Owing to the recent success of Large Language Models, Modern A.I has been much focused on linguistic interactions with humans but less focused on non-linguistic forms of communication between man and machine. In the present paper, we test how affective-linguistic communication, in combination with differential outcomes training, affects mutual learning in a human-robot context. Taking inspiration from child-caregiver dynamics, our human-robot interaction setup consists of a (simulated) robot attempting to learn how best to communicate internal, homeostatically-controlled needs; while a human "caregiver" attempts to learn the correct object to satisfy the robot's present communicated need. We studied the effects of i) human training type, and ii) robot reinforcement learning type, to assess mutual learning terminal accuracy and rate of learning (as measured by the average reward achieved by the robot). Our results find mutual learning between a human and a robot is significantly improved with Differential Outcomes Training (DOT) compared to Non-DOT (control) conditions. We find further improvements when the robot uses an exploration-exploitation policy selection, compared to purely exploitation policy selection. These findings have implications for utilizing socially assistive robots (SAR) in therapeutic contexts, e.g. for cognitive interventions, and educational applications.
Algorithmic Reflectivity in Large Language Models: Methodological Study of Linguistic Structure in Mentalization
University of Pavia（帕维亚大学）；Digital Transformation Institute（数字化转型研究所）；Psychoanalytic Academy of Italian-Speaking Switzerland（意大利语区瑞士精神分析学院）；Psychiatric Services of the Canton of Grisons（格劳宾登州精神病学服务中心）
Can "AI" Be a Doctor? A Study of Empathy, Readability, and Alignment in Clinical LLMs
Department of Electrical Engineering and Information Technology（电子工程与信息科技系）；University of Naples Federico II（那不勒斯费德里科二世大学）；Department of Translational Medical Sciences（转化医学科学系）；University of Campania ”Luigi Vanvitelli”（坎帕尼亚“路易吉·范维蒂利”大学）；Department of Computer Science, McCormick School of Engineering and Applied Science（计算机科学系，麦科马克工程与应用科学学院）；Northwestern University（西北大学）
Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）；University of Bristol（布里斯托尔大学）；University of Warwick（华威大学）；Emotix Inc（Emotix公司）
A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agents
Institute of Biomedical Ethics and History of Medicine, University of Zürich（苏黎世大学生物医学伦理与医学史研究所）；Dalle Molle Institute for Artificial Intelligence (IDSIA), SUPSI（瑞士SUPSI人工智能研究所）；ETH Zürich（苏黎世联邦理工学院）；Institute for Implementation Science in Health Care, University of Zürich（苏黎世大学医疗实施科学研究所）；Department of Management, Technology and Economics, ETH Zürich（苏黎世联邦理工学院管理、技术与经济系）；Dipartimento Tecnologie Innovative, SUPSI（SUPSI创新技术系）；Management in Networked and Digital Societies (MINDS) Department, Kozminski University（科兹明斯基大学网络化与数字化社会管理系）
A Human-Robot Mutual Learning System with Affect-Grounded Language Acquisition and Differential Outcomes Training
University of Cambridge（剑桥大学）；University of Gothenburg（哥德堡大学）；CY Cergy Paris University（CY塞尔吉-巴黎大学）；ENSEA（国立高等电子及应用学院）；CNRS（法国国家科学研究中心）；Vrije Universiteit Brussel（布鲁塞尔自由大学）
Empirical evidence of Large Language Model's influence on human spoken communication
Center for Humans and Machines（人类与机器中心）；Max-Planck Institute for Human Development（人类发展马克斯·普朗克研究所）；Center for Adaptive Rationality（适应性理性中心）；TUD Dresden University of Technology（德累斯顿技术大学）；Department of Business Analytics and Decision Science（商业分析与决策科学系）；Vienna University of Economics and Business（维也纳经济与商业大学）
