Is There an Association between Tinnitus Pitch and Hearing Loss?

Dall’Igna, Celso; Rosito, Letícia S.; Dall'Igna, Daniela Pernigotti; Deutsch, Konrado Massing; Pappen, Carlos Henrique; Sangoi, Ana P. A.; Borges, Felipe M.

doi:10.1177/0194599814541629a239

Cited by 1 publication

(2 citation statements)

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“…Thus, studies on human subjects complement these findings. In several recent studies, it was shown that the tinnitus frequency lies within the range of the hearing loss [14][15][16][17]. Furthermore, Dalligna and coworkers report that the tinnitus is directly centered at the frequency of the largest hearing loss [16] and Keppler and coworkers report that there is no correlation between tinnitus pitch and the edges of the impaired frequency ranges [15].…”

Section: Introductionmentioning

confidence: 99%

“…In several recent studies, it was shown that the tinnitus frequency lies within the range of the hearing loss [14][15][16][17]. Furthermore, Dalligna and coworkers report that the tinnitus is directly centered at the frequency of the largest hearing loss [16] and Keppler and coworkers report that there is no correlation between tinnitus pitch and the edges of the impaired frequency ranges [15]. Nevertheless, there are also some studies reporting a correlation between the edges of the hearing loss and the tinnitus pitch [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Predictive coding and stochastic resonance as fundamental principles of auditory perception

Schilling¹,

Sedley²,

Gerum³

et al. 2022

Preprint

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Cognitive computational neuroscience (CCN) suggests that to gain a mechanistic understanding of brain function, hypothesis driven experiments should be accompanied by biologically plausible computational models. This novel research paradigm offers a way from alchemy to chemistry, in auditory neuroscience. 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, foregrounding the idea that experiments will yield mechanistic insight only when employed to test formal or computational models. This view challenges the popular notion that tinnitus research is primarily data limited, and that producing large, multi-modal, and complex data-sets, analyzed with advanced data analysis algorithms, will lead to fundamental insights into how tinnitus emerges. We conclude that two fundamental processing principles -being ubiquitous in the brain -best fit to a vast number of experimental results and therefore provide the most explanatory power: predictive coding as a top-down, and stochastic resonance as a complementary bottom-up mechanism. Furthermore, we argue that even though contemporary artificial intelligence and machine learning approaches largely lack biological plausibility, the models to be constructed will have to draw on concepts from these fields; since they provide a formal account of the requisite computations that underlie brain function. Nevertheless, biological fidelity will have to be addressed, allowing for testing possible treatment strategies in silico, before application in animal or patient studies. This iteration of computational and empirical studies may help to open the 'black boxes' of both machine learning and the human brain.

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