Background:
Alcohol use disorders (AUDs) are among the most common comorbid psychiatric disorders in patients with adult attention deficit hyperactivity disorder (ADHD), and adult ADHD is an independent risk factor for developing AUD.
Aim:
To study the prevalence of adult ADHD in patients with Alcohol dependence syndrome (ADS).
Materials and Methods:
In total, 177 cases of ADS diagnosed as per International Classification of Diseases-10, Diagnostic Criteria for Research (ICD-10, DCR) were included. The severity of alcohol dependence was estimated by the Severity of Alcohol Dependence Questionnaire (SADQ). The included patients were screened for adult ADHD by the Adult ADHD Self-Report Scale (ASRS-v1.1) symptom checklist, and statistical tests were applied.
Results:
Out of 177 patients with ADS, 21 patients screened positive for adult ADHD (11.9%). Greater severity of alcohol dependence among those screened positive for adult ADHD was noted compared to the adult ADHD negative group (P = 0.013). Adult ADHD-positive patients had an earlier age of onset of alcohol consumption (P = 0.020), higher mean duration of alcohol consumption (P < 0.001), and early onset of ADS (P = 0.038). ICD-10 criteria of loss of control, salience, and use despite harmful effects were significantly higher among the adult ADHD-positive group.
Conclusion:
The study findings suggest a significant prevalence of adult ADHD among patients with ADS.
We investigate whether Large Language Models (LLMs) exhibit human-like cognitive patterns under four established frameworks from psychology: Thematic Apperception Test (TAT), Framing Bias, Moral Foundations Theory (MFT), and Cognitive Dissonance. We evaluated several proprietary and open-source models using structured prompts and automated scoring. Our findings reveal that these models often produce coherent narratives, show susceptibility to positive framing, exhibit moral judgments aligned with Liberty/Oppression concerns, and demonstrate self-contradictions tempered by extensive rationalization. Such behaviors mirror human cognitive tendencies yet are shaped by their training data and alignment methods. We discuss the implications for AI transparency, ethical deployment, and future work that bridges cognitive psychology and AI safety
Large language models (LLMs) are rapidly being integrated into psychological research as research tools, evaluation targets, human simulators, and cognitive models. However, recent evidence reveals severe measurement unreliability: Personality assessments collapse under factor analysis, moral preferences reverse with punctuation changes, and theory-of-mind accuracy varies widely with trivial rephrasing. These "measurement phantoms"--statistical artifacts masquerading as psychological phenomena--threaten the validity of a growing body of research. Guided by the dual-validity framework that integrates psychometrics with causal inference, we present a six-stage workflow that scales validity requirements to research ambition--using LLMs to code text requires basic reliability and accuracy, while claims about psychological properties demand comprehensive construct validation. Researchers must (1) explicitly define their research goal and corresponding validity requirements, (2) develop and validate computational instruments through psychometric testing, (3) design experiments that control for computational confounds, (4) execute protocols with transparency, (5) analyze data using methods appropriate for non-independent observations, and (6) report findings within demonstrated boundaries and use results to refine theory. We illustrate the workflow through an example of model evaluation--"LLM selfhood"--showing how systematic validation can distinguish genuine computational phenomena from measurement artifacts. By establishing validated computational instruments and transparent practices, this workflow provides a path toward building a robust empirical foundation for AI psychology research.
Despite widespread debunking, many psychological myths remain deeply entrenched. This paper investigates whether Large Language Models (LLMs) mimic human behaviour of myth belief and explores methods to mitigate such tendencies. Using 50 popular psychological myths, we evaluate myth belief across multiple LLMs under different prompting strategies, including retrieval-augmented generation and swaying prompts. Results show that LLMs exhibit significantly lower myth belief rates than humans, though user prompting can influence responses. RAG proves effective in reducing myth belief and reveals latent debiasing potential within LLMs. Our findings contribute to the emerging field of Machine Psychology and highlight how cognitive science methods can inform the evaluation and development of LLM-based systems.
