Hasil untuk "Psychology"

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.

Kathryn King, Wladyslawa Czuber-Dochan, Trudie Chalder et al.

Inflammatory bowel disease (IBD) is treated with medications to induce and maintain remission. However, many people with IBD do not take their prescribed treatment. Identifying factors associated with IBD medication adherence is crucial for supporting effective disease management and maintaining remission. Quantitative and qualitative studies researching IBD medication adherence between 2011 and 2023 were reviewed. In total, 36,589 participants were included in 79 studies. The associated non-adherence factors were contradictory across studies, with rates notably higher (72–79%) when measured via medication refill. Non-adherence was lower in high-quality studies using self-report measures (10.7–28.7%). The frequent modifiable non-adherence risks were a poor understanding of treatment or disease, medication accessibility and an individual’s organisation and planning. Clinical variables relating to non-adherence were the treatment type, drug regime and disease activity. Depression, negative treatment beliefs/mood and anxiety increased the non-adherence likelihood. The non-modifiable factors of limited finance, younger age and female sex were also risks. Side effects were the main reason cited for IBD non-adherence in interviews. A large, contradictory set of literature exists regarding the factors underpinning IBD non-adherence, influenced by the adherence measures used. Simpler medication regimes and improved accessibility would help to improve adherence. IBD education could enhance patient knowledge and beliefs. Reminders and cues might minimise forgetting medication. Modifying risks through an adherence support intervention could improve outcomes.

Jing Jin, Yun Wang, Sixiang Liang et al.

Objective: Repetitive transcranial magnetic stimulation (rTMS) has been shown to alleviate depressive and anxiety symptoms in patients with major depressive disorder (MDD), typically by targeting the dorsolateral (DLPFC) or dorsomedial prefrontal cortex (DMPFC). Based on a pre-registered randomized controlled trial, this study presents an exploratory neuroimaging analysis investigating the impact of rTMS targeting the DLPFC versus the DMPFC on functional connectivity with the default mode network (DMN) and frontal-parietal network (FPN) in patients with MDD. Methods: Sixty-four MDD patients were randomly assigned to DLPFC-rTMS (n = 36) or DMPFC-rTMS (n = 28) groups for a 21-day intervention. Symptoms were evaluated with Hamilton Depression Rating Scale (HAMD) and Hamilton Anxiety Rating Scale (HAMA). Changes in individualized functional connectivity (inFC) between individualized targets and DMN/FPN were assessed and correlated with symptom improvements. As a control analysis, FC was evaluated based on the group-based seeds of DLPFC or DMPFC. Additionally, symptom-specific circuit map comparisons were conducted. Results: Both groups showed symptom improvements and changes in inFC with the DMN and FPN, but the specific connectivity profiles differ. In the DMN, the DLPFC-rTMS group showed decreased negative connectivity between left DLPFC and precuneus (t = -2.39, p = 0.022), while the DMPFC-rTMS group showed increased positive inFC between DMPFC and precuneus (t = -2.78, p = 0.01, FDR adjusted p = 0.034) and PCC (t = -3.15, p = 0.004, FDR adjusted p = 0.028). In the FPN, the DLPFC group showed decreased negative inFC with medial superior frontal gyrus (t = -2.35, p = 0.024) and decreased positive inFC with inferior parietal lobule (t = 2.3, p = 0.028). The DMPFC group showed increased positive connectivity with inferior frontal gyrus (t = -3.65, p = 0.001, FDR adjusted p = 0.019) and su pplementary motor area (t = -2.24, p = 0.033), and decreased negative connectivity with middle cingulate cortex (t = 2.27, p = 0.032). Canonical correlation analysis revealed a strong association between inFC changes and depression symptom improvement in the DMPFC-rTMS group (r = 0.57). Group seed-based FC changes were limited to the FPN and correlated with depressive improvement in the DLPFC-rTMS group (r = 0.52). Symptom-specific circuit maps linked to depression and anxiety were consistent across targets. Conclusion: Both DLPFC and DMPFC rTMS alleviate depressive and anxiety symptoms, displaying similar overall circuit patterns but distinct connectivity changes specific to their targets.

