Hasil untuk "Philosophy. Psychology. Religion"

Chongyuan Dai, Jinpeng Hu, Hongchang Shi et al.

Amidst a shortage of qualified mental health professionals, the integration of large language models (LLMs) into psychological applications offers a promising way to alleviate the growing burden of mental health disorders. Recent reasoning-augmented LLMs have achieved remarkable performance in mathematics and programming, while research in the psychological domain has predominantly emphasized emotional support and empathetic dialogue, with limited attention to reasoning mechanisms that are beneficial to generating reliable responses. Therefore, in this paper, we propose Psyche-R1, the first Chinese psychological LLM that jointly integrates empathy, psychological expertise, and reasoning, built upon a novel data curation pipeline. Specifically, we design a comprehensive data synthesis pipeline that produces over 75k high-quality psychological questions paired with detailed rationales, generated through chain-of-thought (CoT) reasoning and iterative prompt-rationale optimization, along with 73k empathetic dialogues. Subsequently, we employ a hybrid training strategy wherein challenging samples are identified through a multi-LLM cross-selection strategy for group relative policy optimization (GRPO) to improve reasoning ability, while the remaining data is used for supervised fine-tuning (SFT) to enhance empathetic response generation and psychological domain knowledge. Extensive experiment results demonstrate the effectiveness of the Psyche-R1 across several psychological benchmarks, where our 7B Psyche-R1 achieves comparable results to 671B DeepSeek-R1.

Tianjie Ju, Yujia Chen, Hao Fei et al.

Previous work has showcased the intriguing capabilities of Large Language Models (LLMs) in instruction-following and rhetorical fluency. However, systematic exploration of their dual capabilities to autonomously persuade and resist persuasion, particularly in contexts involving psychological rhetoric, remains unexplored. In this paper, we first evaluate four commonly adopted LLMs by tasking them to alternately act as persuaders and listeners in adversarial dialogues. Empirical results show that persuader LLMs predominantly employ repetitive strategies, leading to low success rates. Then we introduce eleven comprehensive psychological persuasion strategies, finding that explicitly instructing LLMs to adopt specific strategies such as Fluency Effect and Repetition Effect significantly improves persuasion success rates. However, no ``one-size-fits-all'' strategy proves universally effective, with performance heavily dependent on contextual counterfactuals. Motivated by these observations, we propose an adaptive framework based on direct preference optimization that trains LLMs to autonomously select optimal strategies by leveraging persuasion results from strategy-specific responses as preference pairs. Experiments on three open-source LLMs confirm that the proposed adaptive psychological persuasion method effectively enables persuader LLMs to select optimal strategies, significantly enhancing their success rates while maintaining general capabilities. Our code is available at https://github.com/KalinaEine/PsychologicalPersuasion.

Yichao Feng, Haoran Luo, Lang Feng et al.

Large Language Models show promise in emotion understanding, social reasoning, and empathy, yet they struggle with psychologically grounded tasks that require inferring implicit mental states in context-rich, ambiguous settings. These limitations arise from the absence of theory-aligned supervision and the difficulty of capturing nuanced mental processes in real-world narratives. To address this gap, we leverage expert-labeled, psychologically rich scenarios and propose a trajectory-aware reinforcement learning framework that explicitly imitates expert psychological thought patterns. By integrating real-world stimuli with structured reasoning guidance, our approach enables compact models to internalize social-cognitive principles, perform nuanced psychological inference, and support continual self-improvement. Comprehensive experiments across multiple benchmarks further demonstrate that our models achieve expert-level interpretive capabilities, exhibiting strong out-of-distribution generalization and robust continual learning across diverse, challenging, and psychologically grounded tasks.

Ezieddin Elmahjub

Generative AI (GenAI) outputs are not copyrightable. This article argues why. We bypass conventional doctrinal analysis that focuses on black letter law notions of originality and authorship to re-evaluate copyright's foundational philosophy. GenAI fundamentally severs the direct human creative link to expressive form. Traditional theories utilitarian incentive, labor desert and personality fail to provide coherent justification for protection. The public domain constitutes the default baseline for intellectual creations. Those seeking copyright coverage for GenAI outputs bear the burden of proof. Granting copyright to raw GenAI outputs would not only be philosophically unsound but would also trigger an unprecedented enclosure of the digital commons, creating a legal quagmire and stifling future innovation. The paper advocates for a clear distinction: human creative contributions to AI-generated works may warrant protection, but the raw algorithmic output should remain in the public domain.

