Causal Perception

Causal perception research investigates how individuals and machines understand and utilize causal relationships to interpret information and make decisions, aiming to bridge the gap between cognitive science and artificial intelligence. Current research focuses on developing computational models, including spiking neural networks and generative diffusion models, that can learn and represent causal structures from data, often addressing challenges like position bias in long dialogues and incorporating human-like attention mechanisms. This work has implications for improving the robustness and explainability of AI systems, particularly in applications involving human-machine interaction and decision-making, as well as offering insights into the cognitive processes underlying human causal reasoning.