Hasil untuk "Psychology"

Satoshi Usami

The cross-lagged panel model (CLPM) has been widely used, particularly in psychology, to infer longitudinal relations among variables. At the same time, controlling for between-person heterogeneity and capturing within-person relations as processes of within-person change are regarded as key components to causal inference based on longitudinal data. Since Hamaker, Kuiper, and Grasman (2015) criticized the CLPM for its limitations in inferring within-person relations, the random intercept cross-lagged panel model (RI-CLPM), which incorporates stable trait factors representing stable individual differences, has rapidly spread, especially in psychology. At the same time, although many statistical models are available for inferring within-person relations, the distinctions among them have not been clearly delineated, and discussions over the interpretation and selection of statistical models remain active. In this paper, I position the RI-CLPM as one useful method for inferring within-person relations, explain its practical issues, and organize its mathematical and conceptual relationships with other statistical models, as well as potential problems that may arise in their application. In particular, I point out that a distinctive feature of the stable trait factors in the RI-CLPM, in representing between-person heterogeneity, is the assumption that they are uncorrelated with within-person variability, and that this point serves as an important link to the mathematical relationship with the dynamic panel model, another promising alternative.

Keane Ong, Wei Dai, Carol Li et al.

Using intelligent systems to perceive psychological and social behaviors, that is, the underlying affective, cognitive, and pathological states that are manifested through observable behaviors and social interactions, remains a challenge due to their complex, multifaceted, and personalized nature. Existing work tackling these dimensions through specialized datasets and single-task systems often miss opportunities for scalability, cross-task transfer, and broader generalization. To address this gap, we curate Human Behavior Atlas, a unified benchmark of diverse behavioral tasks designed to support the development of foundation models for understanding psychological and social behaviors. Human Behavior Atlas comprises over 100,000 samples spanning text, audio, and visual modalities, covering tasks on affective states, cognitive states, pathologies, and social processes. Our unification efforts can reduce redundancy and cost, enable training to scale efficiently across tasks, and enhance generalization of behavioral features across domains. On Human Behavior Atlas, we train three models: Omnisapiens-7B SFT, Omnisapiens-7B BAM, and Omnisapiens-7B RL. We show that training on Human Behavior Atlas enables models to consistently outperform existing multimodal LLMs across diverse behavioral tasks. Pretraining on Human Behavior Atlas also improves transfer to novel behavioral datasets; with the targeted use of behavioral descriptors yielding meaningful performance gains. The benchmark, models, and codes can be found at: https://github.com/MIT-MI/human_behavior_atlas.

Jingting Li, Yu Qian, Lin Zhao et al.

Micro-expressions (MEs) are brief, low-intensity, often localized facial expressions. They could reveal genuine emotions individuals may attempt to conceal, valuable in contexts like criminal interrogation and psychological counseling. However, ME recognition (MER) faces challenges, such as small sample sizes and subtle features, which hinder efficient modeling. Additionally, real-world applications encounter ME data privacy issues, leaving the task of enhancing recognition across settings under privacy constraints largely unexplored. To address these issues, we propose a FED-PsyAU research framework. We begin with a psychological study on the coordination of upper and lower facial action units (AUs) to provide structured prior knowledge of facial muscle dynamics. We then develop a DPK-GAT network that combines these psychological priors with statistical AU patterns, enabling hierarchical learning of facial motion features from regional to global levels, effectively enhancing MER performance. Additionally, our federated learning framework advances MER capabilities across multiple clients without data sharing, preserving privacy and alleviating the limited-sample issue for each client. Extensive experiments on commonly-used ME databases demonstrate the effectiveness of our approach.

Seyed Moein Ayyoubzadeh, Kourosh Shahnazari, Mohammadamin Fazli et al.

