Organisms make decisions in the face of uncertainty, implying that decision-making in nature requires statistical inference. How do organisms parse noisy data streams? How do they use these data to decide what to do? We model organismal decision-making using statistical inferential frameworks combined with tools from neuroscience and ethology. Our goal is to understand why natural selection has led to the decision-making strategies we see in nature and how such rules operate in natural environments.
Projects in this area:
Microbial navigation and sensory noise (w/Roman Stocker, Simon Levin, Francesco Carrara, and Doug Brumley)
Flexibility and robustness in attack and evasion behavior (w/Ben Martin and Jimmy Liao)
Decision cascades and misinformation in natural systems (w/ Ashkaan Fahimipour)
Papers in this area:
Hein, Altshuler, Cade, Liao, Martin, Taylor. 2020. An algorithmic approach to natural behavior. Current Biology.
Brumley, Carrara, Hein, Yuwata, Levin, Stocker. 2019. Bacteria push the limits of sensory precision to navigate dynamic chemical Gradients. PNAS.
Hein, Gil, Twomey, Couzin, Levin. 2018. Conserved behavioral circuits govern high-speed decision making in wild fish shoals. PNAS.
Hein, Carrara, Brumley, Stocker, Levin. 2016. Natural search algorithms as a bridge between organisms, evolution, and ecology. PNAS.
Hein, Brumley, Carrara, Stocker, Levin. 2016. Physical limits on bacterial navigation in dynamic environments. J. Royal Society Interface.
Park, Hein, Bobkov, Reidenbach, Ache, Principe. 2016. Neurally encoding time in olfactory navigation. PLoS Comp. Biol.
Hein, Rosenthal, Hagstrom, Berdahl, Torney, Couzin. 2015. The evolution of distributed sensing and collective computation in animal populations. eLife.
Hein, McKinley. 2012. Sensing and decision-making in random search. PNAS.