Psychological profiling of large language models (LLMs) using psychometric questionnaires designed for humans has become widespread. However, it remains unclear whether the resulting profiles mirror the models' psychological characteristics expressed during their real-world interactions with users. To examine the risk of human questionnaires mischaracterizing LLM psychology, we compare two types of profiles for eight open-source LLMs: self-reported Likert scores from established questionnaires (PVQ-40, PVQ-21, BFI-44, BFI-10) and generation probability scores of value- or personality-laden responses to real-world user queries. The two profiles turn out to be substantially different and provide evidence that LLMs' responses to established questionnaires reflect desired behavior rather than stable psychological constructs, which challenges the consistent psychological dispositions of LLMs claimed in prior work. Established questionnaires also risk exaggerating the demographic biases of LLMs. Our results suggest caution when interpreting psychological profiles derived from established questionnaires and point to generation-based profiling as a more reliable approach to LLM psychometrics.
With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain limited. In this paper, we introduce MME-Industry, a novel benchmark designed specifically for evaluating MLLMs in industrial settings.The benchmark encompasses 21 distinct domain, comprising 1050 question-answer pairs with 50 questions per domain. To ensure data integrity and prevent potential leakage from public datasets, all question-answer pairs were manually crafted and validated by domain experts. Besides, the benchmark's complexity is effectively enhanced by incorporating non-OCR questions that can be answered directly, along with tasks requiring specialized domain knowledge. Moreover, we provide both Chinese and English versions of the benchmark, enabling comparative analysis of MLLMs' capabilities across these languages. Our findings contribute valuable insights into MLLMs' practical industrial applications and illuminate promising directions for future model optimization research.
Arghodeep Nandi, Megha Sundriyal, Euna Mehnaz Khan
et al.
Misinformation remains one of the most significant issues in the digital age. While automated fact-checking has emerged as a viable solution, most current systems are limited to evaluating factual accuracy. However, the detrimental effect of misinformation transcends simple falsehoods; it takes advantage of how individuals perceive, interpret, and emotionally react to information. This underscores the need to move beyond factuality and adopt more human-centered detection frameworks. In this survey, we explore the evolving interplay between traditional fact-checking approaches and psychological concepts such as cognitive biases, social dynamics, and emotional responses. By analyzing state-of-the-art misinformation detection systems through the lens of human psychology and behavior, we reveal critical limitations of current methods and identify opportunities for improvement. Additionally, we outline future research directions aimed at creating more robust and adaptive frameworks, such as neuro-behavioural models that integrate technological factors with the complexities of human cognition and social influence. These approaches offer promising pathways to more effectively detect and mitigate the societal harms of misinformation.
Giovanna Takano Natti, Érica Regina Takano Natti, Paulo Laerte Natti
We present a review of the current and future industrial applications of neutrinos. We address the industrial applications of neutrinos in geological and geochemical studies of the Earth's interior, in monitoring earthquakes, in terrestrial communications, in applications for submarines, in monitoring nuclear power plants and fusion reactors, in the management of fissile materials used in nuclear plants, in tracking nuclear tests, among other applications. We also address future possibilities for industrial applications of neutrinos, especially concerning communications in the solar system and geotomography of solar system bodies.
Artificial intelligence (AI) based learning assistants and chatbots are increasingly integrated into higher education. While these tools are often evaluated in terms of technical performance, their successful and ethical use also depends on psychological factors such as trust, perceived risk, technology anxiety, and students general attitudes toward AI. This paper adopts a psychology oriented perspective to examine how university students form trust in AI based learning assistants. Drawing on recent literature in mental health, human AI interaction, and trust in automation, we propose a conceptual framework that organizes psychological predictors of trust into four groups: cognitive appraisals, affective reactions, social relational factors, and contextual moderators. A narrative review approach synthesizes empirical findings and derives research questions and hypotheses for future studies. The paper highlights that trust in AI is a psychological process shaped by individual differences and learning environments, with practical implications for instructors, administrators, and designers of educational AI systems.
