A major outstanding challenge in the biological and mind sciences is understanding how intelligent behaviors emerge from coupling between brains, physiologies, and the biotic and abiotic environment. At Agora, we specialize in building "brain-body-environment" (BBE) models that study behavior in this context. We specifically aim to build models that identify and test the robustness of emergent mechanisms relevant to search, decision-making, social interaction, and other cognitive processes such as learning.
BBE models have two major advantages as a scientific tool. First, BBE models excel at generating mechanistic hypotheses for how a certain intelligent behavior is realized and how it may change when properties of the control system, body, and/or environment change. Because BBE models preserve the mutual constraints among nervous system, body, and environment, they can reveal candidate mechanisms for behavior that would be difficult to identify from any one level. BBE models thus also can challenge overly narrow theoretical assumptions and clarify what empirical data would be most informative to collect to narrow between candidate solutions. Second, BBE models encourage the proliferation of mathematical tools that frequently transcend the specific model they were created for. BBE models can therefore contribute to theory of intelligent behavior more broadly by expanding the toolkits of researchers across disciplines.
Investigators
Eden Forbes, University of Vermont (ejforbes@agorabiology.com)
Gabriel J. Severino, Indiana University Bloomington (gjseverino@agorabiology.com)
Collaborators
Allegra Love, University of Guelph
Randall Beer, Indiana University Bloomington
Relevant Publications
Severino, G. J., & Forbes, E. J. (in preparation). Integrative Biology using Brain-Body-Environment Models.
Forbes, E. J., & Love, A. (in preparation). Measuring Emergent Risk-Reward Trade-offs in a Model Ecology of Disgust.
Severino, G. J., Winkler, S. L., Beer, R. D., & Barwich, A.-S. (2026). Social contingency in embodied neural networks relies on co-constructed dynamical mechanisms. Philosophical Transactions of the Royal Society B: Biological Sciences, 381(1943), 20250098. https://doi.org/10.1098/rstb.2025.0098
Forbes, E. J., Todd, P. M., & Beer, R. D. (2025). The role of signal in saltatory pursuit of cryptic stationary targets. Movement Ecology, 13(1), 89. https://doi.org/10.1186/s40462-025-00611-z
Forbes, E., & Beer, R. (2024). Minimal modeling for cognitive ecologists: measuring decision-making trade-offs in ecological tasks. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46). https://escholarship.org/uc/item/2dn9c12n
Forbes, E., & Beer, R. (2024). Deriving Community Models with Evolutionary Robotics: A Case Study of Sensory Pollution. Artificial Life Conference Proceedings 36, 2024(1), 22. https://doi.org/10.1162/isal_a_00738