Understanding the neurological implementation of emotions is a major research subject from biology to computer sciences that, in the latter case, takes many shapes: from accurate detection of human emotions to the emulation of plausible responses to stimuli. There is, however, room for a more bottom-up approach in which we would thrive to recreate emotions from undifferentiated elementary building blocks.
In this article, we used virtual creatures that interact with their environment through a low-level perception/cognition/action loop to demonstrate their potential for fear responses. Embedded in a physical environment in a typical prey/predator setting, they develop strategies for foraging while minimizing their exposure to danger.
By monitoring the neural activities of these subjects, we were able to highlight the regularities induced by an ES-HyperNEAT encoding and their eventual mapping into “mental states”. We further emphasize the potential of this approach by clustering these ANNs and showing their resulting complexity in terms of conspecific identification, communication, and functional modularity. Indeed, through functional equivalence across numerous topologies, we identify a fear-related neural cluster that serves as a primitive defensive survival circuit.