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Despite the remarkable progress in the field of artificial intelligence (AI), current models seldom incorporate emotions or affective states, constraining their capacity for emulating truly adaptive and human-like behavior. Boredom is a recurrent affective state that signals a mismatch between available cognitive resources and environmental demands, prompting a state-escape response to restore optimal engagement. Cast within a functional psychological framework, boredom is characterized to arise when the level of cognitive engagement falls outside an optimal range, whether due to under- or overstimulation. Here, we translate this principle into a biologically inspired control loop implemented with spiking neural networks. The model continuously monitors simulated cognitive resource utilization, signals deviations that occur due to perturbations in the input and dynamically influences the utilization of resources to maintain an optimal engagement level. Simulations demonstrate that the model effectively maintains stable cognitive engagement by exciting and inhibiting a spiking neuron population that abstractly represents the processing of input. This work establishes a foundation towards the development of a future model capable of autonomously defining and regulating its optimal level of cognitive engagement. By embedding such affective regulation directly into a spiking architecture, our approach bridges cognitive neuroscience and AI, offering insights into how human-like state monitoring and initiating of corrective actions can be realized within brain-inspired AI. • Modeling boredom as a homeostatic control loop with spiking neural networks. • Successful replication of the boredom regulation mechanism that maintains optimal cognitive resource utilization. • Novel approach for replicating human experiences and affective states in an artificial intelligence model.
Schöfer et al. (Sat,) studied this question.