Hasil untuk "Philosophy. Psychology. Religion"

Feriel Kandil

Dans le dernier chapitre de Parcours de la reconnaissance, Ricœur propose une conception pacifiée de la reconnaissance, fondée sur la mutualité plutôt que sur la réciprocité et développée à partir d’une phénoménologie du don. Bien que dans ce chapitre Derrida ne soit pas cité, l’article montre en quoi les analyses des deux philosophes se font écho, dans un jeu croisé entre déconstruction et reconstruction du don, mené à partir de la comparaison avec l’échange marchand. À l’instar de Derrida, Ricœur fait voir l’impossible du don, avec ses trois apories. Mais, à la différence de Derrida, il dévoile, dans un mouvement complémentaire de reconstruction, les pouvoirs pratiques du don, avec la dialectique entre amour et justice constitutive de la puissance et de la fragilité du don.

Jiangnan Yang, Junjie Chen, Fei Wang et al.

Psychological counseling is a fundamentally multimodal cognitive process in which clinicians integrate verbal content with visual and vocal cues to infer clients' mental states and respond empathically. However, most existing language-model-based counseling systems operate on text alone and rely on implicit mental state inference. We introduce DELTA, a deliberative multi-agent framework that models counseling as a structured reasoning process over multimodal signals, separating evidence grounding, mental state abstraction, and response generation. DELTA further incorporates reinforcement learning guided by a distribution-level Emotion Attunement Score to encourage emotionally attuned responses. Experiments on a multimodal counseling benchmark show that DELTA improves both counseling quality and emotion attunement across models. Ablation and qualitative analyses suggest that explicit multimodal reasoning and structured mental state representations play complementary roles in supporting empathic human-AI interaction.

Chengbing Wang, Wuqiang Zheng, Yang Zhang et al.

Large Language Models (LLMs) are increasingly deployed in human-centric applications, yet they often fail to provide substantive emotional support. While Reinforcement Learning (RL) has been utilized to enhance empathy of LLMs, existing reward models typically evaluate empathy from a single perspective, overlooking the inherently bidirectional interaction nature of empathy between the supporter and seeker as defined by Empathy Cycle theory. To address this limitation, we propose Psychology-grounded Empathetic Reward Modeling (PERM). PERM operationalizes empathy evaluation through a bidirectional decomposition: 1) Supporter perspective, assessing internal resonation and communicative expression; 2) Seeker perspective, evaluating emotional reception. Additionally, it incorporates a bystander perspective to monitor overall interaction quality. Extensive experiments on a widely-used emotional intelligence benchmark and an industrial daily conversation dataset demonstrate that PERM outperforms state-of-the-art baselines by over 10\%. Furthermore, a blinded user study reveals a 70\% preference for our approach, highlighting its efficacy in generating more empathetic responses. Our code, dataset, and models are available at https://github.com/ZhengWwwq/PERM.

Gaëlle Vanhoffelen, Laura Vandenbosch, Lara Schreurs

Due to technological advancements such as generative artificial intelligence (AI) and large language models, chatbots enable increasingly human-like, real-time conversations through text (e.g., OpenAI’s ChatGPT) and voice (e.g., Amazon’s Alexa). One AI chatbot that is specifically designed to meet the social-supportive needs of youth is Snapchat’s My AI. Given its increasing popularity among adolescents, the present study investigated whether adolescents’ likelihood of using My AI, as well as their positive or negative emotional experiences from interacting with the chatbot, is related to socio-demographic factors (i.e., gender, age, and socioeconomic status (SES)). A cross-sectional study was conducted among 303 adolescents (64.1% girls, 35.9% boys, 1.0% other, 0.7% preferred not to say their gender; <i>M_age</i> = 15.89, <i>SD_age</i> = 1.69). The findings revealed that younger adolescents were more likely to use My AI and experienced more positive emotions from these interactions than older adolescents. No significant relationships were found for gender or SES. These results highlight the potential for age to play a critical role in shaping adolescents’ engagement with AI chatbots on social media and their emotional outcomes from such interactions, underscoring the need to consider developmental factors in AI design and policy.

