Hasil untuk "Psychology"

A. Sameroff

Ignacio Montero, Orfelio G. León

H. Simon

E. Lepore, Z. Pylyshyn

Jerry A. Fodor

L. Vygotsky

K. Lewin, R. Lippitt, R. White

J. K. McCreary

Omer Nahum, Asael Sklar, Ariel Goldstein et al.

Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation. We examine whether LLMs "report" varying levels of motivation, how these reports relate to their behavior, and whether external factors can influence them. Our experiments reveal consistent and structured patterns that echo human psychology: self-reported motivation aligns with different behavioral signatures, varies across task types, and can be modulated by external manipulations. These findings demonstrate that motivation is a coherent organizing construct for LLM behavior, systematically linking reports, choices, effort, and performance, and revealing motivational dynamics that resemble those documented in human psychology. This perspective deepens our understanding of model behavior and its connection to human-inspired concepts.

Hubert Plisiecki

Text embeddings have become central to computational social science and psychology, enabling scalable measurement of meaning and mixed-method inference. Yet most representation learning is optimized and evaluated for prediction and retrieval, yielding a prediction-measurement gap: representations that perform well as features may be poorly suited as scientific instruments. The paper argues that scientific meaning analysis motivates a distinct family of objectives - scientific usability - emphasizing geometric legibility, interpretability and traceability to linguistic evidence, robustness to non-semantic confounds, and compatibility with regression-style inference over semantic directions. Grounded in cognitive and neuro-psychological views of meaning, the paper assesses static word embeddings and contextual transformer representations against these requirements: static spaces remain attractive for transparent measurement, whereas contextual spaces offer richer semantics but entangle meaning with other signals and exhibit geometric and interpretability issues that complicate inference. The paper then outlines a course-setting agenda around (i) geometry-first design for gradients and abstraction, including hierarchy-aware spaces constrained by psychologically privileged levels; (ii) invertible post-hoc transformations that recondition embedding geometry and reduce nuisance influence; and (iii) meaning atlases and measurement-oriented evaluation protocols for reliable and traceable semantic inference. As the field debates the limits of scale-first progress, measurement-ready representations offer a principled new frontier.

Elisa Gouvêa, Cláudia Aparecida Valderramas Gomes

Este artigo apresenta parte dos resultados de uma pesquisa de mestrado, que teve como escopo a apreensão dos sentidos do trabalho docente durante o período das atividades remotas, na pandemia do novo coronavírus. Para tanto, parte-se do entendimento da Psicologia Histórico-Cultural de que o mundo objetivo possui sua expressão subjetiva, dado que constitui uma unidade objetivo-subjetiva. Tal unidade se traduz para os sujeitos como sentidos subjetivos, composto por processos afetivos e cognitivos. Para a produção dos dados, foram realizados cinco encontros de um grupo focal com quatro docentes do Ensino Médio de uma escola pública, localizada no interior de São Paulo, e para a análise utilizou-se a metodologia qualitativa dos Núcleos de Significação. Os resultados indicaram a constituição de dois núcleos: I) Contexto pandêmico e sofrimento docente e II) Questões estruturais e trabalho docente, ambos sintetizaram mediações, por meio das quais ficou demonstrado que a pandemia intensificou processos de sofrimento que já vinham acometendo professores e professoras, e que o uso das Tecnologias de Informação e Comunicação – TICs – contribuiu para a disseminação da ideologia neoliberal na Educação. O estudo consolidou a atividade como a gênese dos sentidos, os quais, por interferência das condições pandêmicas, materializaram o esvaziamento do trabalho docente.

Bryce-Allen Bagley, Navin Khoshnan

The complexity of human cognition has meant that psychology makes more use of theory and conceptual models than perhaps any other biomedical field. To enable precise quantitative study of the full breadth of phenomena in psychological and psychiatric medicine as well as cognitive aspects of AI safety, there is a need for a mathematical formulation which is both mathematically precise and equally accessible to experts from numerous fields. In this paper we formalize human psychodynamics via the diagrammatic framework of process theory, describe its key properties, and explain the links between a diagrammatic representation and central concepts in analysis of cognitive processes in contexts such as psychotherapy, neurotechnology, AI alignment, AI agent representation of individuals in autonomous negotiations, developing human-like AI systems, and other aspects of AI safety.

Fengming Zhu, Yuxin Pan, Xiaomeng Zhu et al.

Originating in psychology, $\textit{Theory of Mind}$ (ToM) has attracted significant attention across multiple research communities, especially logic, economics, and robotics. Most psychological work does not aim at formalizing those central concepts, namely $\textit{goals}$, $\textit{intentions}$, and $\textit{beliefs}$, to automate a ToM-based computational process, which, by contrast, has been extensively studied by logicians. In this paper, we offer a different perspective by proposing a computational framework viewed through the lens of game theory. On the one hand, the framework prescribes how to make boudedly rational decisions while maintaining a theory of mind about others (and recursively, each of the others holding a theory of mind about the rest); on the other hand, it employs statistical techniques and approximate solutions to retain computability of the inherent computational problem.

Shravan Chaudhari, Trilokya Akula, Yoon Kim et al.

