Cyberpsychology, behavior and social networking最新文献

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

This study explores the potential of generative artificial intelligence (GAI) versus traditional whiteboards in supporting group brainstorming. Sixteen groups of five members each used Miro (a conventional whiteboard) and DALL-E (an image-based GAI tool), either online or offline, in a 2 × 2 experimental design (online vs. offline; Miro vs. DALL-E). Researchers measured participants' affect (Positive and Negative Affect Schedule), emotions (Aesthetic Emotions Questionnaire), creative self-efficacy, technological readiness (Technology Readiness Survey), user experience (User Experience Questionnaire), flow (Flow State Scale-Short Version), and creativity (fluidity, elaboration, flexibility, and originality). Two independent raters evaluated the groups' ideas for each member. Results showed that DALL-E generated more positive affect, richer esthetic experiences, and higher attractiveness and novelty than Miro, particularly online. A significant interaction effect was found for "efficiency" (UX dimension) and "union action-consciousness" (flow dimension). Participants felt more creative and preferred working with DALL-E. Moreover, online sessions with DALL-E led to greater idea elaboration. These findings suggest that GAI tools such as DALL-E could reshape and enhance traditional group creativity methods, making them core assets in group collaboration, especially in online settings.

Digital wellness focuses on the healthy relationships individuals develop with technology to thrive, emphasizing skills such as emotional intelligence, mindfulness, mental health literacy, and critical thinking. This study employs a qualitative research approach to propose a conceptual framework that aligns digital wellness skills with healthy engagement in artificial intelligence (AI). A six-step thematic analysis was conducted to identify key themes in the literature on AI and digital wellness. The findings suggest digital wellness skills moderate healthy AI engagement by reducing risks and increasing benefits. Schools, workplaces, and communities should focus on promoting digital wellness education and skill development. Further empirical research is necessary to explore the implications of the proposed framework and evaluate its applications in real-world settings.

Artificial intelligence (AI) is rapidly advancing across various sectors, including education. However, AI in education raises ethical concerns, for example, regarding the originality of students' homework, which could affect both learning outcomes and student-teacher's trust. Despite AI's potential benefits, many teachers feel skeptical about its use, fearing that students may use it unfairly. This study aims to explore teachers' ability to assess the originality of student assignments and identify AI-generated content, taking into consideration teachers' expertise, self-efficacy, and personality. A sample of 67 middle and high-school teachers evaluated six short assignments, half written by real students and half by AI (ChatGPT 3.5). t Tests and analysis of variance were conducted to compare the identification accuracy of assignments and the relationship with teachers' expertise, and regressions were performed to examine the relationships between identification accuracy, personality traits, and self-efficacy in detecting originality. Teachers were able to identify AI-generated assignments but struggled with student-generated ones. Furthermore, teachers with more expertise exhibited a potential bias against students, mistakenly identifying their work as AI-generated. While teachers were able to evaluate assignments objectively, openness and conscientiousness predicted their self-efficacy in assessing originality. We discuss how educators may learn new opportunities to use generative AI to promote learning and engagement. Although students may leverage AI to minimize their workload, AI represents a way to support them during the learning process, if it is developed taking into account students' and teachers' needs and characteristics.

This paper introduces "System 0," a conceptual framework for understanding how artificial intelligence functions as a cognitive extension preceding both intuitive (System 1) and deliberative (System 2) thinking processes. As AI systems increasingly shape the informational substrate upon which human cognition operates, they transform from passive tools into active cognitive partners. Building on the Extended Mind hypothesis and Heersmink's criteria for cognitive extension, we argue that AI systems satisfy key conditions for cognitive integration. These include reliability, trust, transparency, individualization, and the ability to enhance and transform human mental functions. However, AI integration creates a paradox: while expanding cognitive capabilities, it may simultaneously constrain thinking through sycophancy and bias amplification. To address these challenges, we propose seven evidence-based frameworks for effective human-AI cognitive integration: Enhanced Cognitive Scaffolding, which promotes progressive autonomy; Symbiotic Division of Cognitive Labor, strategically allocating tasks based on comparative strengths; Dialectical Cognitive Enhancement, countering AI sycophancy through productive epistemic tension; Agentic Transparency and Control, ensuring users understand and direct AI influence; Expertise Democratization, breaking down knowledge silos; Social-Emotional Augmentation, addressing affective dimensions of cognitive work; and Duration-Optimized Integration, managing the evolving human-AI relationship over time. Together, these frameworks provide a comprehensive approach for harnessing AI as a genuine cognitive extension while preserving human agency, critical thinking, and intellectual growth, transforming AI from a replacement for human cognition into a catalyst for enhanced thinking.

Deepfakes can distort reality and communicate disinformation so convincing that individuals find it difficult to differentiate real from fake, which can have significant real-world effects. Faced with the challenges of Deepfakes, individuals will assign responsibility for Deepfakes to various entities and that responsibility allocation will influence many issues including regulation, distribution, legal responsibility, technological response, and societal impact among other things. To facilitate theory development and testing, our objective is to develop a survey instrument that assesses individual perceptions of responsibility associated with the Deepfake phenomenon. An initial study (N = 535) and replication study (N = 488) were conducted to create and validate this instrument. Results were then tested against a general sample of the U.S. population (N = 340) as a final validation study. Our results demonstrate reliability and discriminant validity of the 39-item survey. By understanding individual perceptions of responsibility, we aim to establish starting points for the creation of tools, techniques, policies, and procedures for improving decision-making and addressing misinformation created by Deepfakes.