Xueqing Deng

In real life, psychological and physiological states rarely change along a single dimension. Through self-tracking and discussions with clinicians, I have come to recognise with increasing clarity that sleep patterns, autonomic arousal, bodily sensations, and cognitive load are in constant interaction. Existing models often fail to capture this complexity. Many theoretical frameworks continue to analyse these elements in isolation, making it difficult to explain sudden changes reported by individuals,such as abrupt spikes in anxiety, sudden drops in dissociation, or even moments of heightened alertness. The mathematical modelling employed herein does not replace clinical or subjective narratives, but rather provides a structural framework for these rapid transitions and elucidates why bodily-driven and cognitively-driven changes manifest differently. The objective is to build a conceptual bridge between physiological signals and lived experience, laying the groundwork for dynamic modelling and future case analyses.

He Hu, Chiyuan Ma, Qianning Wang et al.

The shortage of mental health professionals has driven the web to become a primary avenue for accessible psychological support. While Large Language Models (LLMs) offer promise for scalable web-based counseling, existing approaches often lack emotional understanding, adaptive strategies, and long-term memory. These limitations pose risks to digital well-being, as disjointed interactions can fail to support vulnerable users effectively. To address these gaps, we introduce TheraMind, a strategic and adaptive agent designed for trustworthy online longitudinal counseling. The cornerstone of TheraMind is a novel dual-loop architecture that decouples the complex counseling process into an Intra-Session Loop for tactical dialogue management and a Cross-Session Loop for strategic therapeutic planning. The Intra-Session Loop perceives the patient's emotional state to dynamically select response strategies while leveraging cross-session memory to ensure continuity. Crucially, the Cross-Session Loop empowers the agent with long-term adaptability by evaluating the efficacy of the applied therapy after each session and adjusting the method for subsequent interactions. We validate our approach in a high-fidelity simulation environment grounded in real clinical cases. Extensive evaluations show that TheraMind outperforms other methods, especially on multi-session metrics like Coherence, Flexibility, and Therapeutic Attunement, validating the effectiveness of its dual-loop design in emulating strategic, adaptive, and longitudinal therapeutic behavior. The code is publicly available at https://github.com/Emo-gml/TheraMind.

Beatriz Santana, Lidivânio Monte, Bianca Santana de Araújo Silva et al.

Context: Psychological safety (PS) is an important factor influencing team well-being and performance, particularly in collaborative and dynamic domains such as software development. Despite its acknowledged significance, research on PS within the field of software engineering remains limited. The socio-technical complexities and fast-paced nature of software development present challenges to cultivating PS. To the best of our knowledge, no systematic secondary study has synthesized existing knowledge on PS in the context of software engineering. Objective: This study aims to systematically review and synthesize the existing body of knowledge on PS in software engineering. Specifically, it seeks to identify the potential antecedents and consequences associated with the presence or absence of PS among individuals involved in the software development process. Methods: A systematic literature review was conducted, encompassing studies retrieved from four digital libraries. The extracted data were subjected to both quantitative and qualitative analyses. Results: The findings indicate a growing academic interest in PS within software engineering, with the majority of studies grounded in Edmondson's framework. Factors antecedents of PS were identified at the individual, team, and organizational levels, including team autonomy, agile methodologies, and leadership behaviors. Conclusion: PS fosters innovation, learning, and team performance within software development. However, significant gaps persist in understanding the contextual factors influencing PS, its underlying mechanisms, and effective strategies for its enhancement. Future research should address these gaps by investigating the practical applications of PS within diverse organizational settings in the software engineering domain.

Yu-Jie Guo, Ping Xue, Ping Xue et al.

IntroductionThe research on cancer patients returning to work in China is still in its infancy, and there is no research and discussion on the adaptability to return-to-work for cancer patients. It is critical to develop the Adaptability to Return-to-Work Scale (ARTWS) for cancer patients and evaluate its psychometric properties.MethodsThe items of the initial scale were compiled based on the theoretical model and literature review results. Through two rounds of Delphi expert consultation (N = 15) and a pilot survey (N = 40), the initial scale was further checked and revised. Conduct a large sample survey (N = 376) and the construct validity and reliability of the ARTWS were assessed by confirmatory factor analysis (CFA) and exploratory factor analysis (EFA).ResultsThe final ARTWS consisted of 24 items. “Focusing on rehabilitation,” “Rebuilding Self-efficiency,” and “Adjusting plans” as common factors in determining adaptability to return to work for cancer patients, and the cumulative variance contribution rate for these three factors was 66.6%. The S-CVI of the total scale was 0.979. The Cronbach’s α coefficient was 0.937 and the 2-week test–retest reliability was 0.814.DiscussionARTWS has good correlation validity and can be used as a tool to measure the adaptability of cancer patients’ return to work. The presentation of the manuscript in Research Square (https://doi.org/10.21203/rs.3.rs-2323264/v1).

Claire S. Lee, Noelle Lim, Michael Guerzhoy

We present a novel task that can elucidate the connection between anxiety and ADHD; use Transformers to make progress toward solving a task that is not solvable by keyword-based classifiers; and discuss a method for visualization of our classifier illuminating the connection between anxiety and ADHD presentations. Up to approximately 50% of adults with ADHD may also have an anxiety disorder and approximately 30\% of adults with anxiety may also have ADHD. Patients presenting with anxiety may be treated for anxiety without ADHD ever being considered, possibly affecting treatment. We show how data that bears on ADHD that is comorbid with anxiety can be obtained from social media data, and show that Transformers can be used to detect a proxy for possible comorbid ADHD in people with anxiety symptoms. We collected data from anxiety and ADHD online forums (subreddits). We identified posters who first started posting in the Anxiety subreddit and later started posting in the ADHD subreddit as well. We use this subset of the posters as a proxy for people who presented with anxiety symptoms and then became aware that they might have ADHD. We fine-tune a Transformer architecture-based classifier to classify people who started posting in the Anxiety subreddit and then started posting in the ADHD subreddit vs. people who posted in the Anxiety subreddit without later posting in the ADHD subreddit. We show that a Transformer architecture is capable of achieving reasonable results (76% correct for RoBERTa vs. under 60% correct for the best keyword-based model, both with 50% base rate).

Ryuichi Sumida, Koji Inoue, Tatsuya Kawahara

While Retrieval-Augmented Generation (RAG) has shown promise in enhancing long-term conversations, the increasing memory load as conversations progress degrades retrieval accuracy. Drawing on psychological insights, we propose LUFY, a simple yet effective method that focuses on emotionally arousing memories and retains less than 10% of the conversation. In the user experiment, participants interacted with three types of RAG chatbots, each for 2 hours over 4 sessions, marking the most extensive assessment of a chatbot's long-term capabilities to date -- more than four times longer than any existing benchmark. The results demonstrate that prioritizing arousing memories while forgetting the majority of the conversation significantly enhances user experience. This study pushes the frontier of long-term conversations and highlights the importance of forgetting unimportant parts of conversations. Code and Dataset: https://github.com/ryuichi-sumida/LUFY, Hugginface Dataset:https://huggingface.co/datasets/RuiSumida/LUFY

Valerio Biscione, Milton L. Montero, Marin Dujmovic et al.

Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the sense they are composed of behavioural and brain responses to naturalistic images that have not been manipulated to test hypotheses regarding how DNNs or humans perceive and identify objects. Here we introduce the toolbox \textit{MindSet: Vision}, consisting of a collection of image datasets and related scripts designed to test DNNs on 30 psychological findings. In all experimental conditions, the stimuli are systematically manipulated to test specific hypotheses regarding human visual perception and object recognition. In addition to providing pre-generated datasets of images, we provide code to regenerate these datasets, offering many configurable parameters which greatly extend the dataset versatility for different research contexts, and code to facilitate the testing of DNNs on these image datasets using three different methods (similarity judgments, out-of-distribution classification, and decoder method), accessible via https://github.com/MindSetVision/MindSetVision. To illustrate the challenges these datasets pose for developing better DNN models of human vision, we test several models on range of datasets included in the toolbox.

Chuanlin Zhang, Junkang Feng, Chenggang Cui et al.

With the growing adoption of electric vehicles (EVs), understanding user charging behavior has become critical for grid stability and transportation planning. This study investigates the behavioral heterogeneity of EV taxi drivers by analyzing the interaction between psychological traits and situational triggers within dynamic travel contexts. Leveraging large language models (LLMs) as a core simulation tool, a novel framework with statistical enhancement is developed to replicate and analyze the charging behaviors of taxi drivers. LLMs simulate personalized decision-making processes by leveraging natural language reasoning and role-playing capabilities, accounting for factors such as time sensitivity, price awareness, and range anxiety. Simulation results indicate that the framework reliably reproduces real-world charging behaviors across multiple urban environments. his fidelity arises from integrating statistical priors into the reasoning process, allowing the model to anchor its decisions in empirical behavioral patterns. Further analysis highlights the joint influence of environmental and psychological variables on charging decisions and reveals the heterogeneity of different user groups. The findings provide new insights into EV user behavior, offering a foundation for optimizing charging infrastructure, informing energy policy, and advancing the integration of EV behavioral models into smart transportation and energy management systems.

Nickholas Grant, Joanna L. Meyer, Michael J. Strambler

The measurement of social and emotional learning (SEL) implementation is a critical part of enhancing and understanding the effects of SEL programming. Research has shown that high-quality SEL implementation is associated with social, emotional, and academic outcomes. Schools achieve these outcomes in part through organizational practices that emphasize ongoing communication, collaboration, coordination, shared decision making, and strategic planning, processes that are ideally informed by evidence. The application of implementation science to SEL has advanced our understanding of the role of implementation in achieving student outcomes. However, the development of practical approaches for measuring and supporting SEL implementation have lagged behind work on measuring student SEL outcomes. Research-practitioner partnerships (RPP), long-term, mutually-beneficial collaborations geared toward identifying problems of practice and testing solutions for improvement, are a promising means for addressing this important gap. Though implementation science and RPPs have complementary aims, there has been limited attention to the integration of these approaches in the context of SEL programming. The goal of this paper is to offer practical strategies for measuring and using SEL implementation data in schools, using the example of an RPP that used implementation science practices to guide SEL implementation. We give special attention to structures that can support the collection and use of implementation data to improve practice, as well as considerations around developing measures, considering trade-offs of data collection decisions, and conducting data analysis.

Advait Sarkar, Ian Drosos, Rob Deline et al.

Generative AI, such as image generation models and large language models, stands to provide tremendous value to end-user programmers in creative and knowledge workflows. Current research methods struggle to engage end-users in a realistic conversation that balances the actually existing capabilities of generative AI with the open-ended nature of user workflows and the many opportunities for the application of this technology. In this work-in-progress paper, we introduce participatory prompting, a method for eliciting opportunities for generative AI in end-user workflows. The participatory prompting method combines a contextual inquiry and a researcher-mediated interaction with a generative model, which helps study participants interact with a generative model without having to develop prompting strategies of their own. We discuss the ongoing development of a study whose aim will be to identify end-user programming opportunities for generative AI in data analysis workflows.

