Learning to Prognostically Forage in a Neural Network Model of the Interactions between Neuromodulators and Prefrontal Cortex

Neuromodulatory systems and prefrontal cortex are involved in a number of decision-making contexts. In this work, we adapt a recent neural network model that simulates interactions between neuromodulatory and prefrontal areas to the problem of prognostic foraging—that is choosing information to update or form a hypothesis. In the context of a simulated geospatial intelligence task, the model assesses a number of decision variables and strategies to choose actions that maximize information utility to more accurately predict the actions of an adversary. The model is also capable of modeling biases in decision making such as deviations from the optimal solution of maximizing information gain. Comparisons to other approaches and problem domains in information foraging are also discussed.