Hasil untuk "Psychology"

Alexandra Ciobotaru, Ana-Maria Bucur, Liviu P. Dinu

Psychological corpora in NLP are collections of texts used to analyze human psychology, emotions, and mental health. These texts allow researchers to study psychological constructs, identify patterns related to mental health problems and analyze emotional language. However, collecting accurate mental health data from social media can be challenging due to the assumptions made by data collectors. A more effective approach involves gathering data through open-ended questions and then assessing participants' mental health status using self-report screening surveys. This method was successfully employed for English, a language with a lot of psychological NLP resources. However, the same cannot be stated for Romanian, which currently has no open-source mental health corpus. To address this gap, we have collected the first open-source corpus focused on depression and anxiety in Romanian, by utilizing a form with 6 open-ended questions along with the standardized PHQ-9 and GAD-7 screening questionnaires. Although the PsihoRo corpus contains texts from only 205 respondents, it represents an important first step toward understanding and analyzing mental health issues within the Romanian population. We employ statistical analysis, text analysis using Romanian LIWC, emotion detection, and topic modeling to identify the most important features of this newly introduced resource for the NLP community. The data is publicly available at https://huggingface.co/datasets/Alegzandra/PsihoRo.

Amalie Brogaard Pauli, Maria Barrett, Max Müller-Eberstein et al.

Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade. Prior work has shown that LLMs can successfully persuade humans and amplify persuasive language. It is therefore essential to understand how user instructions affect the generation of persuasive language, and to understand whether the generated persuasive language differs, for example, when targeting different groups. In this work, we propose a framework for evaluating how persuasive language generation is affected by recipient gender, sender intent, or output language. We evaluate 13 LLMs and 16 languages using pairwise prompt instructions. We evaluate model responses on 19 categories of persuasive language using an LLM-as-judge setup grounded in social psychology and communication science. Our results reveal significant gender differences in the persuasive language generated across all models. These patterns reflect biases consistent with gender-stereotypical linguistic tendencies documented in social psychology and sociolinguistics.

Saiful Haq, Niyati Chhaya, Piyush Pandey et al.

In this paper, we present an investigative study on how Mental Sets influence the reasoning capabilities of LLMs. LLMs have excelled in diverse natural language processing (NLP) tasks, driven by advancements in parameter-efficient fine-tuning (PEFT) and emergent capabilities like in-context learning (ICL). For complex reasoning tasks, selecting the right model for PEFT or ICL is critical, often relying on scores on benchmarks such as MMLU, MATH, and GSM8K. However, current evaluation methods, based on metrics like F1 Score or reasoning chain assessments by larger models, overlook a key dimension: adaptability to unfamiliar situations and overcoming entrenched thinking patterns. In cognitive psychology, Mental Set refers to the tendency to persist with previously successful strategies, even when they become inefficient - a challenge for problem solving and reasoning. We compare the performance of LLM models like Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct and GPT-4o in the presence of mental sets. To the best of our knowledge, this is the first study to integrate cognitive psychology concepts into the evaluation of LLMs for complex reasoning tasks, providing deeper insights into their adaptability and problem-solving efficacy.

Philipp Steinebach, Miriam Stein, Knut Schnell

Abstract Background How accurate are empathic judgments of couples in smartphone messenger communication? Are judgments influenced by the level of experience with messengers and communication frequency?. Objectives The current preregistered study introduced a novel ecological assessment method and a privacy by design approach to study couples’ empathic accuracy in a messenger context. Methods Data from N = 102 participants (51 couples) was used to investigate how accurate judgments of partners’ affect map their partners’ actual affect. Results Our results demonstrate tracking accuracy and assumed similarity bias in reciprocal empathic judgments of affective valence and arousal during social messenger texting. A small moderation effect for experience with text messengers was found, indicating that partners with higher experience have a higher bias of assumed similarity when rating their partner’s valence. A small moderation effect for communication frequency confirms that higher messenger communication frequency is associated with more accurate judgments of arousal. Conclusion These results point to the reciprocal action of accuracy and bias in couples’ messenger communication and the distinct influences of experience and usage. The feasibility and further application of the ecological messenger-based assessment of couples’ empathic accuracy in interpersonal research are discussed.

Laura Bosaia, Gemma Garbi, Elisa Berlin et al.