In this paper, we advance the study of AI-augmented reasoning in the context of Human-Computer Interaction (HCI), psychology and cognitive science, focusing on the critical task of visual perception. Specifically, we investigate the applicability of Multimodal Large Language Models (MLLMs) in this domain. To this end, we leverage established principles and explanations from psychology and cognitive science related to complexity in human visual perception. We use them as guiding principles for the MLLMs to compare and interprete visual content. Our study aims to benchmark MLLMs across various explainability principles relevant to visual perception. Unlike recent approaches that primarily employ advanced deep learning models to predict complexity metrics from visual content, our work does not seek to develop a mere new predictive model. Instead, we propose a novel annotation-free analytical framework to assess utility of MLLMs as cognitive assistants for HCI tasks, using visual perception as a case study. The primary goal is to pave the way for principled study in quantifying and evaluating the interpretability of MLLMs for applications in improving human reasoning capability and uncovering biases in existing perception datasets annotated by humans.

Wei Xie, Shuoyoucheng Ma, Zhenhua Wang et al.

Large Language Models (LLMs) with hundreds of billions of parameters have exhibited human-like intelligence by learning from vast amounts of internet-scale data. However, the uninterpretability of large-scale neural networks raises concerns about the reliability of LLM. Studies have attempted to assess the psychometric properties of LLMs by borrowing concepts from human psychology to enhance their interpretability, but they fail to account for the fundamental differences between LLMs and humans. This results in high rejection rates when human scales are reused directly. Furthermore, these scales do not support the measurement of LLM psychological property variations in different languages. This paper introduces AIPsychoBench, a specialized benchmark tailored to assess the psychological properties of LLM. It uses a lightweight role-playing prompt to bypass LLM alignment, improving the average effective response rate from 70.12% to 90.40%. Meanwhile, the average biases are only 3.3% (positive) and 2.1% (negative), which are significantly lower than the biases of 9.8% and 6.9%, respectively, caused by traditional jailbreak prompts. Furthermore, among the total of 112 psychometric subcategories, the score deviations for seven languages compared to English ranged from 5% to 20.2% in 43 subcategories, providing the first comprehensive evidence of the linguistic impact on the psychometrics of LLM.

Sophia Ivantes-Rodrigues, Camila Cortellete Pereira da Silva, Leonardo Pestillo de Oliveira et al.

Experiências adversas na infância são eventos de vida potencialmente impactantes ao desenvolvimento e podem ter efeitos de longo prazo na saúde. O objetivo deste trabalho foi compreender as mudanças biopsicossociais em crianças e adolescentes que vivenciaram conflitos armados. Trata-se de uma revisão da literatura, que teve como pergunta de pesquisa: “Quais as mudanças biopsicossociais podem ser identificadas em crianças que vivenciaram eventos traumáticos como guerras e conflitos armados de larga escala”. Foram incluídos na amostra 25 artigos originais de pesquisa. Foram construídas quatro categorias temáticas por meio da análise de conteúdo de Bardin: “Adolescentes e coping: a promoção da resiliência”, “Maternidade, desenvolvimento e guerra”, “Intervenções comunitárias, suporte social e estratégias de coping” e “Desenvolvimento infantil: consequências da guerra”. Os estudos ressaltam a importância do desenvolvimento e utilização de diferentes estratégias para a promoção da saúde mental de crianças e adolescentes expostos a conflitos armados.

Julie Bernhardt, Lorna Paul, Cathal Walsh et al.

Introduction Stroke is the second-leading cause of death and disability globally. Participation in physical activity (PA) is a cornerstone of secondary prevention in stroke care. Given the heterogeneous nature of stroke, PA interventions that are adaptive to individual performance are recommended. Mobile health (mHealth) has been identified as a potential approach to supporting PA poststroke. To this end, we aim to use a Sequential Multiple Assignment Randomised Trial (SMART) design to develop an adaptive, user-informed mHealth intervention to improve PA poststroke.Methods and analysis The components included in the 12-week intervention are based on empirical evidence and behavioural change theory and will include treatments to increase participation in Structured Exercise and Lifestyle or a combination of both. 117 participants will be randomly assigned to one of the two treatment components. At 6 weeks postinitial randomisation, participants will be classified as responders or non-responders based on participants’ change in step count. Non-responders to the initial treatment will be randomly assigned to a different treatment allocation. The primary outcome will be PA (steps/day), feasibility and secondary clinical and cost outcomes will also be included. A SMART design will be used to evaluate the optimum adaptive PA intervention among community-dwelling, ambulatory people poststroke.Ethics and dissemination Ethical approval has been granted by the Health Service Executive Mid-Western Ethics Committee (REC Ref: 026/2022). The findings will be submitted for publication and presented at relevant national and international academic conferencesTrials registration number NCT05606770.

Huy Vu, Huy Anh Nguyen, Adithya V Ganesan et al.

Artificial intelligence-based language generators are now a part of most people's lives. However, by default, they tend to generate "average" language without reflecting the ways in which people differ. Here, we propose a lightweight modification to the standard language model transformer architecture - "PsychAdapter" - that uses empirically derived trait-language patterns to generate natural language for specified personality, demographic, and mental health characteristics (with or without prompting). We applied PsychAdapters to modify OpenAI's GPT-2, Google's Gemma, and Meta's Llama 3 and found generated text to reflect the desired traits. For example, expert raters evaluated PsychAdapter's generated text output and found it matched intended trait levels with 87.3% average accuracy for Big Five personalities, and 96.7% for depression and life satisfaction. PsychAdapter is a novel method to introduce psychological behavior patterns into language models at the foundation level, independent of prompting, by influencing every transformer layer. This approach can create chatbots with specific personality profiles, clinical training tools that mirror language associated with psychological conditionals, and machine translations that match an authors reading or education level without taking up LLM context windows. PsychAdapter also allows for the exploration psychological constructs through natural language expression, extending the natural language processing toolkit to study human psychology.

R. Quinn, R. Kahn