Lisa Bonheme

Papers

Total Citations

H-Index

About

Lisa Bonheme is a rising researcher at the forefront of computational cognitive science, with a primary focus on **active inference**—a unified theory of perception, learning, and decision-making that bridges neuroscience, robotics, and machine learning. Her major contribution lies in critically deconstructing and scaling up active inference frameworks through the integration of **Monte Carlo tree search** and **deep learning**. In her highly cited works (2023–2024), Bonheme adopts a contrarian perspective, questioning established assumptions to refine how artificial agents gather information and act under uncertainty. Her papers, each garnering early citations, have quickly become essential reading for researchers working to bridge Bayesian brain theories with practical AI systems. By challenging conventional approaches, Bonheme is helping to shape a more robust, scalable active inference paradigm—one that promises to advance both our understanding of biological intelligence and the development of more adaptive artificial agents. Her work is particularly notable for its clarity in exposing hidden assumptions, making complex theoretical ideas accessible to a broader interdisciplinary audience.