Predictive coding and stochastic resonance as fundamental principles of auditory perception
This work addresses the mechanistic understanding of auditory perception for researchers in neuroscience and AI, but it is incremental as it reviews and synthesizes existing ideas without introducing new methods or data.
The paper tackles the problem of understanding auditory perception by examining tinnitus as a model, arguing that predictive coding and stochastic resonance are fundamental principles that explain why hearing loss does not always lead to tinnitus, with no concrete numerical results provided.
How is information processed in the brain during perception? Mechanistic insight is achieved only when experiments are employed to test formal or computational models. In analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying auditory perception. With a special focus on tinnitus -- as the prime example of auditory phantom perception -- we review recent work at the intersection of artificial intelligence, psychology, and neuroscience. In particular, we discuss why everyone with tinnitus suffers from hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that the increase of sensory precision due to Bayesian inference could be caused by intrinsic neural noise and lead to a prediction error in the cerebral cortex. Hence, two fundamental processing principles - being ubiquitous in the brain - provide the most explanatory power for the emergence of tinnitus: predictive coding as a top-down, and stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles play a crucial role in healthy auditory perception.