Adolescents’ perception and recognition of sexual harassment (SH) are shaped by several psychosocial variables, including gender norms, emotional responses, and ideological beliefs (such as sexism). This study specifically aimed to investigate the mediating roles of moral disengagement and emotional responses in the relationship between tolerance of SH and recognition of harassment scenarios, while considering gender as a moderator. The sample included 380 high-school students (55.3% female, 44.7% male), aged between 14 and 18 years (M_age = 15.71, SD_age = 0.87). No significant direct association was found between attitudes toward sexually harassing behaviour (TSHI) and recognition of potential harassing scenario (assessed by the Sexual Harassment Definitions Questionnaire—SHDO). However, TSHI was indirectly associated with SHDO through two distinct mediational pathways. On the one hand, higher tolerance of sexual harassment was associated with increased moral disengagement, which in turn was related to lower recognition of SH. On the other hand, it was associated with reduced negative emotional reactions, which were in turn associated with greater recognition of harassment. Additionally, gender differences emerged: females demonstrated greater ability to identify harassment scenarios and reported stronger negative emotional reactions. Overall, these findings highlight the role of psychosocial mechanisms in shaping adolescents’ recognition of harassment situations.

Constant Bonard, Gustave Cortal

We propose leveraging cognitive science research on emotions and communication to improve language models for emotion analysis. First, we present the main emotion theories in psychology and cognitive science. Then, we introduce the main methods of emotion annotation in natural language processing and their connections to psychological theories. We also present the two main types of analyses of emotional communication in cognitive pragmatics. Finally, based on the cognitive science research presented, we propose directions for improving language models for emotion analysis. We suggest that these research efforts pave the way for constructing new annotation schemes, methods, and a possible benchmark for emotional understanding, considering different facets of human emotion and communication.

U. León-Domínguez, E. D. Flores-Flores, A. J. García-Jasso et al.

Large Language Models based on transformer algorithms have revolutionized Artificial Intelligence by enabling verbal interaction with machines akin to human conversation. These AI agents have surpassed the Turing Test, achieving confusion rates up to 50%. However, challenges persist, especially with the advent of robots and the need to humanize machines for improved Human-AI collaboration. In this experiment, three GPT agents with varying levels of agreeableness (disagreeable, neutral, agreeable) based on the Big Five Inventory were tested in a Turing Test. All exceeded a 50% confusion rate, with the highly agreeable AI agent surpassing 60%. This agent was also recognized as exhibiting the most human-like traits. Various explanations in the literature address why these GPT agents were perceived as human, including psychological frameworks for understanding anthropomorphism. These findings highlight the importance of personality engineering as an emerging discipline in artificial intelligence, calling for collaboration with psychology to develop ergonomic psychological models that enhance system adaptability in collaborative activities.

Adrita Barua, Gary Brase, Ke Dong et al.

We subject GPT-4 to a number of rigorous psychometric tests and analyze the results. We find that, compared to the average human, GPT-4 tends to show more honesty and humility, and less machiavellianism and narcissism. It sometimes exhibits ambivalent sexism, leans slightly toward masculinity, is moderately anxious but mostly not depressive (but not always). It shows human-average numerical literacy and has cognitive reflection abilities that are above human average for verbal tasks.

Leif Hancox-Li, Borhane Blili-Hamelin

ETHICS is probably the most-cited dataset for testing the ethical capabilities of language models. Drawing on moral theory, psychology, and prompt evaluation, we interrogate the validity of the ETHICS benchmark. Adding to prior work, our findings suggest that having a clear understanding of ethics and how it relates to empirical phenomena is key to the validity of ethics evaluations for AI.

Goran Bubaš

OpenAI's ChatGPT (GPT-4 and GPT-4o) and other Large Language Models (LLMs) like Microsoft's Copilot, Google's Gemini 1.5 Pro, and Antrophic's Claude 3.5 Sonnet can be effectively used in various phases of scientific research. Their performance in diverse verbal tasks and reasoning is close to or above the average human level and rapidly increasing, providing those models with a capacity that resembles a relatively high level of theory of mind. The current ability of LLMs to process information about human psychology and communication creates an opportunity for their scientific use in the fields of personality psychology and interpersonal communication skills. This article illustrates the possible uses of GPT-4o and other advanced LLMs for typical tasks in designing self-assessment scales for interpersonal communication skills measurement like the selection and improvement of scale items and evaluation of content validity of scales. The potential for automated item generation and application is illustrated as well. The case study examples are accompanied by prompts for LLMs that can be useful for these purposes. Finally, a summary is provided of the potential benefits of using LLMs in the process of evaluation, design, and improvement of interpersonal communication skills self-assessment scales.

Grgur Kovač, Rémy Portelas, Masataka Sawayama et al.

