Professor James Marshall

简介

I am an interdisciplinary researcher interested in behaviour, its mechanistic bases, and its evolution. With various colleagues I apply a variety of mathematical and computational approaches to understanding how and why behaviours evolve.I am also interested in what engineering disciplines may learn from this study. Particular research interests include collective social insect behaviour, the evolution of social behaviour, and the application of decision theory to animal behaviour. Some of my research makes use of robotics to test these ideas.

代表成果

• Marshall JAR (2015) Social Evolution and Inclusive Fitness Theory: An Introduction. Princeton University Press.
• MaBouDi H, Roper M, Guiraud M-G, Juusola M, Chittka L & Marshall JAR (2025) A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees. eLife, 14. View this article in WRRO
• Marshall JAR & Barron AB (2025) Are transformers truly foundational for robotics?. npj Robotics, 3. View this article in WRRO
• MaBouDi H, Richter J, Guiraud M-G, Roper M, Marshall JAR & Chittka L (2025) Active vision of bees in a simple pattern discrimination task. eLife, 14. View this article in WRRO
• Marshall JAR & Reina A (2024) On aims and methods of collective animal behaviour. Animal Behaviour, 210, 189-197.
• MaBouDi H, Marshall JAR, Dearden N & Barron AB (2023) How honey bees make fast and accurate decisions. eLife, 12. View this article in WRRO
• Reina A, Bose T, Srivastava V & Marshall JAR (2023) Asynchrony rescues statistically optimal group decisions from information cascades through emergent leaders. Royal Society Open Science, 10(3).
• Zenil H, Marshall JAR & Tegnér J (2023) Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results. Frontiers in Computational Neuroscience, 16.
• Marshall JAR, Reina A, Hay C, Dussutour A & Pirrone A (2022) Magnitude-sensitive reaction times reveal non-linear time costs in multi-alternative decision-making. PLoS Computational Biology, 18(10).
• de Croon GCHE, Dupeyroux JJG, Fuller SB & Marshall JAR (2022) Insect-inspired AI for autonomous robots. Science Robotics, 7(67).