Emergence of sensory attenuation based upon the free-energy principle
This addresses the problem of understanding sensory attenuation mechanisms in neuroscience, but it is incremental as it builds on existing free-energy principles in a simulation context.
The study tackled how sensory attenuation (reduced brain response to self-generated sensations) emerges, showing through simulations that a hierarchical neural network based on free-energy minimization can learn distinct states for self-produced vs. externally produced sensations, leading to shifts between attenuating and amplifying sensory responses.
The brain attenuates its responses to self-produced exteroceptions (e.g., we cannot tickle ourselves). Is this phenomenon, known as sensory attenuation, enabled innately, or acquired through learning? Here, our simulation study using a multimodal hierarchical recurrent neural network model, based on variational free-energy minimization, shows that a mechanism for sensory attenuation can develop through learning of two distinct types of sensorimotor experience, involving self-produced or externally produced exteroceptions. For each sensorimotor context, a particular free-energy state emerged through interaction between top-down prediction with precision and bottom-up sensory prediction error from each sensory area. The executive area in the network served as an information hub. Consequently, shifts between the two sensorimotor contexts triggered transitions from one free-energy state to another in the network via executive control, which caused shifts between attenuating and amplifying prediction-error-induced responses in the sensory areas. This study situates emergence of sensory attenuation (or self-other distinction) in development of distinct free-energy states in the dynamic hierarchical neural system.