The standard way to study Large Language Models (LLMs) with benchmarks or psychology questionnaires is to provide many different queries from similar minimal contexts (e.g. multiple choice questions). However, due to LLMs' highly context-dependent nature, conclusions from such minimal-context evaluations may be little informative about the model's behavior in deployment (where it will be exposed to many new contexts). We argue that context-dependence (specifically, value stability) should be studied as a specific property of LLMs and used as another dimension of LLM comparison (alongside others such as cognitive abilities, knowledge, or model size). We present a case-study on the stability of value expression over different contexts (simulated conversations on different topics) as measured using a standard psychology questionnaire (PVQ) and on behavioral downstream tasks. Reusing methods from psychology, we study Rank-order stability on the population (interpersonal) level, and Ipsative stability on the individual (intrapersonal) level. We consider two settings (with and without instructing LLMs to simulate particular personas), two simulated populations, and three downstream tasks. We observe consistent trends in the stability of models and model families - Mixtral, Mistral, GPT-3.5 and Qwen families are more stable than LLaMa-2 and Phi. The consistency of these trends implies that some models exhibit higher value stability than others, and that stability can be estimated with the set of introduced methodological tools. When instructed to simulate particular personas, LLMs exhibit low Rank-order stability, which further diminishes with conversation length. This highlights the need for future research on LLMs that coherently simulate different personas. This paper provides a foundational step in that direction, and, to our knowledge, it is the first study of value stability in LLMs.

Timothy A. Yonts

The paper explores Michael Novak’s understanding of the human person and his normative case for the market economy, specifically its points of agreement and tension with natural rights libertarianism. For Michael Novak, the imago dei provides the strongest account for the relationship between the market economy, human dignity, and natural rights. Rationalistic attempts, such as those within libertarianism, cannot adequately ground human dignity or sustain free institutions capable of serving the common good, the market economy, and political liberty. However, Novak’s affinity to his libertarian interlocutors presents an opportunity for dialogue on the necessity of economic freedom and related theological influences on natural rights theory for securing human flourishing.

Courtland Leer, Vincent Trost, Vineeth Voruganti

Recent research shows that Large Language Models (LLMs) exhibit a compelling level of proficiency in Theory of Mind (ToM) tasks. This ability to impute unobservable mental states to others is vital to human social cognition and may prove equally important in principal-agent relations between individual humans and Artificial Intelligences (AIs). In this paper, we explore how a mechanism studied in developmental psychology known as Violation of Expectation (VoE) can be implemented to reduce errors in LLM prediction about users by leveraging emergent ToM affordances. And we introduce a \textit{metacognitive prompting} framework to apply VoE in the context of an AI tutor. By storing and retrieving facts derived in cases where LLM expectation about the user was violated, we find that LLMs are able to learn about users in ways that echo theories of human learning. Finally, we discuss latent hazards and augmentative opportunities associated with modeling user psychology and propose ways to mitigate risk along with possible directions for future inquiry.

Louis Hickman, Jason Kuruzovich, Vincent Ng et al.

Organizations are increasingly adopting machine learning (ML) for personnel assessment. However, concerns exist about fairness in designing and implementing ML assessments. Supervised ML models are trained to model patterns in data, meaning ML models tend to yield predictions that reflect subgroup differences in applicant attributes in the training data, regardless of the underlying cause of subgroup differences. In this study, we systematically under- and oversampled minority (Black and Hispanic) applicants to manipulate adverse impact ratios in training data and investigated how training data adverse impact ratios affect ML model adverse impact and accuracy. We used self-reports and interview transcripts from job applicants (N = 2,501) to train 9,702 ML models to predict screening decisions. We found that training data adverse impact related linearly to ML model adverse impact. However, removing adverse impact from training data only slightly reduced ML model adverse impact and tended to negatively affect ML model accuracy. We observed consistent effects across self-reports and interview transcripts, whether oversampling real (i.e., bootstrapping) or synthetic observations. As our study relied on limited predictor sets from one organization, the observed effects on adverse impact may be attenuated among more accurate ML models.