Exploration of the impact of personality traits on social interactions within anonymous online communities poses a challenge at the interface of networked social sciences and psychology. We analyze whether Myers-Briggs Type Indicator (MBTI) personality types impact the dynamics of interactions on an anonymous chat system with over 288,000 messages from 6,076 users. Using a data set including 940 users voluntarily providing MBTI typing and gender, we create a weighted undirected network and apply network-science measures-such as assortativity, centrality measures, and community detection with the Louvain algorithm-to estimate the level of personality-based homophily and heterophily. Contrary to previous observations in structured social settings, our research shows a dominance of heterophilous interactions (89.3%), particularly among cognitively complementary types, i.e., NT (Intuitive-Thinking) and NF (Intuitive-Feeling). However, there is a moderate level of personality-based homophily (10.7%), notably among introverted intuitive personalities (e.g., INTJ, INFP, INFJ), reflecting an underlying cognitive alignment that persists regardless of identity markers. The interaction network exhibits scale-free properties with a power-law exponent of 1.45. In contrast, gender is a stronger homophily attribute, as evidenced by stronger levels of female users' group interactions compared with male users. While MBTI type influences minor interaction preferences, community structure exhibits low modularity (Q = 0.2584). The findings indicate that, in the absence of identity cues, psychological traits subtly shape online behavior, blending exploratory heterophily with subtle homophilic inclinations.

Mengzhu Liu, Zhengqiu Zhu, Chuan Ai et al.

During sudden disaster events, accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained open panic emotion dataset (namely COPE) via human-large language models (LLMs) collaboration to mitigate semantic bias. Then, we develop a framework integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 12.6% to 21.7% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque "data-driven fitting" to transparent "role-based simulation with mechanistic interpretation" for panic emotion prediction during emergencies. Our implementation is publicly available at: https://anonymous.4open.science/r/PsychoAgent-19DD.

Yougen Zhou, Ningning Zhou, Qin Chen et al.

Psychotherapy reaches only a small fraction of individuals suffering from mental disorders due to social stigma and the limited availability of therapists. Large language models (LLMs), when equipped with professional psychotherapeutic skills, offer a promising solution to expand access to mental health services. However, the lack of psychological conversation datasets presents significant challenges in developing effective psychotherapy-guided conversational agents. In this paper, we construct a long-periodic dialogue corpus for counseling based on cognitive behavioral therapy (CBT). Our curated dataset includes multiple sessions for each counseling and incorporates cognitive conceptualization diagrams (CCDs) to guide client simulation across diverse scenarios. To evaluate the utility of our dataset, we train an in-depth counseling model and present a comprehensive evaluation framework to benchmark it against established psychological criteria for CBT-based counseling. Results demonstrate that DiaCBT effectively enhances LLMs' ability to emulate psychologists with CBT expertise, underscoring its potential for training more professional counseling agents.

Margaret McConnell, Alya Alsager, Plyce Fuchu et al.

Abstract Background Preterm birth is a leading cause of childhood mortality and developmental disabilities, with persistent socioeconomic disparities in incidence and outcomes. Maternal presence during prolonged neonatal intensive care unit (NICU) hospitalization is critical for preterm infant health, enabling mothers to provide breast milk, directly breastfeed, and engage in skin-to-skin care—all of which promote infant physiological stability and neurodevelopment. Low-income mothers face significant barriers to visiting the NICU and participating in caregiving due to financial burdens and the psychological impact of financial stress. This randomized controlled trial aims to evaluate the effectiveness of financial transfers in promoting maternal caregiving behaviors that directly impact preterm infant health outcomes during NICU hospitalization. Methods We will conduct a two-arm, single-blinded randomized controlled trial with 420 Medicaid-eligible mothers of infants born between 24 weeks 0 days to 34 weeks 1 day gestation across four Level 3 NICUs in Georgia and Massachusetts. Mothers in the intervention arm will receive standard of care enhanced with weekly financial transfers and will be informed that these funds are intended to help them spend more time with their infants in the NICU. All participants will be provided with a hospital-grade breast pump and educational materials on the benefits of breast milk and skin-to-skin care. Participants will complete surveys during their infant’s hospitalization and following discharge, capturing outcomes related to maternal mental and physical health, caregiving behaviors, cognitive function, financial and socioeconomic factors, infant health and growth, and perceptions of NICU care quality. Primary outcomes are the provision of breast milk and engagement in skin-to-skin care. Secondary outcomes include infant growth and health outcomes, NICU visitation, financial and socioeconomic hardship, maternal physical and mental health measures, cognitive function, and perception of NICU care quality. Discussion This study will provide evidence of the impact of financial transfers on maternal caregiving behaviors in the NICU, addressing critical gaps in our understanding of how financial stress affects low-income mothers. Findings may inform health policy, particularly regarding Medicaid coverage of non-medical services, and contribute to understanding how to address disparities in preterm infant care. Trial registration The trial was prospectively registered with the American Economic Association Trial Registry, the primary registry for academic economists conducting policy trials, on 16 April 2024 (AEARCTR-0013256). It was also registered on ClinicalTrials.gov (NCT06362798) on 10 April 2024.

