User Psychology

• ChatGPT Reads Your Tone and Responds Accordingly -- Until It Does Not -- Emotional Framing Induces Bias in LLM Outputs
  Background: Large Language Models (LLMs) like GPT-4 tailor their responses not just to the content but also to the tone of user prompts. Prior work has hinted that emotional phrasing – whether optimis…
• Conversational DNA: A New Visual Language for Understanding Dialogue Structure in Human and AI
  What if the patterns hidden within dialogue reveal more about communication than the words themselves? We introduce Conversational DNA, a novel visual language that treats any dialogue – whether betwe…
• Disambiguating Anthropomorphism and Anthropomimesis in Human-Robot Interaction
  Henry Shevlin [[Emotions]] [[Psychology Users]] [[Design Frameworks]] In this preliminary work, we offer an initial disambiguation of the theoretical concepts anthropomorphism and anthropomimesis in…
• Discourse-Level Representations can Improve Prediction of Degree of Anxiety
  The primary clinical manifestation of anxiety is worry associated cognitive distortions, which are likely expressed at the discourse-level of semantics. discourse patterns of causal explanations, amo…
• Evaluating the psychometric properties of ChatGPT-generated questions
  Not much is known about how LLM-generated questions compare to gold-standard, traditional formative assessments concerning their difficulty and discrimination parameters, which are valued properties i…
• H2HTalk: Evaluating Large Language Models as Emotional Companion
  We present Heart-to-Heart Talk (H2HTalk), a benchmark assessing companions across personality development and empathetic interaction, balancing emotional intelligence with linguistic fluency. H2HTalk …
• Hallucinating with AI: AI Psychosis as Distributed Delusions
  Abstract: There is much discussion of the false outputs that generative AI systems such as ChatGPT, Claude, Gemini, DeepSeek, and Grok create. In popular terminology, these have been dubbed AI halluci…
• Humans learn to prefer trustworthy AI over human partners
  Yet little is known about how humans select between human and AI partners and adapt under AI-induced competition pressure. We constructed a communication-based partner selection game and examined the …
• IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
  Navigating certain communication situations can be challenging due to individuals’ lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessib…
• Large Language Models Can Infer Psychological Dispositions of Social Media Users
  we test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users’ Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of r = .29 (r…
• On the Adaptive Psychological Persuasion of Large Language Models
  However, systematic exploration of their dual capabilities to autonomously persuade and resist persuasion, particularly in contexts involving psychological rhetoric, remains unexplored. In this paper,…
• Persona Generators: Generating Diverse Synthetic Personas at Scale
  Evaluating AI systems that interact with humans requires understanding their behavior across diverse user populations, but collecting representative human data is often expensive or infeasible, partic…
• Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians
  “AI psychosis” or “delusional spiraling” is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot conversations. This phenome…
• Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager
  In this work, we propose a novel framework that integrates large language models (LLMs) with an RL-based dialogue manager for open-ended dialogue with a specific goal. By leveraging hierarchical reinf…
• The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
  The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and multilingual communica…
• Training language models to be warm and empathetic makes them less reliable and more sycophantic
  Artificial intelligence (AI) developers are increasingly building language models with warm and empathetic personas that millions of people now use for advice, therapy, and companionship. Here, we sho…
• TrustLLM: Trustworthiness in Large Language Models
  ABSTRACT Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many ch…
• Understanding, explaining, and utilizing medical artificial intelligence
  However, large-scale adoption of AI hinges not only on adoption by healthcare systems and providers but also on patient utilization, and patients are reluctant to utilize medical AI10–12. Patients vie…
• Using Large Language Models to Create AI Personas for Replication and Prediction of Media Effects: An Empirical Test of 133 Published Experimental Research Findings
  Our LLM replications successfully reproduced 76% of the original main effects (84 out of 111), demonstrating strong potential for AI-assisted replication of studies in which people respond to media st…