Hasil untuk "Psychology"

G. Gillund, R. Shiffrin

R. Stogdill

L. Resnick, J. Levine, Stephanie D. Teasley

D. Krebs

Benjamin J. Choi, Melanie Weber

The geometric structure of latent representations in large language models (LLMs) is an active area of research, driven in part by its implications for model transparency and AI safety. Existing literature has focused mainly on general geometric and topological properties of the learnt representations, but due to a lack of ground-truth latent geometry, validating the findings of such approaches is challenging. Emotion processing provides an intriguing testbed for probing representational geometry, as emotions exhibit both categorical organization and continuous affective dimensions, which are well-established in the psychology literature. Moreover, understanding such representations carries safety relevance. In this work, we investigate the latent structure of affective representations in LLMs using geometric data analysis tools. We present three main findings. First, we show that LLMs learn coherent latent representations of affective emotions that align with widely used valence--arousal models from psychology. Second, we find that these representations exhibit nonlinear geometric structure that can nonetheless be well-approximated linearly, providing empirical support for the linear representation hypothesis commonly assumed in model transparency methods. Third, we demonstrate that the learned latent representation space can be leveraged to quantify uncertainty in emotion processing tasks. Our findings suggest that LLMs acquire affective representations with geometric structure paralleling established models of human emotion, with practical implications for model interpretability and safety.

Bennett Kleinberg, Riccardo Loconte, Bruno Verschuere

Background: Deception detection through analysing language is a promising avenue using both human judgments and automated machine learning judgments. For both forms of credibility assessment, automated adversarial attacks that rewrite deceptive statements to appear truthful pose a serious threat. Methods: We used a dataset of 243 truthful and 262 fabricated autobiographical stories in a deception detection task for humans and machine learning models. A large language model was tasked to rewrite deceptive statements so that they appear truthful. In Study 1, humans who made a deception judgment or used the detailedness heuristic and two machine learning models (a fine-tuned language model and a simple n-gram model) judged original or adversarial modifications of deceptive statements. In Study 2, we manipulated the target alignment of the modifications, i.e. tailoring the attack to whether the statements would be assessed by humans or computer models. Results: When adversarial modifications were aligned with their target, human (d=-0.07 and d=-0.04) and machine judgments (51% accuracy) dropped to the chance level. When the attack was not aligned with the target, both human heuristics judgments (d=0.30 and d=0.36) and machine learning predictions (63-78%) were significantly better than chance. Conclusions: Easily accessible language models can effectively help anyone fake deception detection efforts both by humans and machine learning models. Robustness against adversarial modifications for humans and machines depends on that target alignment. We close with suggestions on advancing deception research with adversarial attack designs and techniques.

Brooklyn J. Corbett, Jason M. Tangen

Misconceptions in psychology and education persist despite clear contradictory evidence, resisting traditional correction methods. This study investigated whether personalised AI dialogue could effectively correct these stubborn beliefs. In a preregistered experiment (N = 375), participants holding strong psychology misconceptions engaged in one of three interventions: (1) personalised AI dialogue targeting their specific misconception, (2) generic textbook-style refutation, or (3) neutral AI dialogue (control). Results showed that personalised AI dialogue produced significantly larger immediate belief reductions compared to both textbook reading and neutral dialogue. This advantage persisted at 10-day follow-up but diminished by 2 months, where AI dialogue and textbook conditions converged while both remained superior to control. Both AI conditions generated significantly higher engagement and confidence than textbook reading, demonstrating the motivational benefits of conversational interaction. These findings demonstrate that AI dialogue can accelerate initial belief correction through personalised, interactive engagement that disrupts the cognitive processes maintaining misconceptions. However, the convergence of effects over time suggests brief interventions require reinforcement for lasting change. Future applications should integrate AI tutoring into structured educational programs with spaced reinforcement to sustain the initial advantages of personalised dialogue.

Şen Şenol

This study aimed to investigate the relationships between intrinsic motivation, self-efficacy, task value, and smartphone addiction. To examine the relationship between smartphone addiction and intrinsic motivation, a mediation model was developed, in which self-efficacy and task value acted as mediating variables. The Smartphone Addiction Scale and Motivated Strategies for Learning Questionnaire were administered to 534 high school students to collect data. The results revealed statistically significant negative relationships between smartphone addiction and students’ intrinsic motivation, self-efficacy, and task values. Additionally, self-efficacy and task value were found to be possible mediators within the model. These findings suggest that smartphone addiction may lead to a decrease in students’ intrinsic motivation by reducing their self-efficacy and task value. In conclusion, although smartphone use enhances our daily lives, it may also have negative effects on certain affective variables that play an important role in the learning process, as demonstrated in this study.

