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.

Anton Dzega, Aviad Elyashar, Ortal Slobodin et al.

Thematic Apperception Test (TAT) is a psychometrically grounded, multidimensional assessment framework that systematically differentiates between cognitive-representational and affective-relational components of personality-like functioning. This test is a projective psychological framework designed to uncover unconscious aspects of personality. This study examines whether the personality traits of Large Multimodal Models (LMMs) can be assessed through non-language-based modalities, using the Social Cognition and Object Relations Scale - Global (SCORS-G). LMMs are employed in two distinct roles: as subject models (SMs), which generate stories in response to TAT images, and as evaluator models (EMs), who assess these narratives using the SCORS-G framework. Evaluators demonstrated an excellent ability to understand and analyze TAT responses. Their interpretations are highly consistent with those of human experts. Assessment results highlight that all models understand interpersonal dynamics very well and have a good grasp of the concept of self. However, they consistently fail to perceive and regulate aggression. Performance varied systematically across model families, with larger and more recent models consistently outperforming smaller and earlier ones across SCORS-G dimensions.

Zhe Xu

Reinforcement learning algorithms often suffer from slow convergence due to sparse reward signals, particularly in complex environments where feedback is delayed or infrequent. This paper introduces the Psychological Regret Model (PRM), a novel approach that accelerates learning by incorporating regret-based feedback signals after each decision step. Rather than waiting for terminal rewards, PRM computes a regret signal based on the difference between the expected value of the optimal action and the value of the action taken in each state. This transforms sparse rewards into dense feedback signals through a step-wise scoring framework, enabling faster convergence. We demonstrate that PRM achieves stable performance approximately 36\% faster than traditional Proximal Policy Optimization (PPO) in benchmark environments such as Lunar Lander. Our results indicate that PRM is particularly effective in continuous control tasks and environments with delayed feedback, making it suitable for real-world applications such as robotics, finance, and adaptive education where rapid policy adaptation is critical. The approach formalizes human-inspired counterfactual thinking as a computable regret signal, bridging behavioral economics and reinforcement learning.

Tamunotonye Harry, Ivoline Ngong, Chima Nweke et al.

User interactions with language models vary due to static properties of the user (trait) and the specific context of the interaction (state). However, existing persona datasets (like PersonaChat, PANDORA etc.) capture only trait, and ignore the impact of state. We introduce Chameleon, a dataset of 5,001 contextual psychological profiles from 1,667 Reddit users, each measured across multiple contexts. Using the Chameleon dataset, we present three key findings. First, inspired by Latent State-Trait theory, we decompose variance and find that 74\% is within-person(state) while only 26\% is between-person (trait). Second, we find that LLMs are state-blind: they focus on trait only, and produce similar responses regardless of state. Third, we find that reward models react to user state, but inconsistently: different models favor or penalize the same users in opposite directions. We release Chameleon to support research on affective computing, personalized dialogue, and RLHF alignment.

Samuel Rhys Cox, Joel Wester, Niels van Berkel

As conversational agents become increasingly common in behaviour change interventions, understanding optimal feedback delivery mechanisms becomes increasingly important. However, choosing a style that both lessens psychological reactance (perceived threats to freedom) while simultaneously eliciting feelings of surprise and engagement represents a complex design problem. We explored how three different feedback styles: 'Direct', 'Politeness', and 'Verbal Leakage' (slips or disfluencies to reveal a desired behaviour) affect user perceptions and behavioural intentions. Matching expectations from literature, the 'Direct' chatbot led to lower behavioural intentions and higher reactance, while the 'Politeness' chatbot evoked higher behavioural intentions and lower reactance. However, 'Politeness' was also seen as unsurprising and unengaging by participants. In contrast, 'Verbal Leakage' evoked reactance, yet also elicited higher feelings of surprise, engagement, and humour. These findings highlight that effective feedback requires navigating trade-offs between user reactance and engagement, with novel approaches such as 'Verbal Leakage' offering promising alternative design opportunities.

Guifeng Deng, Shuyin Rao, Tianyu Lin et al.