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.

Yu Yuan, Lili Zhao, Wei Chen et al.

Capturing human learning behavior based on deep learning methods has become a major research focus in both psychology and intelligent systems. Recent approaches rely on controlled experiments or rule-based models to explore cognitive processes. However, they struggle to capture learning dynamics, track progress over time, or provide explainability. To address these challenges, we introduce LearnerAgent, a novel multi-agent framework based on Large Language Models (LLMs) to simulate a realistic teaching environment. To explore human-like learning dynamics, we construct learners with psychologically grounded profiles-such as Deep, Surface, and Lazy-as well as a persona-free General Learner to inspect the base LLM's default behavior. Through weekly knowledge acquisition, monthly strategic choices, periodic tests, and peer interaction, we can track the dynamic learning progress of individual learners over a full-year journey. Our findings are fourfold: 1) Longitudinal analysis reveals that only Deep Learner achieves sustained cognitive growth. Our specially designed "trap questions" effectively diagnose Surface Learner's shallow knowledge. 2) The behavioral and cognitive patterns of distinct learners align closely with their psychological profiles. 3) Learners' self-concept scores evolve realistically, with the General Learner developing surprisingly high self-efficacy despite its cognitive limitations. 4) Critically, the default profile of base LLM is a "diligent but brittle Surface Learner"-an agent that mimics the behaviors of a good student but lacks true, generalizable understanding. Extensive simulation experiments demonstrate that LearnerAgent aligns well with real scenarios, yielding more insightful findings about LLMs' behavior.

Samuel J. Gershman

Where do objective functions come from? How do we select what goals to pursue? Human intelligence is adept at synthesizing new objective functions on the fly. How does this work, and can we endow artificial systems with the same ability? This paper proposes an approach to answering these questions, starting with the concept of a subjective function, a higher-order objective function that is endogenous to the agent (i.e., defined with respect to the agent's features, rather than an external task). Expected prediction error is studied as a concrete example of a subjective function. This proposal has many connections to ideas in psychology, neuroscience, and machine learning.

Carolin M. Schuster, Maria-Alexandra Dinisor, Shashwat Ghatiwala et al.

Large language models (LLMs) are the foundation of the current successes of artificial intelligence (AI), however, they are unavoidably biased. To effectively communicate the risks and encourage mitigation efforts these models need adequate and intuitive descriptions of their discriminatory properties, appropriate for all audiences of AI. We suggest bias profiles with respect to stereotype dimensions based on dictionaries from social psychology research. Along these dimensions we investigate gender bias in contextual embeddings, across contexts and layers, and generate stereotype profiles for twelve different LLMs, demonstrating their intuition and use case for exposing and visualizing bias.

Jason L. Harman, Jaelle Scheuerman

This paper describes a generalizable model evaluation method that can be adapted to evaluate AI/ML models across multiple criteria including core scientific principles and more practical outcomes. Emerging from prediction competitions in Psychology and Decision Science, the method evaluates a group of candidate models of varying type and structure across multiple scientific, theoretic, and practical criteria. Ordinal ranking of criteria scores are evaluated using voting rules from the field of computational social choice and allow the comparison of divergent measures and types of models in a holistic evaluation. Additional advantages and applications are discussed.

Maha Nadeem, Dr Ivan Suneel Samuel

Xiao Li, Kaiwen Liu, H. Eric Tseng et al.

Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging. In this paper, we consider a behavioral model that incorporates both social behaviors and personal objectives of the interacting drivers. Leveraging this model, we develop a receding-horizon control-based decision-making strategy, that estimates online the other drivers' intentions using Bayesian filtering and incorporates predictions of nearby vehicles' behaviors under uncertain intentions. The effectiveness of the proposed decision-making strategy is demonstrated and evaluated based on simulation studies in comparison with a game theoretic controller and a real-world traffic dataset.

