Implementing Goal-Directed Foraging Decisions of a Simpler Nervous System in Simulation

Economic decisions arise from evaluation of alternative actions in contexts of motivation and memory. In the predatory sea-slug the economic decisions of foraging are found to occur by the workings of a simple, affectively controlled homeostat with learning abilities. Here, the neuronal circuit relations for approach-avoidance choice of are expressed and tested in the foraging simulation Cyberslug. Choice is organized around appetitive state as a moment-to-moment integration of sensation, motivation (satiation/hunger), and memory. Appetitive state controls a switch for approach vs. avoidance turn responses to sensation. Sensory stimuli are separately integrated for incentive value into appetitive state, and for prey location (stimulus place) into mapping motor response. Learning interacts with satiation to regulate prey choice affectively. The virtual predator realistically reproduces the decisions of the real one in varying circumstances and satisfies optimal foraging criteria. The basic relations are open to experimental embellishment toward enhanced neural and behavioral complexity in simulation, as was the ancestral bilaterian nervous system in evolution.