Ana Paula Giardini Pedro

RESUMO No século XV, pontífices, senhores seculares e homens de poder italianos, imbuídos do desejo de restaurar a dignidade que outrora caracterizou a antiga Roma, identificavam as virtudes de liberalitas e magnificentia como instrumentos de legitimação política. No entanto, contavam com disposição territorial e financeira tanto mais estreita que aquela de pregressos impérios. Nesse contexto, Leon Battista Alberti, vislumbrando incitar investimentos edilícios e profícuas relações de mecenato a arquitetos, identifica em valores antigos a chave para essa complexa questão: a dignificação da arquitetura e do artífice para construção de registros sempiternos do ‘bom governo’. Em seu De Re Ædifcatoria, o humanista prescreve a excelência da arte fundada na concinnitas, que perpassa a ratio mediocritatis e cinge os preceitos vitruvianos de decor e symmetria descritos no De Architectura. Assim, este artigo propõe compreender como ambos os tratadistas, cada um a seu modo, demonstram que o valor excelso da Arquitetura, alcançado pela sollertia ingenium guiada pela reflexão doutrinal, ultrapassa a preciosidade vulgar de investimentos suntuosos. Assim, conferindo auctoritas a obras que fizessem presente dignitas e honestas no reconhecimento visual, mesmo com investimentos parcimoniosos.

B. Foss

Andrew Smart, Ding Wang, Ellis Monk et al.

Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness, model performance, and new lines of research have begun to examine the working conditions for data annotation workers, the impacts and role of annotator subjectivity on labels, and the potential psychological harms from aspects of annotation work. This paper outlines a critical genealogy of data annotation; starting with its psychological and perceptual aspects. We draw on similarities with critiques of the rise of computerized lab-based psychological experiments in the 1970's which question whether these experiments permit the generalization of results beyond the laboratory settings within which these results are typically obtained. Do data annotations permit the generalization of results beyond the settings, or locations, in which they were obtained? Psychology is overly reliant on participants from Western, Educated, Industrialized, Rich, and Democratic societies (WEIRD). Many of the people who work as data annotation platform workers, however, are not from WEIRD countries; most data annotation workers are based in Global South countries. Social categorizations and classifications from WEIRD countries are imposed on non-WEIRD annotators through instructions and tasks, and through them, on data, which is then used to train or evaluate AI models in WEIRD countries. We synthesize evidence from several recent lines of research and argue that data annotation is a form of automated social categorization that risks entrenching outdated and static social categories that are in reality dynamic and changing. We propose a framework for understanding the interplay of the global social conditions of data annotation with the subjective phenomenological experience of data annotation work.

Sannyuya Liu, Qing Li, Xiaoxuan Shen et al.

Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack generalizability. This paper aims to unearth the laws of skill learning from large-scale training log data. A two-stage algorithm was developed to tackle the issues of unobservable cognitive states and algorithmic explosion in searching. Initially a deep learning model is employed to determine the learner's cognitive state and assess the feature importance. Subsequently, symbolic regression algorithms are utilized to parse the neural network model into algebraic equations. Experimental results show the algorithm can accurately restore preset laws within a noise range in continuous feedback settings. When applied to Lumosity training data, the method outperforms traditional and recent models in fitness terms. The study reveals two new forms of skill acquisition laws and reaffirms some previous findings.

Irina Rudenko, Andrey Rudenko, Achim J. Lilienthal et al.

Social robots, owing to their embodied physical presence in human spaces and the ability to directly interact with the users and their environment, have a great potential to support children in various activities in education, healthcare and daily life. Child-Robot Interaction (CRI), as any domain involving children, inevitably faces the major challenge of designing generalized strategies to work with unique, turbulent and very diverse individuals. Addressing this challenging endeavor requires to combine the standpoint of the robot-centered perspective, i.e. what robots technically can and are best positioned to do, with that of the child-centered perspective, i.e. what children may gain from the robot and how the robot should act to best support them in reaching the goals of the interaction. This article aims to help researchers bridge the two perspectives and proposes to address the development of CRI scenarios with insights from child psychology and child development theories. To that end, we review the outcomes of the CRI studies, outline common trends and challenges, and identify two key factors from child psychology that impact child-robot interactions, especially in a long-term perspective: developmental stage and individual characteristics. For both of them we discuss prospective experiment designs which support building naturally engaging and sustainable interactions.

Luna Fazio, Paul-Christian Bürkner

Accounting for the complexity of psychological theories requires methods that can predict not only changes in the means of latent variables -- such as personality factors, creativity, or intelligence -- but also changes in their variances. Structural equation modeling (SEM) is the framework of choice for analyzing complex relationships among latent variables, but the modeling of latent variances as a function of other latent variables is a task that current methods only support to a limited extent. In this paper, we develop a Bayesian framework for Gaussian distributional SEM which broadens the scope of feasible models for latent heteroscedasticity. We use statistical simulation to validate our framework across four distinct model structures, in which we demonstrate that reliable statistical inferences can be achieved and that computation can be performed with sufficient efficiency for practical everyday use. We illustrate our framework's applicability in a real-world case study that addresses a substantive hypothesis from personality psychology.