Zehao Liu, Xi Lin

Large Language Models (LLMs) have gained considerable popularity and protected by increasingly sophisticated safety mechanisms. However, jailbreak attacks continue to pose a critical security threat by inducing models to generate policy-violating behaviors. Current paradigms focus on input-level anomalies, overlooking that the model's internal psychometric state can be systematically manipulated. To address this, we introduce Psychological Jailbreak, a new jailbreak attack paradigm that exposes a stateful psychological attack surface in LLMs, where attackers exploit the manipulation of a model's psychological state across interactions. Building on this insight, we propose Human-like Psychological Manipulation (HPM), a black-box jailbreak method that dynamically profiles a target model's latent psychological vulnerabilities and synthesizes tailored multi-turn attack strategies. By leveraging the model's optimization for anthropomorphic consistency, HPM creates a psychological pressure where social compliance overrides safety constraints. To systematically measure psychological safety, we construct an evaluation framework incorporating psychometric datasets and the Policy Corruption Score (PCS). Benchmarking against various models (e.g., GPT-4o, DeepSeek-V3, Gemini-2-Flash), HPM achieves a mean Attack Success Rate (ASR) of 88.1%, outperforming state-of-the-art attack baselines. Our experiments demonstrate robust penetration against advanced defenses, including adversarial prompt optimization (e.g., RPO) and cognitive interventions (e.g., Self-Reminder). Ultimately, PCS analysis confirms HPM induces safety breakdown to satisfy manipulated contexts. Our work advocates for a fundamental paradigm shift from static content filtering to psychological safety, prioritizing the development of psychological defense mechanisms against deep cognitive manipulation.

Emeralda Sesari, Federica Sarro, Ayushi Rastogi

Background: Psychological safety refers to the belief that team members can speak up or make mistakes without fear of negative consequences. While it is recognized as important in traditional software teams, its role in open-source software development remains understudied. Open-source contributors often collaborate without formal roles or structures, where interpersonal relationships can significantly influence participation. Code review, a central and collaborative activity in modern software development, offers a valuable context for observing such team interactions. Aims: This study investigates whether team-level psychological safety, inferred from code review activities, is associated with contributors' sustained participation in open-source projects. Method: Using data from 60,684 pull requests across multiple repositories, we developed a psychological safety index based on observable cues such as merge decisions, comment activity, interaction diversity, and mentions. We analyzed the relationship between this index and contributors' short-term (within 1 year) and long-term (over 4--5 years) sustained participation using three logistic regression models. Results: Contributors are more likely to remain active in repositories with higher levels of psychological safety. Psychological safety is positively associated with both short-term and long-term sustained participation. However, prior participation emerges as a stronger predictor of future engagement, reducing the effect of psychological safety when accounted for. Conclusions: This study introduces a scalable, data-driven approach to measuring psychological safety through pull request data and provides new empirical evidence of its relevance in sustaining participation within open-source development.

Jieyi Wang, Yue Huang, Zeming Liu et al.

Online psychological counseling dialogue systems are trending, offering a convenient and accessible alternative to traditional in-person therapy. However, existing psychological counseling dialogue systems mainly focus on basic empathetic dialogue or QA with minimal professional knowledge and without goal guidance. In many real-world counseling scenarios, clients often seek multi-type help, such as diagnosis, consultation, therapy, console, and common questions, but existing dialogue systems struggle to combine different dialogue types naturally. In this paper, we identify this challenge as how to construct mixed-type dialogue systems for psychological counseling that enable clients to clarify their goals before proceeding with counseling. To mitigate the challenge, we collect a mixed-type counseling dialogues corpus termed STAMPsy, covering five dialogue types, task-oriented dialogue for diagnosis, knowledge-grounded dialogue, conversational recommendation, empathetic dialogue, and question answering, over 5,000 conversations. Moreover, spatiotemporal-aware knowledge enables systems to have world awareness and has been proven to affect one's mental health. Therefore, we link dialogues in STAMPsy to spatiotemporal state and propose a spatiotemporal-aware mixed-type psychological counseling dataset. Additionally, we build baselines on STAMPsy and develop an iterative self-feedback psychological dialogue generation framework, named Self-STAMPsy. Results indicate that clarifying dialogue goals in advance and utilizing spatiotemporal states are effective.

