Controlling human causal inference through in silico task design

• Published in: Cell reports (Cambridge) Vol. 43; no. 2; p. 113702
• Main Authors: Lee, Jee Hang, Heo, Su Yeon, Lee, Sang Wan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 27.02.2024; Elsevier
• Subjects: automatic task design; Bayesian inference; causal inference; cognitive model; computational model; CP: Neuroscience; deep reinforcement learning; Humans; Learning; neural network; Neural Networks, Computer; one-shot inference; task control; cognitive model; CP: Neuroscience; one-shot inference; computational model; task control; automatic task design; deep reinforcement learning; causal inference; Bayesian inference; neural network
• ISSN: 2211-1247; 2211-1247
• DOI: 10.1016/j.celrep.2024.113702

Learning causal relationships is crucial for survival. The human brain’s functional flexibility allows for effective causal inference, underlying various learning processes. While past studies focused on environmental factors influencing causal inference, a fundamental question remains: can these factors be manipulated for strategic causal inference control? This paper presents a task control framework for orchestrating causal learning task design. It utilizes a two-player game setting where a neural network learns to manipulate task variables by interacting with a human causal inference model. Training the task controller to generate experimental designs, we confirm its ability to accommodate complexities of environmental causal structure. Experiments involving 126 human subjects successfully validate the impact of task control on performance and learning efficiency. Additionally, we find that task control policy reflects the intrinsic nature of human causal inference: one-shot learning. This framework holds promising potential for applications paving the way for targeted behavioral outcomes in humans. [Display omitted] •Rapid and highly performative causal inference is the brain’s remarkable ability•Propose the task control framework to guide human’s causal inference process•Task controller fully taps into the brain’s functional flexibility in causal learning•Confirm the behavioral effect of task control on learning performance and efficiency The human brain exhibits remarkable functional flexibility for rapid and highly performative causal learning, known as one-shot learning. Lee et al. present a computational framework that incorporates the brain’s inherent flexibility to enable the learning of task variable manipulation, thereby controlling the human causal inference process in diverse ways.