Tânia Caetano, Maria Salomé Pinho, Hugo Freire et al.

Abstract Cognitive deficits have been shown to increase the likelihood of relapse in individuals with substance use disorders (SUD). As such, cognitive training programs are important interventions for this population. In this study, we describe the development and test the usability of a virtual reality (VR)-based cognitive training program for individuals with SUD – VRainSUD. A total of 17 patients receiving inpatient treatment for SUD at an Addiction Treatment Center agreed to participate in the study. Participants completed 9 tasks designed to test the platform’s usability. The key performance indicators (e.g., time to complete the task) as well as any relevant observations were recorded. Finally, each participant completed a brief survey and the Post-Study System Usability Questionnaire (PSSUQ). VRainSUD was considered easy and pleasant to use but additional instructions were required on certain cognitive training tasks. The total PSSUQ score confirmed an overall high level of satisfaction concerning the platform’s usability (2.72 ± 1.92). Among the three subscales, system usefulness presented the most satisfactory score (1.76 ± 1.37) and information quality presented the least satisfactory score (3.00 ± 1.95). Changes were made to the platform to improve the on-screen information and instructions. Overall, participants showed interest in integrating VRainSUD into their standard treatment. Despite limited prior VR experience, they quickly adapted to the controllers and navigation. VRainSUD can be a potentially successful add-on to SUD treatment.

Nikolay B Petrov, Gregory Serapio-García, Jason Rentfrow

The humanlike responses of large language models (LLMs) have prompted social scientists to investigate whether LLMs can be used to simulate human participants in experiments, opinion polls and surveys. Of central interest in this line of research has been mapping out the psychological profiles of LLMs by prompting them to respond to standardized questionnaires. The conflicting findings of this research are unsurprising given that mapping out underlying, or latent, traits from LLMs' text responses to questionnaires is no easy task. To address this, we use psychometrics, the science of psychological measurement. In this study, we prompt OpenAI's flagship models, GPT-3.5 and GPT-4, to assume different personas and respond to a range of standardized measures of personality constructs. We used two kinds of persona descriptions: either generic (four or five random person descriptions) or specific (mostly demographics of actual humans from a large-scale human dataset). We found that the responses from GPT-4, but not GPT-3.5, using generic persona descriptions show promising, albeit not perfect, psychometric properties, similar to human norms, but the data from both LLMs when using specific demographic profiles, show poor psychometrics properties. We conclude that, currently, when LLMs are asked to simulate silicon personas, their responses are poor signals of potentially underlying latent traits. Thus, our work casts doubt on LLMs' ability to simulate individual-level human behaviour across multiple-choice question answering tasks.

Anthonette Adanyin

The rapid spread of digital technologies has produced data-driven feedback loops, wearable devices, social media networks, and mobile applications that shape user behavior, motivation, and mental well-being. While these systems encourage self-improvement and the development of healthier habits through real-time feedback, they also create psychological risks such as technostress, addiction, and loss of autonomy. The present study also aims to investigate the positive and negative psychological consequences of feedback mechanisms on users' behaviour and well-being. Employing a descriptive survey method, the study collected data from 200 purposely selected users to assess changes in behaviour, motivation, and mental well-being related to health, social, and lifestyle applications. Results indicate that while feedback mechanisms facilitate goal attainment and social interconnection through streaks and badges, among other components, they also enhance anxiety, mental weariness, and loss of productivity due to actions that are considered feedback-seeking. Furthermore, test subjects reported that their actions are unconsciously shaped by app feedback, often at the expense of personal autonomy, while real-time feedback minimally influences professional or social interactions. The study shows that data-driven feedback loops deliver not only motivational benefits but also psychological challenges. To mitigate these risks, users should establish boundaries regarding their use of technology to prevent burnout and addiction, while developers need to refine feedback mechanisms to reduce cognitive load and foster more inclusive participation. Future research should focus on designing feedback mechanisms that promote well-being without compromising individual freedom or increasing social comparison.