Fabrycio Leite Nakano Almada, Kauan Divino Pouso Mariano, Maykon Adriell Dutra et al.

This paper presents "Emotion Talk," a system designed to provide continuous emotional support through audio messages for psychological assistance. The primary objective is to offer consistent support to patients outside traditional therapy sessions by analyzing audio messages to detect emotions and generate appropriate responses. The solution focuses on Portuguese-speaking users, ensuring that the system is linguistically and culturally relevant. This system aims to complement and enhance the psychological follow-up process conducted by therapists, providing immediate and accessible assistance, especially in emergency situations where rapid response is crucial. Experimental results demonstrate the effectiveness of the proposed system, highlighting its potential in applications of psychological support.

Mohit Chandra, Suchismita Naik, Denae Ford et al.

Recent gains in popularity of AI conversational agents have led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences of individuals. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-representation of the impact of psychological risks of AI use. To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through the lived experiences of individuals. We employed a mixed-method approach, involving a comprehensive survey with 283 people with lived mental health experience and workshops involving experts with lived experience to develop a psychological risk taxonomy. Our taxonomy features 19 AI behaviors, 21 negative psychological impacts, and 15 contexts related to individuals. Additionally, we propose a novel multi-path vignette-based framework for understanding the complex interplay between AI behaviors, psychological impacts, and individual user contexts. Finally, based on the feedback obtained from the workshop sessions, we present design recommendations for developing safer and more robust AI agents. Our work offers an in-depth understanding of the psychological risks associated with AI conversational agents and provides actionable recommendations for policymakers, researchers, and developers.

Eldar Knar

The chronological hierarchy and classification of psychological types of individuals are examined. The anomalous nature of psychological activity in individuals involved in scientific work is highlighted. Certain aspects of the introverted thinking type in scientific activities are analyzed. For the first time, psychological archetypes of scientists with pronounced introversion are postulated in the context of twelve hypotheses about the specifics of professional attributes of introverted scientific activities. A linear regression and Bayesian equation are proposed for quantitatively assessing the econometric degree of introversion in scientific employees, considering a wide range of characteristics inherent to introverts in scientific processing. Specifically, expressions for a comprehensive assessment of introversion in a linear model and the posterior probability of the econometric (scientometric) degree of introversion in a Bayesian model are formulated. The models are based on several econometric (scientometric) hypotheses regarding various aspects of professional activities of introverted scientists, such as a preference for solo publications, low social activity, narrow specialization, high research depth, and so forth. Empirical data and multiple linear regression methods can be used to calibrate the equations. The model can be applied to gain a deeper understanding of the psychological characteristics of scientific employees, which is particularly useful in ergonomics and the management of scientific teams and projects. The proposed method also provides scientists with pronounced introversion the opportunity to develop their careers, focusing on individual preferences and features.

Adrianna Grabizna

At each introduction of a new edition of psychiatric classifications, a vivid debate resurfaces and concerns their very validity: should classifications be based on etiology or should they be descriptive, based on observation, and not on some or other theories of etiopathogenesis? I shift the attention to the philosophical aspect of the debate. Psychiatric classifications employ (and have always employed) taxonomic methodology but in fact are not (and never were) based on biological mechanisms leading to mental disorders. Therefore I tried to catch the moment where certain observable features, recognized as symptoms, begins to be perceived as an ontologically independent entities and we start to think that nosological units must have a specific cause (e.g. a neuropathogenesis), which is simply reflected in the diagnostic picture. I tried to catch the moment, when by naming, classifying and diagnosing, we, in a sense, create objects. Then I show how from there we can slide into objectification: we can stop to see a person and start to an illness.

Daniel J. Lasker

Tianyi Miao

The precise definition of causality is currently an open problem in philosophy and statistics. We believe causality should be defined as functions (in mathematics) that map causes to effects. We propose a reductive definition of causality based on Structural Functional Model (SFM). Using delta compression and contrastive forward inference, SFM can produce causal utterances like "X causes Y" and "X is the cause of Y" that match our intuitions. We compile a dataset of causal scenarios and use SFM in all of them. SFM is compatible with but not reducible to probability theory. We also compare SFM with other theories of causation and apply SFM to downstream problems like free will, causal explanation, and mental causation.