Psychological support hotlines serve as critical lifelines for crisis intervention but encounter significant challenges due to rising demand and limited resources. Large language models (LLMs) offer potential support in crisis assessments, yet their effectiveness in emotionally sensitive, real-world clinical settings remains underexplored. We introduce PsyCrisisBench, a comprehensive benchmark of 540 annotated transcripts from the Hangzhou Psychological Assistance Hotline, assessing four key tasks: mood status recognition, suicidal ideation detection, suicide plan identification, and risk assessment. 64 LLMs across 15 model families (including closed-source such as GPT, Claude, Gemini and open-source such as Llama, Qwen, DeepSeek) were evaluated using zero-shot, few-shot, and fine-tuning paradigms. LLMs showed strong results in suicidal ideation detection (F1=0.880), suicide plan identification (F1=0.779), and risk assessment (F1=0.907), with notable gains from few-shot prompting and fine-tuning. Compared to trained human operators, LLMs achieved comparable or superior performance on suicide plan identification and risk assessment, while humans retained advantages on mood status recognition and suicidal ideation detection. Mood status recognition remained challenging (max F1=0.709), likely due to missing vocal cues and semantic ambiguity. Notably, a fine-tuned 1.5B-parameter model (Qwen2.5-1.5B) outperformed larger models on mood and suicidal ideation tasks. LLMs demonstrate performance broadly comparable to trained human operators in text-based crisis assessment, with complementary strengths across task types. PsyCrisisBench provides a robust, real-world evaluation framework to guide future model development and ethical deployment in clinical mental health.

Risza Subiantoro, Margarita M Maramis, Nining Febriana et al.

Introduction: Postoperative depression is a condition of depressive effects in patients without symptoms of depressive mood that occurs a few weeks after surgery and persists for at least 2 weeks. It generally possesses the same symptoms as major depressive disorder. Review: Their difference is that surgery is the trigger of depression in postoperative depression cases. Postoperative depression is associated with increased patients’ morbidity and mortality, increased the risk of disease complications, reduced postoperative healing process, prolonged the duration of treatment, and reduced patients’ quality of life. Therefore, mental health conditions should always be assessed on patients after undergoing surgery. Postoperative depression therapy needs to consider the benefits of antidepressants and adequate pain management. Antidepressant considerations also need to consider interactions with other drugs. Psychotherapy and cognitive behavioral therapy are also useful in postoperative depression management. Conclusion: This review is aimed to give insight about postoperative depression, its importance, and how to treat it.

Ingrid Zukerman

In this report, I provide a brief summary of the literature in philosophy, psychology and cognitive science about Explanatory Virtues, and link these concepts to eXplainable AI.

Guangyu Meng, Qingkai Zeng, John P. Lalor et al.

Directly learning from examples of varying difficulty levels is often challenging for both humans and machine learning models. A more effective strategy involves exposing learners to examples in a progressive order from easy to difficult. Curriculum Learning (CL) has been proposed to implement this strategy in machine learning model training. However, two key challenges persist in CL framework design: defining the difficulty of training data and determining the appropriate amount of data to input at each training step. Drawing inspiration from psychometrics, this paper presents a Psychology-based Unified Dynamic Framework for Curriculum Learning (PUDF). We quantify the difficulty of training data by applying Item Response Theory (IRT) to responses from Artificial Crowds (AC). This theory-driven IRT-AC approach leads to global (i.e., model-independent) and interpretable difficulty values. Leveraging IRT, we propose a training strategy, Dynamic Data Selection via Model Ability Estimation (DDS-MAE), to schedule the appropriate amount of data during model training. Since our difficulty labeling and model ability estimation are based on a consistent theory, namely IRT, their values are comparable within the same scope, potentially leading to aligned training data selection and faster convergence compared to the other CL methods. Experimental results demonstrate that fine-tuning pre-trained large language models with PUDF leads to higher accuracy and faster convergence on a suite of benchmark datasets compared to standard fine-tuning and state-of-the-art CL methods. Ablation studies and downstream analyses further validate the impact of PUDF for CL.

Prasoon Ambalathankandy, Yafei Ou, Sae Kaneko et al.