Jacob Quibell

Cybercrime is estimated to cost the global economy almost \$10 trillion annually and with businesses and governments reporting an ever-increasing number of successful cyber-attacks there is a growing demand to rethink the strategy towards cyber security. The traditional, perimeter security approach to cyber defence has so far proved inadequate to combat the growing threat of cybercrime. Cyber deception offers a promising alternative by creating a dynamic defence environment. Deceptive techniques aim to mislead attackers, diverting them from critical assets whilst simultaneously gathering cyber threat intelligence on the threat actor. This article presents a proof-of-concept (POC) cyber deception system that has been developed to capture the profile of an attacker in-situ, during a simulated cyber-attack in real time. By dynamically and autonomously generating deception material based on the observed attacker behaviour and analysing how the attacker interacts with the deception material, the system outputs a prediction on the attacker's motive. The article also explores how this POC can be expanded to infer other features of the attacker's profile such as psychological characteristics. By dynamically and autonomously generating deception material based on observed attacker behaviour and analysing how the attacker interacts with the deception material, the system outputs a prediciton on the attacker's motive. The article also explores how this POC can be expanded to infer other features of the attacker's profile such as psychological characteristics.

Andrei Khrennikov, Masanao Ozawa, Felix Benninger et al.

The past few years have seen a surge in the application of quantum theory methodologies and quantum-like modeling in fields such as cognition, psychology, and decision-making. Despite the success of this approach in explaining various psychological phenomena such as order, conjunction, disjunction, and response replicability effects there remains a potential dissatisfaction due to its lack of clear connection to neurophysiological processes in the brain. Currently, it remains a phenomenological approach. In this paper, we develop a quantum-like representation of networks of communicating neurons. This representation is not based on standard quantum theory but on generalized probability theory (GPT), with a focus on the operational measurement framework. Specifically, we use a version of GPT that relies on ordered linear state spaces rather than the traditional complex Hilbert spaces. A network of communicating neurons is modeled as a weighted directed graph, which is encoded by its weight matrix. The state space of these weight matrices is embedded within the GPT framework, incorporating effect observables and state updates within the theory of measurement instruments a critical aspect of this model. This GPT based approach successfully reproduces key quantum-like effects, such as order, non-repeatability, and disjunction effects (commonly associated with decision interference). Moreover, this framework supports quantum-like modeling in medical diagnostics for neurological conditions such as depression and epilepsy. While this paper focuses primarily on cognition and neuronal networks, the proposed formalism and methodology can be directly applied to a wide range of biological and social networks.

