Natural Decision Making
Minerva Fast Track Group
Our group aims to uncover computational, algorithmic, and neural mechanisms underlying adaptive decision-making in naturalistic settings.
Natural environments change over a broad range of timescales, driving continuous fluctuations in vital resources, such as food, water, and shelter. To survive, animals must track these dynamics and adapt their decisions accordingly. This adaptivity has emerged through evolution, development, and learning, yielding diverse solutions tailored to distinct ecological niches. By mapping the shared and species-specific solutions along the evolutionary spectrum, our goal is to identify the computational, biological, and ecological principles governing adaptive decision-making.
We focus specifically on foraging behavior as a natural decision process shared among species. During foraging, animals must constantly decide whether to exploit a currently depleting resource or leave to explore for alternatives. This exploration-exploitation tradeoff lies at the core of many ethologically relevant decisions that animals face daily, such as searching for food, water, mates, or shelter. By integrating computational models and analyses of data from theory-driven experiments across different species, we aim to uncover (1) what decision strategies different animals use during foraging, (2) how these multi-timescale decisions arise from underlying neural circuits, and (3) relate to the spatiotemporal structure of their unique ecological niche, as well as their sensory-motor constraints.
We conduct systematic, cross-species studies of foraging by developing data-driven, species-specific computational models. Our approach combines methods from reinforcement learning, dynamical systems, and AI to identify computational, algorithmic, and neural mechanisms underlying adaptive decision-making. Working in close collaboration with various experimental labs, we also design theory-driven, comparable foraging tasks across different species, such as C. elegans, zebrafish, and mice, complemented by targeted human experiments conducted in our group. This cross-species, interdisciplinary approach allows us to gain a comprehensive understanding of mechanisms shaping adaptive decisions, while highlighting algorithmic and structural inductive biases that can enhance the adaptivity of AI systems.