Abstract
Predictive coding is the theory that each level of the nervous system constructs a model of its inputs and then tests new inputs against the model. If the new inputs match the model, then no change in the model is required. If not, then extra brain processing is required to update the model. We tested the precision of such a model in the auditory system by using Shepard tones arranged into a discrete Shepard scale—a series of notes, each comprising sine tones of different amplitudes and one octave apart, and with each tone separated from the next by one semitone, yielding a scale that ascends or descends forever. We unpredictably and occasionally replaced an expected tone in the scale by one that was two thirds of a semitone less, one third of a semitone less, one third of a semitone more, or two thirds of a semitone more. We measured the electrical activity of 20 participants’ brains with 128 scalp electrodes (electroencephalography, EEG) while the tones were delivered to their ears. We found that event-related potentials (ERPs) from 180-220 ms to these unpredictable tones were more negative the farther they were from the predicted note and more negative for tones that were less than the expected note. We conclude that the predictive model for the kind of regularity in a discrete Shepard scale has a sensitivity to tones less than one third of a semitone and is more sensitive to undershoots than to overshoots.
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How precise is predictive coding for things we hear? Mismatch negativity with Shepard tones | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 October 2025 V1 Latest version Share on How precise is predictive coding for things we hear? Mismatch negativity with Shepard tones Authors : Urte Roeber 0000-0002-6631-5828 [email protected] and Robert O'Shea Authors Info & Affiliations https://doi.org/10.22541/au.175971796.67172303/v1 237 views 133 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Predictive coding is the theory that each level of the nervous system constructs a model of its inputs and then tests new inputs against the model. If the new inputs match the model, then no change in the model is required. If not, then extra brain processing is required to update the model. We tested the precision of such a model in the auditory system by using Shepard tones arranged into a discrete Shepard scale—a series of notes, each comprising sine tones of different amplitudes and one octave apart, and with each tone separated from the next by one semitone, yielding a scale that ascends or descends forever. We unpredictably and occasionally replaced an expected tone in the scale by one that was two thirds of a semitone less, one third of a semitone less, one third of a semitone more, or two thirds of a semitone more. We measured the electrical activity of 20 participants’ brains with 128 scalp electrodes (electroencephalography, EEG) while the tones were delivered to their ears. We found that event-related potentials (ERPs) from 180-220 ms to these unpredictable tones were more negative the farther they were from the predicted note and more negative for tones that were less than the expected note. We conclude that the predictive model for the kind of regularity in a discrete Shepard scale has a sensitivity to tones less than one third of a semitone and is more sensitive to undershoots than to overshoots. Supplementary Material File (mmmshepard_final.pdf) Download 4.76 MB Information & Authors Information Version history V1 Version 1 06 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords audition event-related potentials (erp) mismatch negativity (mmn) predictive coding shepard tones Authors Affiliations Urte Roeber 0000-0002-6631-5828 [email protected] Leipzig University View all articles by this author Robert O'Shea Leipzig University View all articles by this author Metrics & Citations Metrics Article Usage 237 views 133 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Urte Roeber, Robert O'Shea. How precise is predictive coding for things we hear? Mismatch negativity with Shepard tones. 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