Martín Chicolino

El presente estudio monográfico (que se divide en dos artículos que serán publicados consecutivamente en esta revista) está dedicado a estudiar la manera en que Gilles Deleuze abordó el problema psico-político de la dominación y de la violencia psico-sexo-política masculina. Para ello, será necesario partir de la pregunta que interroga acerca de la relación genética que el Patriarcado (en tanto que mega-red de relaciones de alianzas masculinas de sexo-poder) guarda con el Estado (en tanto que forma de organizar la sociabilidad humana) y con el Capitalismo (en tanto que modo de organizar la productividad humana). Con respecto a dicha relación genética, Deleuze postulará, siguiendo al sinólogo marxista Ferenc Tökei, que ‘esclavo liberto’ (antepasado del proletario moderno) fue la personificación social masculina clave en la génesis patriarcal del Capitalismo por el Estado, siendo la explotación psico-sexual de las mujeres (prostitución) una empresa de primer orden para la comprensión de dicha génesis. Según el Esquizoanálisis, la explotación psico-sexual y libidinal es la ratio de la explotación económica y de la dominación de clase. Por eso, no podremos abordar dicha relación genética entre Patriarcado, Estado y Capital (tocante a las violencias masculinas) sin abordar, en esta «Parte I», las críticas de Deleuze/Guattari hacia Marx/Engels: esquizo-análisis ácrata (en tanto que análisis de procesos maquínicos) versus materialismo dialéctico (en tanto que análisis de procesos dialécticos). Ahora bien, ¿acaso el materialismo dialéctico resulta operativo (en la crítica y en la clínica) a la hora de comprender y de caracterizar (y de luchar contra) las violencias psico-sexuales patriarcales masculinas, como por ejemplo, la prostitución? ¿Cómo Marx, Engels, Bebel y Riazánov caracterizaron (desde el materialismo dialéctico) tanto a la prostitución, como a la prostituta, y al prostituyente? ¿No acaban incurriendo en un reduccionismo economicista y cambista (en materia sexual) que torna invisibles las causas patriarcales de la dominación y las violencias masculinas, tornándose incluso su garante insospechado “por izquierda”? Este será el problema central de la «Parte II». Nuestro concepto social de ‘Salud Mental’ depende directamente del modo de caracterizar dichos problemas psico-sexo políticos.

Asfahani Asfahani, Zohaib Hassan Sain, Emy Yunita Rahma Pratiwi

The article "Transformation Early Childhood Education in Iraq: Challenges, Innovations, and Future Prospects" provides a comprehensive analysis of the current landscape of early childhood education (ECE) in Iraq. Through a systematic literature review methodology, the study identifies and examines the major challenges facing ECE in Iraq, including inadequate funding, shortage of qualified educators, and cultural barriers. Moreover, the research explores innovative approaches and promising initiatives aimed at revitalizing ECE in Iraq, such as community-driven projects, technology-enabled solutions, and government-led reforms. By synthesizing empirical evidence with theoretical frameworks and situating the research within the socio-political context of Iraq, the analysis offers valuable insights for informing evidence-based decision-making and practice in the field of early childhood education. The study concludes with recommendations for future research, emphasizing the need for longitudinal studies, intersectional analyses, and rigorous evaluations to drive meaningful change and foster equitable access to quality ECE for all children in Iraq.

Markanday Sharma, Prateek Yadav, Srikrishna P. Panda

Diagnostic and interventional aspects of psychiatric care can be augmented by the use of digital health technologies. Recent studies have tried to explore the use of artificial intelligence-driven technologies in screening, diagnosing, and treating psychiatric disorders. This short communication presents a current perspective on using Artificial Intelligence in psychiatry.

Maria Boulos, Michaela Hynie, Shauna Spirling et al.

This study explored changes in shared decision-making roles (day-to-day, financial, and major life decisions) and their relationships to perceived stress among 148 Syrian refugee parents after resettling in Toronto using a generalized estimated equation model. Parents were categorized as “towards shared” decision-making for 20.3%, 23.0%, and 21.6% of day-to-day, major life, and financial decisions, respectively. In families where both parents were unemployed, those who “always shared” making financial decisions had significantly lower perceived stress than those “towards shared” (p = .02). Understanding the cultural contexts of gender roles and the impact of acculturation may help promote better post-migration strategies.

Anis Ernawati

Introduction: Children with special needs are often considered to be cursed. They are frequently ostracized from society thus making them feel lonely. This paper aims to help patients, especially children with special needs, find good friends through the review of current studies. Methods: This study used a descriptive quantitative method and data that was retrieved from Child and Adolescence Psychiatric Outpatients Daycare, Dr. Soetomo General Academic Hospital, Surabaya with 80 children with ADHD and 160 children with ASD. Results: A common intervention used in Indonesia is applied behavior analysis (ABA), a method that trains children to have social skills such as how to communicate, interact, and express themselves in social settings. Besides personal intervention, the need for integrated care for children with special needs such as pharmacological therapy, speech and behavioral therapy, occupational therapy, and special education, is needed to support them in helping them make friends. Conclusion: To help children with special needs make friends, we can give support, appreciation, and motivation. However, children with special needs need different treatment from their peers, so special attention and understanding are needed so that children with special needs can socialize and make friends well. Keywords: Children, Special Needs, Friends, Mental Health, Loneliness

Astrid Carolus, Martin Koch, Samantha Straka et al.