Chuang Chen, Xiao Sun, Zhi Liu

Visual emotion analysis holds significant research value in both computer vision and psychology. However, existing methods for visual emotion analysis suffer from limited generalizability due to the ambiguity of emotion perception and the diversity of data scenarios. To tackle this issue, we introduce UniEmoX, a cross-modal semantic-guided large-scale pretraining framework. Inspired by psychological research emphasizing the inseparability of the emotional exploration process from the interaction between individuals and their environment, UniEmoX integrates scene-centric and person-centric low-level image spatial structural information, aiming to derive more nuanced and discriminative emotional representations. By exploiting the similarity between paired and unpaired image-text samples, UniEmoX distills rich semantic knowledge from the CLIP model to enhance emotional embedding representations more effectively. To the best of our knowledge, this is the first large-scale pretraining framework that integrates psychological theories with contemporary contrastive learning and masked image modeling techniques for emotion analysis across diverse scenarios. Additionally, we develop a visual emotional dataset titled Emo8. Emo8 samples cover a range of domains, including cartoon, natural, realistic, science fiction and advertising cover styles, covering nearly all common emotional scenes. Comprehensive experiments conducted on six benchmark datasets across two downstream tasks validate the effectiveness of UniEmoX. The source code is available at https://github.com/chincharles/u-emo.

Lu'liyatul Mutmainah, Listia Andani, Ela Susilawati

The mosque has so far been known only as a place of worship such as prayer and recitation by Muslims. History records that during the time of the Prophet, the mosque was also the center of government, economic center, education center, and others. Some mosques are also tourist areas that provide more economic value so that they can improve people's welfare. However, tourist areas often cause problems related to waste, water use, and others. Understanding of the circular economy that can provide sustainable benefits is still not widely known and implemented, including for managing mosques. This study aims to analyze and propose an optimization model for mosque-based circular economic empowerment to achieve a sustainable economy. The research uses a qualitative approach with literature studies and in-depth interviews with related parties. The results of the study show that empowering mosques based on a circular economy will not only have a positive impact on places of worship but also the economic, social, and environmental sectors. For example, managed mosque waste can provide economic value. In addition, the use of ablution water can be reused for land irrigation and fish farming. The synergy between the government, universities, communities, and the industrial world can be carried out to implement this mosque-based circular economy. The results of this study can be used as a basis for recommendations and a pilot project for implementing mosque-based circular economic empowerment.

María del Carmen Salgueiro

Matthias Gegenwart

This paper analyses, if the Intertemporal Guarantee of Freedom, that was developed by the German Federal Constitutional Court (GFCC), can be used to expand the protection of human rights against the harms of climate change. The case of the Swiss Senior Women shows that there are jurisdictions, where the Intertemporal Guarantee of Freedom could be applied to improve standing and the control standard of states’ climate change action. Within international law bodies with jurisdiction over human rights treaties there are distinctive standards of protection against the harms of climate change. A major deficit within the international human rights protection against climate change lies within the focus on the positive obligations and the corresponding wide margin of appreciation granted to the states. The Intertemporal Guarantee of Freedom could provide a protection expansion in this regard, especially in the case of the European Court of Human Rights. It could also enable and legitimise present human rights concerns focused on the future actions of states following their past inaction. One considerable hurdle that is not addressed by it are procedural hurdles like the Plaumann formula applied by the European Court of Justice. The Intertemporal Guarantee of Freedom cannot solve major problems for climate change litigation like procedural hurdles. Yet, it can provide a new approach for complaints to address unambitious mitigation legislation which will lead to future human rights infringements.

Jane K.J. Lee, Yun-Tao Liu, Jason J. Hu et al.

Propionyl-CoA carboxylase (PCC) is a multienzyme complex consisting of up to six α-subunits and six β-subunits. Belonging to a metabolic pathway converging on the citric acid cycle, it is present in most forms of life and irregularities in its assembly lead to serious illness in humans, known as propionic acidemia. Here, we report the cryogenic electron microscopy (cryoEM) structures and assembly of different oligomeric isomers of endogenous PCC from the parasitic protozoan Leishmania tarentolae (LtPCC). These structures and their statistical distribution reveal the mechanics of PCC assembly and disassembly at equilibrium. We show that, in solution, endogenous LtPCC β-subunits form stable homohexamers, to which different numbers of α-subunits attach. Sorting LtPCC particles into seven classes (i.e., oligomeric formulae α0β6, α1β6, α2β6, α3β6, α4β6, α5β6, α6β6) enables formulation of a model for PCC assembly. Our results suggest how multimerization regulates PCC enzymatic activity and showcase the utility of cryoEM in revealing the statistical mechanics of reaction pathways.

L. Carmichael

This is the second edition of a well known encyclopedic manual of child psychology, written ten by twenty well known specialists whose research covers every aspect of child development and behavior. It is a basic book for any one planning research in growth and development, whether it deal with animals or human beings, physical growth or intellectual, emotional and social development. The pediatrician in practice will find this a valuable reference book, not only for information, but for its bibliographies. Every department of pediatrics should have this book in its library.