Karin Verelst

Isaac Newton, in popular imagination the Ur-scientist, was an outstanding humanist scholar. His researches on, among others, ancient philosophy, are thorough and appear to be connected to and fit within his larger philosophical and theological agenda. It is therefore relevant to take a closer look at Newton's intellectual choices, at how and why precisely he would occupy himself with specific text-sources, and how this interest fits into the larger picture of his scientific and intellectual endeavours. In what follows, we shall follow Newton into his study and look over his shoulder while reading compendia and original source-texts in his personal library at Cambridge, meticulously investigating and comparing fragments and commentaries, and carefully keeping track in private notes of how they support his own developing ideas. Indeed, Newton was convinced that precursors to his own insights and discoveries were present already in Antiquity, even before the Greeks, in ancient Egypt, and he puts a lot of time and effort into making the point, especially, and not incidentially, in the period between the first and the second edition of the Principia. A clear understanding of his reading of the classic sources therefore matters to our understanding of its content and gestation process. In what follows we will confine ourselves to the classical legacy, and investigate Newton's intellectual intercourse with it.

Kenichiro Ishizu, Tomu Ohtsuki, Yoshiyuki Shimoda

This study investigated whether lower psychological flexibility (psychological inflexibility) mediates the relationship between contingent self-worth and depressive symptoms among Japanese adolescents. A total of 210 Japanese junior high school students aged 12 to 15 years (106 boys and 104 girls) were recruited for this study. Participants completed the Japanese adaptations of the Self-Worth Contingency Questionnaire, the Avoidance and Fusion Questionnaire for Youth, and the Depression Self-Rating Scale for Children. Results indicated that psychological inflexibility mediated the association between contingent self-worth and depressive symptoms. Specifically, contingent self-worth affected lower psychological flexibility, which influenced higher depressive symptoms. The results highlight the importance of fostering autonomy and promoting psychological flexibility to reduce the risk of depression among adolescents.

Christian Fernando Ribeiro Guimarães Vinci

Esse ensaio buscará sondar as relações entre filosofia e literatura, no pensamento de Gilles Deleuze, a despeito de sua parceria conjunta com Félix Guattari, atentando tanto para as concepções de escrita expressas ao longo de sua obra quanto para o modo como essas concepções teriam influenciado o estilo de seus escritos filosóficos. Partindo da premissa deleuziana de que a escrita possui um acentuado lastro clínico, sendo a responsável pela elaboração de um diagnóstico das forças capazes de aprisionar ou calar a vida, procurar-se-á esmiuçar as ressonâncias desse lastro clínico, na concepção de filosofia como ato criativo, elaborada pelo autor. Como hipótese a ser aqui trabalhada, defende-se que a escrita deleuziana – compreendida como portadora de uma literalidade, conforme sustenta François Zourabchivili, ou como encrustada de uma poética imanentista, tal qual sugere Anita Costa Malufe – procuraria produzir uma zona de vizinhança ou indiscernibilidade entre a escrita filosófica, de caráter mais exegético, e a escrita literária, mais afectiva, de modo a produzir um deslocamento na relação do leitor com o ato de pensar.

Ivana Milković

U radu se analiziraju glavni i sporedni likovi romana Matilda Roalda Dahla. Razmatraju se osobine i postupci Matilde, glavnoga lika, iz perspektive zlostavljanja i zanemarivanja djece u dječjoj književnosti. Izdvajaju se primjeri različitih oblika posrednoga i neposrednoga fizičkoga i emocionalnoga zlostavljanja, te zanemarivanja navedenih u djelu. Matilda kao junakinja razvija svoje intelektualne sposobnosti do razine u kojoj oni postaju fantastičan element priče i tako rješava nepovoljnu situaciju u kojoj se nalazi. Matilda je aktivan i glavni akter svojega djetinjstva i neustrašivo preuzima odgovornost za svoje sretno djetinjstvo. Analizira se i uloga sporednih likova. Odrasli likovi su ili zlostavljači pa ih treba kazniti ili pak sami svojevoljno ostaju ravnodušnima prema zlostavljanju, pa im nije posvećena gotovo nikakva pažnja. Djecu, kao žrtve zlostavljanja, karakteriziraju dva tipa odnosa prema zlostavljačima. Jedan tip su djeca žrtve nasilja koje paralizira strah od zlostavljača. Drugi tip su djeca koja se na sve moguće načine bore protiv zlostavljača i pokušavaju ga pobijediti. U drugi tip ubrajamo i Matildu, kojoj je dana tolika intelektualna moć i duhovna snaga da ona ni u jednom trenu ne posumnja u negativnost postupaka zlostavljanja. Sigurna je u svoj put te naposljetku u potpunosti preuzima ulogu junakinje, ne samo u svom životu, već i u svojoj okolini.