Grayscale images are essential in image processing and computer vision tasks. They effectively emphasize luminance and contrast, highlighting important visual features, while also being easily compatible with other algorithms. Moreover, their simplified representation makes them efficient for storage and transmission purposes. While preserving contrast is important for maintaining visual quality, other factors such as preserving information relevant to the specific application or task at hand may be more critical for achieving optimal performance. To evaluate and compare different decolorization algorithms, we designed a psychological experiment. During the experiment, participants were instructed to imagine color images in a hypothetical "colorless world" and select the grayscale image that best resembled their mental visualization. We conducted a comparison between two types of algorithms: (i) perceptual-based simple color space conversion algorithms, and (ii) spatial contrast-based algorithms, including iteration-based methods. Our experimental findings indicate that CIELAB exhibited superior performance on average, providing further evidence for the effectiveness of perception-based decolorization algorithms. On the other hand, the spatial contrast-based algorithms showed relatively poorer performance, possibly due to factors such as DC-offset and artificial contrast generation. However, these algorithms demonstrated shorter selection times. Notably, no single algorithm consistently outperformed the others across all test images. In this paper, we will delve into a comprehensive discussion on the significance of contrast and luminance in color-to-grayscale mapping based on our experimental results and analysis.

Jacob Russin, Sam Whitman McGrath, Danielle J. Williams

Compositionality has long been considered a key explanatory property underlying human intelligence: arbitrary concepts can be composed into novel complex combinations, permitting the acquisition of an open ended, potentially infinite expressive capacity from finite learning experiences. Influential arguments have held that neural networks fail to explain this aspect of behavior, leading many to dismiss them as viable models of human cognition. Over the last decade, however, modern deep neural networks (DNNs), which share the same fundamental design principles as their predecessors, have come to dominate artificial intelligence, exhibiting the most advanced cognitive behaviors ever demonstrated in machines. In particular, large language models (LLMs), DNNs trained to predict the next word on a large corpus of text, have proven capable of sophisticated behaviors such as writing syntactically complex sentences without grammatical errors, producing cogent chains of reasoning, and even writing original computer programs -- all behaviors thought to require compositional processing. In this chapter, we survey recent empirical work from machine learning for a broad audience in philosophy, cognitive science, and neuroscience, situating recent breakthroughs within the broader context of philosophical arguments about compositionality. In particular, our review emphasizes two approaches to endowing neural networks with compositional generalization capabilities: (1) architectural inductive biases, and (2) metalearning, or learning to learn. We also present findings suggesting that LLM pretraining can be understood as a kind of metalearning, and can thereby equip DNNs with compositional generalization abilities in a similar way. We conclude by discussing the implications that these findings may have for the study of compositionality in human cognition and by suggesting avenues for future research.

Mark Rubin

During multiple testing, researchers often adjust their alpha level to control the familywise error rate for a statistical inference about a joint union alternative hypothesis (e.g., "H1,1 or H1,2"). However, in some cases, they do not make this inference. Instead, they make separate inferences about each of the individual hypotheses that comprise the joint hypothesis (e.g., H1,1 and H1,2). For example, a researcher might use a Bonferroni correction to adjust their alpha level from the conventional level of 0.050 to 0.025 when testing H1,1 and H1,2, find a significant result for H1,1 (p < 0.025) and not for H1,2 (p > .0.025), and so claim support for H1,1 and not for H1,2. However, these separate individual inferences do not require an alpha adjustment. Only a statistical inference about the union alternative hypothesis "H1,1 or H1,2" requires an alpha adjustment because it is based on "at least one" significant result among the two tests, and so it refers to the familywise error rate. Hence, an inconsistent correction occurs when a researcher corrects their alpha level during multiple testing but does not make an inference about a union alternative hypothesis. In the present article, I discuss this inconsistent correction problem, including its reduction in statistical power for tests of individual hypotheses and its potential causes vis-a-vis error rate confusions and the alpha adjustment ritual. I also provide three illustrations of inconsistent corrections from recent psychology studies. I conclude that inconsistent corrections represent a symptom of statisticism, and I call for a more nuanced inference-based approach to multiple testing corrections.