In this article, I will explore how the underlying research values of ‘openness’ and ‘mutual responsiveness’, which are central to open science practices, can be integrated into a new ethos of science. Firstly, I will revisit Robert Merton's early contribution to this issue, examining whether the ethos of science should be understood as a set of norms for scientists to practice ‘good’ science or as a set of research values as a functional requirement of the scientific system to produce knowledge, irrespective of individual adherence to these norms. Secondly, I will analyse the recent codification of scientific practice in terms of ‘scientific integrity’, a framework that Merton did not pursue. Based on this analysis, and illustrated on the case of COVID-19 as a case in which the institution of science was challenged to deliver urgently on societal desirable outcomes, I will argue that promoting open science and its core norms of collaboration and openness requires broader governance of the institution of science in its relationship with society at large, rather than relying solely on self-governance within the scientific community through a new ethos of science. This conclusion has implications for re-evaluating research assessments, suggesting that the evaluation of the scientific system should take precedence over evaluating individual researchers, and that incentives should be provided to encourage specific research behaviour rather than solely focusing on individual research outputs.
The relevance of research on the conceptual foundations of personnel strategy formation in industrial enterprises under the conditions of digitalisation appears to be very relevant, which is due to the following factors. The digital transformation of industrial enterprises leads to the emergence of new technologies, processes and personnel requirements. The HR strategy must adapt to these changes in order to ensure that the company has the necessary qualifications and skills. Under the conditions of digitalisation, the demand for specialists in the field of information technologies and digital data processing is increasing. Companies need to develop strategies to attract, retain and develop such personnel in order to remain competitive. Digital technologies make it possible to automate and optimise business processes, so the HR strategy must reflect these changes and take into account the new roles and requirements for personnel. As technologies and processes change, there is a need for continuous training and development of personnel. The HR strategy should include training and development programmes aimed at increasing digital skills. Studying the conceptual foundations of personnel strategy development in the conditions of digitalisation will help companies to effectively adapt to contemporary challenges and ensure their competitiveness. The purpose of the article is to disclose the conceptual foundations of forming a personnel strategy at industrial enterprises in the context of digitalisation. The methodology of the study is an interdisciplinary approach based on the idea of effective development of an industrial enterprise as a sociotechnological system, which allowed to combine systemic knowledge that will help to understand how social factors affect the implementation of technologies and the development of the enterprise as a whole. The enterprise as a complex social and technological system should be analysed within the framework of the socio-humanistic approach, which is based on the recognition of the human being as the subject and object of all processes. In the context of a socio-humanistic approach to business analysis, it is important to address aspects related to social relations, human needs and values, and the interaction between employees and management. The fundamental principles of the socio-humanistic approach, such as taking into account the individual needs of employees, supporting their development and satisfaction from professional activities, can be applied to the analysis of an enterprise's performance. This means that the efficiency and success of an organisation is assessed not only by financial indicators, but also by the level of satisfaction and development of its employees. Studying the social interactions, communication and psychological climate of the team can help to understand what factors contribute to or hinder the effective functioning of the organisation. This approach allows to see the company as a living, organic system in which human relationships and values play an essential role. The study opens up the possibility of integrating different scientific disciplines, such as sociology, technology, economics, psychology and management, in order to understand the complex relationships in today's industrial companies. The implementation of the recommendations will help companies improve the efficiency of personnel management, provide the necessary human resources and meet the requirements of the modern market. The research generates new knowledge in the field of personnel management and digitalisation, has direct practical application for companies in increasing their competitiveness and stability in the market.
Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit profound capabilities in collective intelligence. However, the potential misuse of this intelligence for malicious purposes presents significant risks. To date, comprehensive research on the safety issues associated with multi-agent systems remains limited. In this paper, we explore these concerns through the innovative lens of agent psychology, revealing that the dark psychological states of agents constitute a significant threat to safety. To tackle these concerns, we propose a comprehensive framework (PsySafe) grounded in agent psychology, focusing on three key areas: firstly, identifying how dark personality traits in agents can lead to risky behaviors; secondly, evaluating the safety of multi-agent systems from the psychological and behavioral perspectives, and thirdly, devising effective strategies to mitigate these risks. Our experiments reveal several intriguing phenomena, such as the collective dangerous behaviors among agents, agents' self-reflection when engaging in dangerous behavior, and the correlation between agents' psychological assessments and dangerous behaviors. We anticipate that our framework and observations will provide valuable insights for further research into the safety of multi-agent systems. We will make our data and code publicly accessible at https://github.com/AI4Good24/PsySafe.
Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, with the Industrial Internet of Things (IIoT) playing a pivotal role. IIoT evolves from a simple data pipeline to an intelligent infrastructure, enabling and enhancing these advanced AI systems. This survey explores the integration of IIoT with large models (LMs) and their potential applications in industrial environments. We focus on four primary types of industrial LMs: language-based, vision-based, time-series, and multimodal models. The lifecycle of LMs is segmented into four critical phases: data foundation, model training, model connectivity, and continuous evolution. First, we analyze how IIoT provides abundant and diverse data resources, supporting the training and fine-tuning of LMs. Second, we discuss how IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments. Third, we highlight the deployment advantages of LMs within IIoT, emphasizing IIoT's role as a connectivity nexus fostering emergent intelligence through modular design, dynamic routing, and model merging to enhance system scalability and adaptability. Finally, we demonstrate how IIoT supports continual learning mechanisms, enabling LMs to adapt to dynamic industrial conditions and ensure long-term effectiveness. This paper underscores IIoT's critical role in the evolution of industrial intelligence with large models, offering a theoretical framework and actionable insights for future research.
Large language models (LLMs), such as ChatGPT, have shown remarkable abilities in natural language processing, opening new avenues in psychological research. This study explores whether LLMs can predict human memory performance in tasks involving garden-path sentences and contextual information. In the first part, we used ChatGPT to rate the relatedness and memorability of garden-path sentences preceded by either fitting or unfitting contexts. In the second part, human participants read the same sentences, rated their relatedness, and completed a surprise memory test. The results demonstrated that ChatGPT's relatedness ratings closely matched those of the human participants, and its memorability ratings effectively predicted human memory performance. Both LLM and human data revealed that higher relatedness in the unfitting context condition was associated with better memory performance, aligning with probabilistic frameworks of context-dependent learning. These findings suggest that LLMs, despite lacking human-like memory mechanisms, can model aspects of human cognition and serve as valuable tools in psychological research. We propose the field of machine psychology to explore this interplay between human cognition and artificial intelligence, offering a bidirectional approach where LLMs can both benefit from and contribute to our understanding of human cognitive processes.
Grgur Kovač, Rémy Portelas, Peter Ford Dominey
et al.
Developmental psychologists have long-established the importance of socio-cognitive abilities in human intelligence. These abilities enable us to enter, participate and benefit from human culture. AI research on social interactive agents mostly concerns the emergence of culture in a multi-agent setting (often without a strong grounding in developmental psychology). We argue that AI research should be informed by psychology and study socio-cognitive abilities enabling to enter a culture too. We discuss the theories of Michael Tomasello and Jerome Bruner to introduce some of their concepts to AI and outline key concepts and socio-cognitive abilities. We present The SocialAI school - a tool including a customizable parameterized uite of procedurally generated environments, which simplifies conducting experiments regarding those concepts. We show examples of such experiments with RL agents and Large Language Models. The main motivation of this work is to engage the AI community around the problem of social intelligence informed by developmental psychology, and to provide a tool to simplify first steps in this direction. Refer to the project website for code and additional information: https://sites.google.com/view/socialai-school.
The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes a unified industrial large knowledge model (ILKM) framework, emphasizing its potential to revolutionize future industries. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, the "6S Principle" is proposed as the guideline for ILKM development, and several potential opportunities are highlighted for ILKM deployment in Industry 4.0 and smart manufacturing.