Mustla Sofyan Tasfiq

Indonesia shows a high prevalence rate of marriage at young age or underage marriage. The number of child marriages in Indonesia from 2008 to 2018 has shown a decline. In 2008-2012, the percentage of child marriage under 18 years of age was still relatively high, namely 14.67%. Continued in 2013-2014, it decreased to 13%, and decreased in 2018 with an early marriage rate of 11.21%. Malang, East Java is an area that shows that the phenomenon of early age marriage or child marriage is still rife. Therefore, the author wants to examine how the practice of child marriage in Malang is seen from the perspective of phenomeological theory. Using qualitative methods, descriptive analysis, the data used will be data obtained from the national statistical agency, then from the religious court. Then analyzed using the phenomenological theory of Edmund Husserl. After postponing it to find out the essence behind the phenomenon of young marriage in Malang, we found several factors. socialization of children and lack of control from parents, low awareness of public education, and the community's economy. Indonesia menunjukkan angka prevelensi perkawinan usia muda atau perkawinan dibawah umur yang cukup tinggi. Angka pernikahan anak dibawah umur di Indonesia dari tahun 2008 hingga 2018 tercatat telah menunjukkan penurunan. Pada tahun 2008-2012, presentase perkawinan anak usia dibawah 18 tahun masih terbilang tinggi yakni 14,67%. Dilanjutkan pada tahun 2013-2014 turun menjadi 13%, dan semakin turun pada tahun 2018 dengan angka perkawinan dini sebanyak 11,21%. Malang Jawa Timur merupakan daerah yang menunjukkan bahwa fenomena perkawinan usia dini atau perkawinan anak masih marak terjadi. Oleh karena itu penulis ingin mengkaji bagaimana praktik perkawinan anak di daerah Malang dilihat dari perspektif teori fenomeologi. Menggunakan metode kualitatif, deskriptif analisis,maka nantinya data yang digunakan adalah data yang diperoleh dari badan statisti nasional, kemudian dari pengadilan agama. Lalu dianalisis menggunakan teori fenomenologi Edmund Husserl. Setelah dilakukan penundaan untuk mengetahui esensi yang melatarbelakangi fenomena perkawinan usia muda di Malang, maka kami menemukan beberapa faktor. pergaulan anak dan kurangnya kontrol dari orang tua, rendahnya kesadaran pendidikan masyarakat, dan ekonomi masyarakat.

Muhammad Sohail, Ataullah Khan Mahmood

Divine law is the basic law in the Muslim states that guides the positive law of the state. Islamic law is called the Sharī’ah; while Islamic jurisprudence is called the Fiqh. Allāh Almighty has prescribed fixed punishments for some offences. Those offences are called the Hudood offence. There is not any consensus about the exact number of Hudood offences; however the figure fluctuates from four to ten offences that fall in the category of Hudood offences. Islamic criminal jurisprudence developed in the fact that trafficking in persons is included in the category of Hudood offences. Allāh almighty has prescribed limits for every act of human being. Any person violating such limits is condemned and held as sinful which is called as offence in the positive legal system. Any such violation in more shameful manner is called Fasād fī al Arḍ. Trafficking in persons is also one of the wrongs creating Fasād fī al Arḍ.