TY - JOUR T1 - Many paths to the same goal: balancing exploration and exploitation during probabilistic route planning JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0536-19.2020 SP - ENEURO.0536-19.2020 AU - Brian J. Jackson AU - Gusti Lulu Fatima AU - Sujean Oh AU - David H. Gire Y1 - 2020/05/15 UR - http://www.eneuro.org/content/early/2020/05/15/ENEURO.0536-19.2020.abstract N2 - During self-guided behaviors animals identify constraints of the problems they face and adaptively employ appropriate strategies (Marsh, 2002). In the case of foraging, animals must balance sensory-guided exploration of an environment with memory- guided exploitation of known resource locations. Here we show that animals adaptively shift cognitive resources between sensory and memory systems during foraging to optimize route planning under uncertainty. We demonstrate this using a new, laboratory-based discovery method to define the strategies used to solve a difficult route optimization scenario, the probabilistic “traveling salesman” problem (Anaya Fuentes et al., 2018; Mukherjee et al., 2019; Raman & Gill, 2017). Using this system, we precisely manipulated the strength of prior information as well as the complexity of the problem. We find that rats are capable of efficiently solving this route-planning problem, even under conditions with unreliable prior information and a large space of possible solutions. Through analysis of animals’ trajectories, we show that they shift the balance between exploiting known locations and searching for new locations of rewards based upon the predictability of reward locations. When compared to a Bayesian search, we found that animal performance is consistent with an approach that adaptively allocates cognitive resources between sensory processing and memory, enhancing sensory acuity and reducing memory load under conditions in which prior information is unreliable. Our findings establish new approaches to understand neural substrates of natural behavior as well as the rational development of biologically inspired approaches for complex real-world optimizationSignificance Statement Animals display remarkable problem-solving abilities across a variety of complex situations. Here, we used a large, computer-controlled foraging field with precisely controlled probabilities of food resources in either repeated or random locations to test how rats determine which strategies to use to solve an extremely complicated route planning problem. We found that rats balanced exploration for novel locations of food with exploitation of known food locations to solve this problem, with the balance between exploratory and exploitative strategies governed by the amount of information available regarding resource location. Our results show how animals balance sensory input with learned information to solve complex, real-world route planning problems. ER -