One of our recent works, Emotion as an emergent phenomenon of the neuro-computational energy regulation mechanism of a cognitive agent in a decision-making task, is now available in the Journal of Adaptive Behavior. In this work, we hypothesized that the high-level emotions (e.g., boredom and wellbeing) could emerge within the agent's neural system as a result of the neurocomputational energy regulation mechanism. Here is the abstract of the paper.

Biological agents need to complete perception-action cycles to perform various cognitive and biological tasks such as maximizing their well-being and their chances of genetic continuation. However, the processes performed in these cycles come at a cost. Such cost forces the agent to evaluate a trade-off between the optimality of the decision making and the time and computational effort required to make it. Several cognitive mechanisms that play critical roles in managing this trade-off have been identified. Among these mechanisms, there are: adaptation, learning, memory, attention, and planning. One of the often overlooked outcomes of these cognitive mechanisms, in spite of the critical effect that it may have on the perception-action cycle of organisms, is `emotion.' In this study, we hold that emotion can be considered as an emergent phenomenon of a plausible neuro-computational energy regulation mechanism, which generates an internal reward signal to minimize the neural energy consumption of a sequence of actions (decisions), where each action triggers a visual memory recall process. To realize an optimal action selection over a sequence of actions in a visual recalling task, we adopted a model-free reinforcement learning framework, in which the reward signal -- i.e., the cost-- was based on the iterations steps of the convergence state of an associative memory network. The proposed mechanism has been implemented in simulation and on a robotic platform: the iCub humanoid robot. The results show that the computational energy regulation mechanism enables the agent to modulate its behavior to minimize the required neuro-computational energy in performing the visual recalling task.