmental sculpting to the acquisition of the animacy detection mechanism. Moreover, the neural and behavioral integration of multisensory animacy cues—encompassing visual, auditory, and olfactory modalities—constitutes a frontier that warrants further exploration. Finally, elucidating the principles and cognitive-neural underpinnings of the animacy detector holds considerable translational promise for the early diagnosis and intervention of certain neuropsychiatric conditions. Given that impaired social cognition is a core feature of neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), future research could leverage animacy detection techniques (e.g., the presentation of faces or biological motion stimuli) to facilitate early identification of these conditions, thereby opening new avenues for timely psychological and social intervention.
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    Optimizing the construction path of psychological crisis intervention systems for public emergencies
    FAN Yunge, FAN Fang, MA Hong, MO Lei
    2026, 34 (9):  1684-1694.  doi: 10.3724/SP.J.1042.2026.1684
    Abstract ( 13 )   PDF (2284KB) ( 22 )   Peer Review Comments
    This paper addresses persistent structural deficiencies in China's psychological crisis intervention system for public emergencies, particularly fragmented inter-departmental coordination, inadequate risk identification, weak plan-execution linkages, and the absence of long-term evaluation, and proposes an integrated theoretical and operational framework for system optimization in the digital-intelligent era. Beyond synthesizing existing policy experiences, the paper makes three principal innovative contributions.
    First, from a research perspective, the paper systematically introduces the Disaster Management Cycle (DMC) theory, comprising the four interconnected phases of prevention, preparedness, response, and recovery, into the analysis of China's psychological crisis intervention system, and adapts the framework to Chinese institutional realities. Rather than treating crisis intervention as a series of event-driven actions, the framework conceptualizes it as a continuous, self-iterating cycle in which experiences and evaluation outcomes from each phase feed forward into the next, enabling sustained system improvement. This shifts the analytical lens from isolated process optimization toward a holistic structural understanding of crisis governance.
    Second, in terms of theoretical mechanism, the paper proposes that the effective functioning of the system depends on three structural capacities, that is, institutionalization, cross-sectoral coordination, and digital-intelligent empowerment, and systematically maps them onto the four DMC phases, producing a 3 × 4 analytical matrix that specifies their differentiated roles. In the prevention phase, institutionalization clarifies risk-monitoring responsibilities and data governance standards; coordination links education, medical, and community systems; and digital-intelligent technologies enable multi-source data integration and AI-driven risk surveillance. In the preparedness phase, institutionalization mandates psychological intervention as a routinized component of emergency management; coordination builds multi-stakeholder service networks; and digital platforms convert paper-based plans into executable workflows with intelligent resource allocation. The response phase requires unified command structures, professional accreditation standards, and intelligent assessment-and-dispatch systems supporting tiered triage. The recovery phase emphasizes sustained funding mechanisms, integrated long-term support networks, and digital databases enabling continuous outcome evaluation.
    Building on this matrix, the paper introduces a “technology-institution co-evolutionary mechanism” as its central theoretical contribution. Departing from prevailing views that treat digital-intelligent technology as a discrete efficiency tool, the paper argues that digital-intelligent capacity functions effectively only when embedded within institutional norms and coordination structures, while simultaneously transforming those very arrangements. Data accumulated and evidence-based evaluations generated in the recovery phase feed back through institutional channels into the prevention and preparedness phases, completing a “data collection-effectiveness assessment-institutional optimization” loop. This reframes digital-intelligent technology from an efficiency aid into a structural driver capable of dissolving administrative silos and reconfiguring interdepartmental collaboration.
    Third, in model construction, the paper proposes an “optimization pathway model for the digital-intelligent psychological crisis intervention system”, which integrates institutional rules, organizational coordination, and technological capacity into a dynamic four-phase loop. The model offers both an analytical framework for understanding long-term system operation and actionable guidance for empirical research and policy practice.
    The paper makes four additional substantive contributions. (1) It diagnoses stage-specific bottlenecks in China's current system: cross-departmental data fragmentation in prevention, plan-execution gaps in preparedness, multi-headed management in response, and resource-trauma temporal mismatches in recovery. (2) It explicitly addresses governance risks accompanying digital-intelligent transformation, privacy and data security, algorithmic misjudgment and accountability allocation, the digital divide, and online intervention ethics, arguing that such risks should be institutionally absorbed through stage-differentiated regulatory rules rather than left to technical solutions alone. (3) It emphasizes cultural adaptability, contending that algorithmic design must incorporate China's collectivist orientation, family-centered structure, and indigenous help-seeking patterns to achieve genuine usability and cultural sensitivity. (4) It proposes a multi-stakeholder governance structure incorporating individuals, families, communities, and social organizations across all four phases, supplementing the top-down government-and-professional model and fostering a more resilient social-psychological support community.