Jacqueline Harding

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research.

Gianluca Cassese

We obtain an elementary characterization of expected utility based on a representation of choice in terms of psychological gambles, which requires no assumption other than coherence between ex-ante and ex-post preferences. Weaker version of coherence are associated with various attitudes towards complexity and lead to a characterization of minimax or Choquet expected utility.

Davood Saeedi, abdollah frozanfar

Today, due to the corrupt sequence of gender discriminatory views, their criticism has a special importance. Our hypothesis in this article is to prove the generality and prevalence of this gender view in the reasons of polygamy. It seems that the criticism and explanation of these reasons based on human nature and social interests with gender justice is more compatible. In this research, adherence to the verses related to the goals of marriage as a confirmation of the writers' point of view in the interpretation of the Qur'an, the third verse of Surah Al-Nisa is referred, for the first time. This article, which has been done in a descriptive-analytical method and by using library and internet resources, shows that the most limited and at the same time the most gendered views in the reasons of polygamy are experimental approaches, especially medical and psychological approaches.

Atoosa Kasirzadeh

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness.

Ruben S. Verhagen, Siddharth Mehrotra, Mark A. Neerincx et al.

The rapid development of Artificial Intelligence (AI) requires developers and designers of AI systems to focus on the collaboration between humans and machines. AI explanations of system behavior and reasoning are vital for effective collaboration by fostering appropriate trust, ensuring understanding, and addressing issues of fairness and bias. However, various contextual and subjective factors can influence an AI system explanation's effectiveness. This work draws inspiration from findings in cognitive psychology to understand how effective explanations can be designed. We identify four components to which explanation designers can pay special attention: perception, semantics, intent, and user & context. We illustrate the use of these four explanation components with an example of estimating food calories by combining text with visuals, probabilities with exemplars, and intent communication with both user and context in mind. We propose that the significant challenge for effective AI explanations is an additional step between explanation generation using algorithms not producing interpretable explanations and explanation communication. We believe this extra step will benefit from carefully considering the four explanation components outlined in our work, which can positively affect the explanation's effectiveness.

Ilir Kola, Catholijn M. Jonker, M. Birna van Riemsdijk

Support agents that help users in their daily lives need to take into account not only the user's characteristics, but also the social situation of the user. Existing work on including social context uses some type of situation cue as an input to information processing techniques in order to assess the expected behavior of the user. However, research shows that it is important to also determine the meaning of a situation, a step which we refer to as social situation comprehension. We propose using psychological characteristics of situations, which have been proposed in social science for ascribing meaning to situations, as the basis for social situation comprehension. Using data from user studies, we evaluate this proposal from two perspectives. First, from a technical perspective, we show that psychological characteristics of situations can be used as input to predict the priority of social situations, and that psychological characteristics of situations can be predicted from the features of a social situation. Second, we investigate the role of the comprehension step in human-machine meaning making. We show that psychological characteristics can be successfully used as a basis for explanations given to users about the decisions of an agenda management personal assistant agent.

Felipe Miranda Zanetti

Uma conceituação de saúde específica permeia as discussões clínicas em suas mais diversas facetas e na psicologia, como nas ciências humanas em geral, a saúde torna-se – segundo uma noção positivista – um ideal a ser alcançado a partir de ferramentas clínicas com intuito terapêutico. Pensar a conceituação de saúde, a implicação desse conceito na construção de cada modo-de-ser e compreender a atuação do terapeuta diante dos ideais positivistas instaurados como verdades absolutas, compõe o caminho deste trabalho, bem como destacar os limites da técnica contemporânea, que forçosamente faz com que os fenômenos se mostrem a partir de suas imposições. Compreendeu-se que, segundo o olhar fenomenológico, o adoecer se dá por meio de restrições das possibilidades para a realização de uma existência, não sendo saúde, portanto, apenas a funcionalidade biológica de um corpo, mas, muito além disso, a abertura à liberdade ontológica de cada ser.