Human Mind
Research on the human mind currently focuses on understanding complex cognitive processes like Theory of Mind (ToM), the ability to understand others' mental states, and its implications for human-AI interaction and collaboration. This involves developing and evaluating computational models, often employing large language models (LLMs) and multimodal architectures, to predict and simulate human behavior in various social and collaborative contexts. Key areas of investigation include improving the accuracy and efficiency of these models, particularly in handling uncertainty and noisy data, and exploring the ethical implications of increasingly sophisticated AI systems capable of understanding and responding to human mental states. These advancements have significant implications for improving human-computer interaction, developing more effective assistive technologies, and furthering our understanding of the human mind itself.
Papers
Hearing the Robot's Mind: Sonification for Explicit Feedback in Human-Robot Interaction
Simone Arreghini, Antonio Paolillo, Gabriele Abbate, Alessandro Giusti
Artificial Theory of Mind and Self-Guided Social Organisation
Michael S. Harré, Jaime Ruiz-Serra, Catherine Drysdale
Theory of Mind Enhances Collective Intelligence
Michael S. Harré, Catherine Drysdale, Jaime Ruiz-Serra
Looking Inward: Language Models Can Learn About Themselves by Introspection
Felix J Binder, James Chua, Tomek Korbak, Henry Sleight, John Hughes, Robert Long, Ethan Perez, Miles Turpin, Owain Evans
SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs
Yuling Gu, Oyvind Tafjord, Hyunwoo Kim, Jared Moore, Ronan Le Bras, Peter Clark, Yejin Choi
Probing the Robustness of Theory of Mind in Large Language Models
Christian Nickel, Laura Schrewe, Lucie Flek
Entering Real Social World! Benchmarking the Theory of Mind and Socialization Capabilities of LLMs from a First-person Perspective
Guiyang Hou, Wenqi Zhang, Yongliang Shen, Zeqi Tan, Sihao Shen, Weiming Lu