    By systematically integrating institutional rules, organizational coordination, and technological capacity within a dynamic four-phase loop, this paper advances both theoretical understanding and practical guidance for psychological crisis governance. It provides a coherent reference for transforming China's psychological crisis intervention system from event-driven to normalized operation, from short-term intervention to long-term support, and from experience-based to data-driven decision-making. The paper contributes a Chinese perspective to international research on disaster mental health systems and offers a transferable analytical framework for other countries navigating similar challenges in digital-era public mental health governance.
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    The dynamic process of individual career adaptation: The career construction model of adaptation
    YUWEN Ziqi, XIAO Yubei, YAO Xiang
    2026, 34 (9):  1695-1708.  doi: 10.3724/SP.J.1042.2026.1695
    Abstract ( 16 )   PDF (602KB) ( 27 )   Peer Review Comments
    The Career Construction Model of Adaptation (CCMA) conceptualizes career adaptation as a dynamic process through which individuals respond to career tasks, role transitions, and contextual uncertainty. It specifies four sequential stages: adaptive readiness, adaptability resources, adapting responses, and adaptation results. Although CCMA has been widely used in empirical studies, existing evidence has rarely been reviewed from the perspective of its complete process mechanism. This review synthesized 101 empirical studies published since 2012 to examine empirical tests of CCMA, clarify variable positioning within its four-stage structure, and identify directions for further model development.
    This review contributes to CCMA research in four respects. First, it shifts the review focus from career adaptability as an isolated construct to CCMA as a four-stage process model. Previous reviews and meta-analyses have mainly summarized the antecedents, correlates, and outcomes of career adaptability. In contrast, this review treats career adaptation as a process in which different stages serve distinct theoretical functions. Based on database searches and backward citation tracking, we included empirical studies that explicitly used CCMA or tested stage pathways implied by the model. Each study was coded for variables, model paths, research design, sample characteristics, and career developmental stage.
    Second, the review clarifies variable positioning within the four stages. Existing empirical studies differ considerably in assigning variables to CCMA stages. One inconsistency concerns the treatment of external contextual factors, such as parental support, educational environment, and parent-child relationship quality. These variables have sometimes been treated as adaptive readiness, although they more directly reflect contextual conditions than internal preparedness to adapt. Another inconsistency concerns the distinction between adapting responses and adaptation results. Variables such as academic or work engagement have sometimes been treated as adaptive behaviors and sometimes as outcomes, blurring the boundary between adaptive action and adaptation states. To improve comparability, this review classifies variables according to their theoretical functions: readiness as internal preparedness, resources as career adaptability, responses as adaptive beliefs, behaviors, or barriers, and results as relatively final states of person-environment fit or career functioning.
    Third, this review evaluates empirical support for CCMA by linking process pathways with career developmental stages. Full four-stage tests are mainly concentrated in the exploration stage and the early establishment stage. In early exploration, studies usually involve students in educational contexts and use CCMA to explain general adaptation outcomes, such as well-being, life satisfaction, school satisfaction, and meaning in life. In late exploration, studies are more closely related to the school-to-work transition and focus more on career-specific outcomes, such as career decidedness, career certainty, employability, career commitment, and pre-entry adaptation. In early establishment, CCMA has been used to explain work-role adaptation through job-related responses and outcomes such as job embeddedness, job crafting, work engagement, performance, perceived career success, and career sustainability. However, evidence from the growth, maintenance, and disengagement stages remains limited. Existing studies also rarely specify whether variables correspond to the core developmental tasks of each stage. Future CCMA research should therefore select variables and construct pathways according to the specific tasks of different career stages.
    Partial pathway studies serve a different purpose. Rather than testing the complete four-stage sequence, they use selected stages of CCMA to examine specific relationships among variables. For example, readiness-resources-responses studies examine whether psychological traits and beliefs foster career adaptability, which then predicts planning, exploration, self-efficacy, and career construction behaviors. Resources-responses-results and resources-results studies explain how career adaptability is linked to career or life outcomes, either through adaptive responses such as work engagement and job crafting or through more direct associations. These studies enrich understanding of specific mechanisms within CCMA, but they should be interpreted as evidence for partial mechanisms rather than direct tests of the full dynamic process.
    Fourth, this review identifies feedback mechanisms and cyclical pathways as a key direction for future research. Most existing studies adopt a sequential logic in which readiness predicts resources, resources predict responses, and responses predict results. However, career construction theory also implies repeated adjustment between individuals and environments. Adaptation results may reshape later readiness and resources, whereas adapting responses may strengthen or deplete adaptability resources. Future studies should examine these reciprocal processes through multiwave longitudinal designs, transition-based tracking, intervention follow-ups, and qualitative longitudinal methods.
    Overall, this review shows that CCMA has received broad empirical attention, but its development as a dynamic process model remains incomplete. Future research should justify the stage assignment of variables, select variables according to the core tasks of specific career developmental stages, and examine feedback mechanisms within and across adaptation cycles. CCMA should be understood not only as a sequential model of career adaptation, but also as a framework for explaining the continuous construction and reconstruction of careers over time.
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