Fatemeh Nikkhoo, Somaiyeh Fathipoor

The mental pressure caused by having a deaf child has tremendous effects on mothers. This pressure leads to psychological distress and parenting stress for mothers. Therefore, the aim of this study was to investigate the effectiveness of the training protocol based on acceptance and commitment on the psychological distress and parenting stress of mothers with deaf children. The current research was a semi-experimental type with pre-test-post-test and control group. The statistical population was all mothers with deaf children in Tehran. Among these mothers, 30 mothers were selected by available sampling method and then randomly assigned to two experimental and control groups equally. The instrument of this study was the scale of psychological distress and stress of the overbearing child. The sessions of the experimental group consisted of 8 90-minute sessions of acceptance and commitment-based therapy (Bond et al., 2011), which were implemented as a group. Data were analyzed using multivariate analysis of covariance. The results showed that the training protocol based on acceptance and commitment has led to the reduction of psychological distress and parenting stress of mothers (p<0.05). Based on the obtained results, the training protocol based on acceptance and commitment is effective in reducing psychological distress and reducing parenting stress. The results of this study can be effective in helping to reduce the psychological problems of families with deaf children and thus reducing the mental health costs of these families.

Muhammad Ovais Ahmad, Hadi Ghanbari, Tomas Gustavsson

Software development is a team work and largely dependent on open social interaction and continuous learning of individuals. Drawing on well established theoretical concepts proposed by social psychology and organizational science disciplines, we develop a theoretical framework proposing that team climate has a significant influence on team learning and ultimately affects team performance. Our study consists of two goals. First to understand the preconditions of team learning and second to investigate the relationship between team learning, psychological safety, and team performance in large scale agile software development projects. We plan to conduct a survey with software professionals in Sweden from three companies partners in pur large-scale agile research project.

Alexandre Augusto de Deus Pontual, Alexandre Augusto de Deus Pontual, Luís Fernando Tófoli et al.

Recent studies have recognized the importance of non-pharmacological factors such as setting to induce or promote mystical experiences or challenging experiences among ayahuasca users. This study aimed to evaluate the association between the setting in which ayahuasca is consumed and the intensity of mystical and challenging experiences considering three ayahuasca using traditions (União do Vegetal, Santo Daime and neo-shamanic groups). A cross-sectional analysis was performed on survey data collected online from 2,751 participants. The Setting Questionnaire for the Ayahuasca Experience (SQAE) was used to evaluate six dimensions of the setting characteristics. The Mystical Experience Questionnaire (MEQ) and the Challenging Experience Questionnaire (CEQ) were used to quantify the psychedelic experience. Ratings on every SQAE setting dimension were negatively correlated with ratings of the CEQ (r values between 0.21 and 0.36) for all ayahuasca using traditions. Regression analysis revealed that ratings on four SQAE dimensions (Social, Comfort, Infrastructure and Decoration) explained 41% of the variance in CEQ ratings. Associations between SQAE and MEQ ratings were relatively weak and confined to the dimensions Leadership and Comfort, explaining 14% of the variance in MEQ ratings. Ratings of Social context were higher among members of União do Vegetal compared to Santo Daime and neo-shamanic members. Ratings of Infrastructure, Comfort and Decoration were more consistently correlated with MEQ in the neoshamanic tradition compared to the other traditions. This study shows that the setting is an important moderator of a challenging experience under ayahuasca. Maximizing the quality of the setting in which ayahuasca is taken will reduce the chance of a challenging experience while contributing positively to a mystical experience. The present findings can be considered when designing rituals and the (social) environment of ayahuasca ceremonies, and indicate that the SQAE questionnaire can be employed to monitor the influence of ceremonial settings on the ayahuasca experience.

Bron Taylor

La traduzione rispetta sia la forma linguistica che la struttura compositiva del testo originale. Per questo motivo si è ritenuto di non dover tradurre le definizioni green religion, dark green religion, spiritual but not religious e greening of religion in quanto non ci sono corrispettivi italiani già in uso che rendano la complessità delle suddette categorie. Riteniamo che i termini possano risultare comunque comprensibili ad un pubblico italofono che si approcci al testo.

Bohan Jiang, Mansooreh Karami, Lu Cheng et al.

Echo chambers may exclude social media users from being exposed to other opinions, therefore, can cause rampant negative effects. Among abundant evidence are the 2016 and 2020 US presidential elections conspiracy theories and polarization, as well as the COVID-19 disinfodemic. To help better detect echo chambers and mitigate its negative effects, this paper explores the mechanisms and attributes of echo chambers in social media. In particular, we first illustrate four primary mechanisms related to three main factors: human psychology, social networks, and automatic systems. We then depict common attributes of echo chambers with a focus on the diffusion of misinformation, spreading of conspiracy theory, creation of social trends, political polarization, and emotional contagion of users. We illustrate each mechanism and attribute in a multi-perspective of sociology, psychology, and social computing with recent case studies. Our analysis suggest an emerging need to detect echo chambers and mitigate their negative effects.

José A. Ruiz S. Román

José Pérez Adán, Ediciones Internacionales Universitarias, Madrid 2005