Clara Weber, Clara Weber, Virna Monero Flores et al.

Background: Previous research indicates that the physical environment of healthcare facilities plays an important role in the health, well-being, and recovery outcomes of patients. However, prior works on mental healthcare facilities have incorporated physical environment effects from general healthcare settings and patient groups, which cannot be readily transferred to mental healthcare settings or its patients. There appears to be a specific need for evidence synthesis of physical environmental effects in mental healthcare settings by psychopathology.Purpose: This review evaluates the state (in terms of extent, nature and quality) of the current empirical evidence of physical environmental on mental health, well-being, and recovery outcomes in mental healthcare inpatients by psychopathology.Method: A systematic review (PRISMA guidelines) was performed of studies published in English, German, Dutch, Swedish, and Spanish, of all available years until September 2020, searched in Cochrane, Ovid Index, PsycINFO, PubMed, and Web of Science and identified through extensive hand-picking. Inclusion criteria were: Adult patients being treated for mental ill-health (common mental health and mood disorders, Cochrane frame); inpatient mental health care facilities; specifications of the physical and socio-physical environment (e.g., design features, ambient conditions, privacy); all types of empirical study designs. Quality assessment and data synthesis were undertaken.Results: The search retrieved 1,068 titles of which 26 met the inclusion criteria. Findings suggest that there is only indicative evidence of the impact of the physical healthcare environment on patients' mental health, well-being, and recovery outcomes. There is significant lack of pathology-specific evidence. Methodological shortcomings and empirical scarcity account for the poor evidence.Conclusion: This review highlights the need for more research using advanced study designs.

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.

Thomas R. Shultz, Ardavan S. Nobandegani, Zilong Wang

We propose a relatively simple computational neural-network model of number comparison. Training on comparisons of the integers 1-9 enable the model to efficiently and accurately simulate a wide range of phenomena, including distance and ratio effects and robust generalization to multidigit integers, negative numbers, and decimal numbers. An accompanying logical model of number comparison provides further insights into the workings of number comparison and its relation to the Arabic number system. These models provide a rational basis for the psychology of number comparison and the ability of neural networks to efficiently learn a powerful system with robust generalization.

Keith Harrigian, Mark Dredze

Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental health language from the last decade. However, psychiatric conditions are dynamic; a prior depression diagnosis may no longer be indicative of an individual's mental health, either due to treatment or other mitigating factors. We ask: to what extent are self-disclosures of mental health diagnoses actually relevant over time? We analyze recent activity from individuals who disclosed a depression diagnosis on social media over five years ago and, in turn, acquire a new understanding of how presentations of mental health status on social media manifest longitudinally. We also provide expanded evidence for the presence of personality-related biases in datasets curated using self-disclosed diagnoses. Our findings motivate three practical recommendations for improving mental health datasets curated using self-disclosed diagnoses: 1) Annotate diagnosis dates and psychiatric comorbidities; 2) Sample control groups using propensity score matching; 3) Identify and remove spurious correlations introduced by selection bias.

Heidelinde Dehaene, Jan De Neve, Yves Rosseel

We propose an extension of the Wilcoxon–Mann–Whitney test to compare two groups when the outcome variable is latent. We empirically demonstrate that the test can have superior power properties relative to tests based on Structural Equation Modeling for a variety of settings. In addition, several other advantages of the Wilcoxon–Mann–Whitney test are retained such as robustness to outliers and good small sample performance. We demonstrate the proposed methodology on a case study.

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.

Jingye Chen, Haiyang Yu, Jianqi Ma et al.

In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Likewise, when humans observe a low-resolution text image, they will inherently use partial stroke-level details to recover the appearance of holistic characters. Inspired by Gestalt Psychology, we put forward a Stroke-Aware Scene Text Image Super-Resolution method containing a Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures of characters in text images. Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on TextZoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.

Viet-Hung Dao, David Gunawan, Minh-Ngoc Tran et al.

Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-sample descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation has remained out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, the CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, and makes it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible.