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Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy 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 (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.

We tested the popular, unproven theory that tinnitus is caused by resetting of auditory predictions toward a persistent low-intensity sound. Electroencephalographic mismatch negativity responses, which quantify the violation of sensory predictions, to unattended tinnitus-like sounds were greater in response to upward than downward intensity deviants in 26 unselected chronic tinnitus subjects with normal to severely impaired hearing, and in 15 acute tinnitus subjects, but not in 26 hearing and age-matched controls (p < 0.001, receiver operator characteristic, area under the curve, 0.77), or in 20 healthy and hearing-impaired controls presented with simulated tinnitus. The findings support a prediction resetting model of tinnitus generation, and may form the basis of a convenient tinnitus biomarker, which we name Intensity Mismatch Asymmetry, which is usable across species, is quick and tolerable, and requires no training. [ABSTRACT FROM AUTHOR]

• Perception is an inference about the causes of activity in sensory pathways. • Hallucinations can occur when this activity is not caused by environmental stimuli. • Tinnitus is well explained under a predictive coding framework. • Predictive coding offers a bridge between several different brain (dys)functions. More than 150 years have passed since Helmholtz first described perception as a process of unconscious inference about the causes of sensations. His ideas have since inspired a wealth of literature investigating the mechanisms underlying these inferences. In recent years, much of this work has converged on the notion that the brain is a hierarchical generative model of its environment that predicts sensations and updates itself based on prediction errors. Here, we build a case for modeling tinnitus from this perspective, i.e. predictive coding. We emphasize two key claims: (1) acute tinnitus reflects an increase in sensory precision in related frequency channels and (2) chronic tinnitus reflects a change in the brain's default prediction. We further discuss specific neural biomarkers that would constitute evidence for or against these claims. Finally, we explore the implications of our model for clinical intervention strategies. We conclude that predictive coding offers the basis for a unifying theory of cognitive neuroscience, which we demonstrate with several examples linking tinnitus to other lines of brain research. [ABSTRACT FROM AUTHOR]

Humans use predictions to improve speech perception, especially in noisy environments. Here we use 7-T functional MRI (fMRI) to decode brain representations of written phonological predictions and degraded speech signals in healthy humans and people with selective frontal neurodegeneration (non-fluent variant primary progressive aphasia [nfvPPA]). Multivariate analyses of item-specific patterns of neural activation indicate dissimilar representations of verified and violated predictions in left inferior frontal gyrus, suggestive of processing by distinct neural populations. In contrast, precentral gyrus represents a combination of phonological information and weighted prediction error. In the presence of intact temporal cortex, frontal neurodegeneration results in inflexible predictions. This manifests neurally as a failure to suppress incorrect predictions in anterior superior temporal gyrus and reduced stability of phonological representations in precentral gyrus. We propose a tripartite speech perception network in which inferior frontal gyrus supports prediction reconciliation in echoic memory, and precentral gyrus invokes a motor model to instantiate and refine perceptual predictions for speech. [Display omitted] • Left precentral gyrus represents spoken-word phonology and weighted prediction error • Left inferior frontal gyrus represents verified and violated predictions independently • Frontal neurodegeneration impairs integration of speech predictions in anterior STG • A tripartite speech perception network using a predictive motor model is proposed Cope et al. use multivariate 7-T fMRI in patients with language impairment from selective frontal neurodegeneration (nfvPPA) to show that precentral gyrus instantiates and refines perceptual speech predictions, while inferior frontal gyrus contains distinct representations of verified and violated predictions that support their reconciliation with sensory input in anterior STG. [ABSTRACT FROM AUTHOR]