Marcin Rabiza

Despite significant advancements in XAI, scholars continue to note a persistent lack of robust conceptual foundations and integration with broader discourse on scientific explanation. In response, emerging XAI research increasingly draws on explanatory strategies from various scientific disciplines and the philosophy of science to address these gaps. This paper outlines a mechanistic strategy for explaining the functional organization of deep learning systems, situating recent developments in AI explainability within a broader philosophical context. According to the mechanistic approach, explaining opaque AI systems involves identifying the mechanisms underlying decision-making processes. For deep neural networks, this means discerning functionally relevant components - such as neurons, layers, circuits, or activation patterns - and understanding their roles through decomposition, localization, and recomposition. Proof-of-principle case studies from image recognition and language modeling align this theoretical framework with recent research from OpenAI and Anthropic. The findings suggest that pursuing mechanistic explanations can uncover elements that traditional explainability techniques may overlook, ultimately contributing to more thoroughly explainable AI.

Mohsen Falah Rad, Kamrad Khoshhal Roudposhti, Mohammad Hassan Khoobkar et al.

The growth in the elderly population and the shift in the age pyramid have increased the demand for healthcare and well-being services. To address this concern, alongside the rising cost of medical care, the concept of ageing at home has emerged, driven by recent advances in medical and technological solutions. Experts in computer science, communication technology, and healthcare have collaborated to develop affordable health solutions by employing sensors in living environments, wearable devices, and smartphones, in association with advanced data mining and intelligent systems with learning capabilities, to monitor, analyze, and predict the health status of elderly individuals. However, implementing intelligent healthcare systems and developing analytical techniques requires testing and evaluating algorithms on real-world data. Despite the need, there is a shortage of publicly available datasets that meet these requirements. To address this gap, we present the DAMMI dataset in this work, designed to support researchers in the field. The dataset includes daily activity data of an elderly individual collected via home-installed sensors, smartphone data, and a wristband over 146 days. It also contains daily psychological reports provided by a team of psychologists. Furthermore, the data collection spans significant events such as the COVID-19 pandemic, New Year's holidays, and the religious month of Ramadan, offering additional opportunities for analysis. In this paper, we outline detailed information about the data collection system, the types of data recorded, and pre-processed event logs. This dataset is intended to assist professionals in IoT and data mining in evaluating and implementing their research ideas.

Péter Simonik

A dolgozat a tatai Esterházy-uradalomhoz tartozó Alsógalla, Bánhida, Felsőgalla, valamint Tatabánya községek területén élt zsidó közösség történetén keresztül kívánja megragadni a modernizáció, a polgárosodás és az asszimiláció folyamatát egy olyan térségben, amely a 19. század második felében, a szénbányászat megjelenését követően, jelentős gazdasági átalakuláson ment keresztül. A zsidóság helyi társadalomban betöltött szerepének, valamint a zsidó és nem zsidó népesség viszonyának vizsgálata révén beazonosításra kerültek azon tényezők, amelyek hozzájárultak az izraelita felekezetű népesség társadalmi beilleszkedéséhez, továbbá kijelölésre kerültek azon mérföldkövek is, amelyek az asszimiláció egyes szakaszaihoz köthetők.

Yinan Hu, Quanyan Zhu

The concept of cyber deception has been receiving emerging attention. The development of cyber defensive deception techniques requires interdisciplinary work, among which cognitive science plays an important role. In this work, we adopt a signaling game framework between a defender and a human agent to develop a cyber defensive deception protocol that takes advantage of the cognitive biases of human decision-making using quantum decision theory to combat insider attacks (IA). The defender deceives an inside human attacker by luring him to access decoy sensors via generators producing perceptions of classical signals to manipulate the human attacker's psychological state of mind. Our results reveal that even without changing the classical traffic data, strategically designed generators can result in a worse performance for defending against insider attackers in identifying decoys than the ones in the deceptive scheme without generators, which generate random information based on input signals. The proposed framework leads to fundamental theories in designing more effective signaling schemes.

Mengmeng Liu

Celebrity influence plays a significant role in fostering the consumers’ impulse buying tendency and purchase intention. In the modern advertising era, the celebrity endorsement characteristics have driven the firms’ promotion campaigns, stimulating consumer purchasing behavior through celebrity branding. The study signifies the relationship between celebrity’s traits of trustworthiness, attractiveness, credibility, and expertise influence consumers’ impulse behavior. The data was collected from the 371 customers of the fast fashion industry by using the convenient-sampling technique. SMART-PLS was used for data analysis by applying structural equation modeling. The study results show that celebrity trustworthiness, the attractiveness of a celebrity endorser, the credibility of a celebrity endorser, and celebrity expertise positively impact purchase intention and impulse buying tendency. Purchase intention plays a mediating role between independent and dependent variables.

