Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception.
Open accessAbstract
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.
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Most tinnitus patients also experience hearing loss.
“Most tinnitus patients also experience hearing loss (Krauss et al., 2016; Schilling et al., 2023; Sedley et al., 2016)”
From Combining the unequal variance signal detection model with the health belief model to optimize shared decision making in tinnitus patients: part 2—patient profiling (2025) · cites this paper as Schilling et al., 2023
The Introduction (page not provided) describes correlations between tinnitus and hearing loss and discusses tinnitus pitch in the frequency range of hearing loss. It does not say that most tinnitus patients have hearing loss; searches for a majority or prevalence figure did not establish that proportion. (LLM, conf=0.78)
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Cognitive Computational Neuroscience: A New Conference for an Emerging Discipline
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Self-citationNeural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing
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A Cognitive Computational Model of Risk Hypothesis Generation
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On lateral inhibition in the auditory system.
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Parameter-Free Binarization and Skeletonization of Fiber Networks from Confocal Image Stacks
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Hoetink, A. E., Kaldenbach, S., Lieftink, A., Versnel, H., & Stokroos, R. J. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1451741 · Cited by 3 · see citing works
Lehóczky, Z. Z., Smit, A. L., Kaldenbach, S., Lieftink, A., Versnel, H., Stokroos, R. J., & Hoetink, A. E. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1466354
- Authors
- Schilling, A., Sedley, W., Gerum, R., Metzner, C., Tziridis, K., Maier, A., Schulze, H., Zeng, F.-G., Friston, K. J., & Krauss, P.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Newcastle University (GB)
- Universitätsklinikum Erlangen (DE)
- University of California, Irvine (US)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Schilling, Achim
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-07-28
- Volume
- 146
- Issue
- 12
- Pages
- 4809-4825
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC10690027
- OpenAlex
- W4385331364
- Cited by
- 70
- References
- 189
- Citations by year
- 2026: 14 · 2025: 14 · 2024: 29 · 2023: 10 · 2022: 3
- FWCI
- 3.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Brain (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- ERC
- DFG
- Wellcome Centre for Human Neuroimaging
- Canada-UK Artificial Intelligence Initiative
- Emerging Talents Initiative
- University Erlangen-Nürnberg
- Topics
- Life Sciences › Neuroscience › Sensory Systems › Hearing, Cochlea, Tinnitus, Genetics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Health Sciences › Health Professions › Speech and Hearing › Noise Effects and Management
- Keywords
- tinnitus, predictive coding, stochastic resonance, neural noise, Bayesian brain, hidden hearing loss, auditory pathway, top-down and bottom-up processing, prior expectations, neuroplasticity
- MeSH terms
- Artificial Intelligence, Auditory Pathways, Auditory Perception, Bayes Theorem, Humans, Tinnitus, Hearing Loss
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