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.
Papers in this area:
Gil, M A and A M Hein. 2017. Social interactions among grazing reef fish drive material flux in a coral reef ecosystem. PNAS
Hein, A M, F Carrara, D. Brumley, R Stocker, and S A Levin. 2016. Natural search algorithms as a bridge between organisms, evolution, and ecology. PNAS
Hein, A M, D. Brumley, F Carrara, R Stocker, and S A Levin. 2016. Physical limits on bacterial navigation in dynamic environments. J. Royal Society Interface.
Park, I, A M Hein, Y V Bobkov, M A Reidenbach, B Ache, and J C Principe. 2016. Neurally encoding time in olfactory navigation. PLoS Comp. Biol.
Hein, A M, S B Rosenthal, G I Hagstrom, A Berdahl, C J Torney, and I D Couzin. 2015. The evolution of distributed sensing and collective computation in animal populations.
Hein, A M and S A McKinley. 2012. Sensing and decision-making in random search. PNAS.