The goal of the present paper is to develop and validate a questionnaire to assess AI literacy. In particular, the questionnaire should be deeply grounded in the existing literature on AI literacy, should be modular (i.e., including different facets that can be used independently of each other) to be flexibly applicable in professional life depending on the goals and use cases, and should meet psychological requirements and thus includes further psychological competencies in addition to the typical facets of AIL. We derived 60 items to represent different facets of AI Literacy according to Ng and colleagues conceptualisation of AI literacy and additional 12 items to represent psychological competencies such as problem solving, learning, and emotion regulation in regard to AI. For this purpose, data were collected online from 300 German-speaking adults. The items were tested for factorial structure in confirmatory factor analyses. The result is a measurement instrument that measures AI literacy with the facets Use & apply AI, Understand AI, Detect AI, and AI Ethics and the ability to Create AI as a separate construct, and AI Self-efficacy in learning and problem solving and AI Self-management. This study contributes to the research on AI literacy by providing a measurement instrument relying on profound competency models. In addition, higher-order psychological competencies are included that are particularly important in the context of pervasive change through AI systems.

Wenyan Yu, Yiping Jiang, Yanqi Wu et al.

To confront college students’ new reading patterns and the continuous decline in academic library borrowing rates, we conducted empirical research on promoting multisensory reading as a way to attract students’ attention, and to stimulate interest in, and promote the practice of, reading through a library program called “Reading Today Listening Everyday” (RTLE) on a library’s WeChat public account. The program involved 48 librarians and 105 students who were recruited into different groups to co-create, edit and release multisensory tweets every workday. Multisensory contents including text-based content, audio-based content and emotional resonance were presented to evoke readers’ visual, audio, and emotional senses to induce more reading practice. Using the Context, Input, Process and Product (CIPP) evaluation method, the multisensory presentation in RTLE program was proven to be effective in promoting library reading with a high number of tweeted page views and an increased borrowing rate for recommended books. In 2020, 269 issues accompanied by 269 audio frequencies garnered 80,268 page views, depending on the caliber of the reading promoter out of the 48 librarians and 52 student anchors behind it. The 484 RTLE-recommended books were borrowed 113 times in 2020, which was a rate 1.46 times higher than in 2019 (77 times). The analysis of the relationship between tweet views and borrowing rates for recommended books indicates that more page views indicate greater reader interest, leading to increased borrowing. From readers’ feedback and comments, the gain afforded by multisensory reading can improve higher-level reading trends such as the number of reading interests, enjoyment, engagement, etc.

Nicolas A. Nunez, Boney Joseph, Rakesh Kumar et al.

Ketamine has shown rapid antidepressant and anti-suicidal effects in treatment-resistant depression (TRD) with single and serial intravenous (IV) infusions, but the effectiveness for depressive episodes of bipolar disorder is less clear. We conducted an updated systematic review and meta-analysis to appraise the current evidence on the efficacy and tolerability of ketamine/esketamine in bipolar depression. A search was conducted to identify randomized controlled trials (RCTs) and non-randomized studies examining single or multiple infusions of ketamine or esketamine treatments. A total of 2657 articles were screened; 11 studies were included in the systematic review of which 7 studies were included in the meta-analysis (five non-randomized, N = 159; two RCTs, N = 33) with a mean age of 42.58 ± 13.1 years and 54.5% females. Pooled analysis from two RCTs showed a significant improvement in depression symptoms measured with MADRS after receiving a single infusion of ketamine (1-day WMD = −11.07; and 2 days WMD = −12.03). Non-randomized studies showed significant response (53%, <i>p</i> < 0.001) and remission rates (38%, <i>p</i> < 0.001) at the study endpoint. The response (54% vs. 55%) and remission (30% vs. 40%) rates for single versus serial ketamine infusion studies were similar. The affective switch rate in the included studies approximated 2.4%. Esketamine data for bipolar depression are limited, based on non-randomized, small sample-sized studies. Further studies with larger sample sizes are required to strengthen the evidence.