Daniel Eckhoff, Royce Ng, Alvaro Cassinelli

In this paper we introduce novel Virtual Reality (VR) and Augmented Reality (AR) treatments to improve the psychological well being of patients in palliative care, based on interviews with a clinical psychologist who has successfully implemented VR assisted interventions on palliative care patients in the Hong Kong hospital system. Our VR and AR assisted interventions are adaptations of traditional palliative care therapies which simultaneously facilitate patients communication with family and friends while isolated in hospital due to physical weakness and COVID-19 related restrictions. The first system we propose is a networked, metaverse platform for palliative care patients to create customized virtual environments with therapists, family and friends which function as immersive and collaborative versions of 'life review' and 'reminiscence therapy'. The second proposed system will investigate the use of Mixed Reality telepresence and haptic touch in an AR environment, which will allow palliative care patients to physically feel friends and family in a virtual space, adding to the sense of presence and immersion in that environment.

Eny Purwandari, Asep Irawan

Sholat is an obligation for Muslims. Sholat obligations are determined in a day are marked by a call, the name adzan. In Indonesia, with a majority Muslim population, the signs of sholat times are very easy to recognize. But not a few ignore it. The purpose of this study is to understand sholat as a behavior from the perspective of planned behavior. This research is a qualitative descriptive study, with data collection using an open-ended questionnaire and a pocket book as a daily sholat record that informants must fill in for 2 weeks. The informants were 72 under-graduate students, both male and female. The results of this study indicate that the attitude of continuing to sholat in the given timeframe, but not necessarily at the beginning of the sholat time because there are other tasks. The presence of a family is very important as an influential person who reminds him to immediately perform sholat (as a subjective norm). Other things that are 2 considered more important and must be done besides sholat strengthen the intention to postpone praying. The behavior of postponing sholat is very strong based on these findings, because it forms attitudes, family influences and activities, which are more importantly less controllable. Therefore, a positive attitude so that prayers on time need to be built, the presence of the family is very dominant and put other things for a while to be continued after the prayer needs to be removed so that the procrastination of sholat does not occur

Lilian Blanck de Oliveira, Simone Riske-Koch

O território latino-americano é marcado por colonialidades decorrentes dos processos de colonização. Espanhóis, portugueses e europeus chegando à região de Abya Yala — agora América Latina — trouxeram em sua bagagem intentos de invadir para expropriar, buscando colonizar política, cultural e economicamente as populações originárias. Neste processo, a colonialidade impetrada impingiu na educação formal práticas reprodutoras de visões de mundo dos colonizadores, historicamente hegemônicas. Todavia, a multiplicidade de povos da Abya Yala, com suas culturas e práticas peculiares, historicamente aprendeu a sobreviver, resistir e mover-se entre lógicas e códigos coloniais. Partimos de pressupostos de que, nos processos formativos coletivos e individuais, algumas possibilidades de uma decolonialidade na educação passam pela formação docente e se efetivam a partir de uma episte(me)todologia comprometida com a diferença — as diversidades. Neste artigo objetivamos problematizar efeitos da colonização na educação brasileira, especificamente no Ensino Religioso, analisando algumas práticas e exercícios decoloniais na formação docente inicial e continuada. A investigação é de perspectiva bibliográfica e documental, utilizando, especialmente, registros do curso de licenciatura em Ciências da Religião da Universidade Regional de Blumenau, referentes ao período entre 1996 e 2020. Os resultados sinalizam a existência de possibilidades de uma formação que acolhe e reconhece outras histórias, saberes e culturas. Indicam, ainda, a viabilidade de criar espaços de abertura e lugares de diálogo, de romper com hierarquizações de saberes e poderes e do reconhecimento de diversidades epistêmicas e metodológicas — ações que interpelam e afetam o ensino da/para/com a diversidade religiosa no cotidiano da educação básica.