Centaur: a foundation model of human cognition

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, Centaur is the first real candidate for a unified model of human cognition. We anticipate that it will have a disruptive impact on the cognitive sciences, challenging the existing paradigm for developing computational models.

Toward a Wearable Biosensor Ecosystem on ROS 2 for Real-time Human-Robot Interaction Systems

Wearable biosensors can enable continuous human data capture, facilitating development of real-world Human-Robot Interaction (HRI) systems. However, a lack of standardized libraries and implementations adds extraneous complexity to HRI system designs, and precludes collaboration across disciplines and institutions. Here, we introduce a novel wearable biosensor package for the Robot Operating System 2 (ROS 2) system. The ROS2 officially supports real-time computing and multi-robot systems, and thus provides easy-to-use and reliable streaming data from multiple nodes. The package standardizes biosensor HRI integration, lowers the technical barrier of entry, and expands the biosensor ecosystem into the robotics field. Each biosensor package node follows a generalized node and topic structure concentrated on ease of use. Current package capabilities, listed by biosensor, highlight package standardization. Collected example data demonstrate a full integration of each biosensor into ROS2. We expect that standardization of this biosensors package for ROS2 will greatly simplify use and cross-collaboration across many disciplines. The wearable biosensor package is made publicly available on GitHub at \https://github.com/SMARTlab-Purdue/ros2-foxy-wearable-biosensors.