Adaptive oscillators support Bayesian prediction in temporal processing.
Humans excel at predictively synchronizing their behavior with external rhythms, as in dance or music performance. The neural processes underlying rhythmic inferences are debated: whether predictive perception relies on high-level generative models or whether it can readily be implemented locally by...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011669&type=printable |
| _version_ | 1797377753496944640 |
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| author | Keith B Doelling Luc H Arnal M Florencia Assaneo |
| author_facet | Keith B Doelling Luc H Arnal M Florencia Assaneo |
| author_sort | Keith B Doelling |
| collection | DOAJ |
| description | Humans excel at predictively synchronizing their behavior with external rhythms, as in dance or music performance. The neural processes underlying rhythmic inferences are debated: whether predictive perception relies on high-level generative models or whether it can readily be implemented locally by hard-coded intrinsic oscillators synchronizing to rhythmic input remains unclear and different underlying computational mechanisms have been proposed. Here we explore human perception for tone sequences with some temporal regularity at varying rates, but with considerable variability. Next, using a dynamical systems perspective, we successfully model the participants behavior using an adaptive frequency oscillator which adjusts its spontaneous frequency based on the rate of stimuli. This model better reflects human behavior than a canonical nonlinear oscillator and a predictive ramping model-both widely used for temporal estimation and prediction-and demonstrate that the classical distinction between absolute and relative computational mechanisms can be unified under this framework. In addition, we show that neural oscillators may constitute hard-coded physiological priors-in a Bayesian sense-that reduce temporal uncertainty and facilitate the predictive processing of noisy rhythms. Together, the results show that adaptive oscillators provide an elegant and biologically plausible means to subserve rhythmic inference, reconciling previously incompatible frameworks for temporal inferential processes. |
| first_indexed | 2024-03-08T19:58:02Z |
| format | Article |
| id | doaj.art-54b44bc2adf24d328c4047d2e9459fd8 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-08T19:58:02Z |
| publishDate | 2023-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-54b44bc2adf24d328c4047d2e9459fd82023-12-24T05:31:42ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-11-011911e101166910.1371/journal.pcbi.1011669Adaptive oscillators support Bayesian prediction in temporal processing.Keith B DoellingLuc H ArnalM Florencia AssaneoHumans excel at predictively synchronizing their behavior with external rhythms, as in dance or music performance. The neural processes underlying rhythmic inferences are debated: whether predictive perception relies on high-level generative models or whether it can readily be implemented locally by hard-coded intrinsic oscillators synchronizing to rhythmic input remains unclear and different underlying computational mechanisms have been proposed. Here we explore human perception for tone sequences with some temporal regularity at varying rates, but with considerable variability. Next, using a dynamical systems perspective, we successfully model the participants behavior using an adaptive frequency oscillator which adjusts its spontaneous frequency based on the rate of stimuli. This model better reflects human behavior than a canonical nonlinear oscillator and a predictive ramping model-both widely used for temporal estimation and prediction-and demonstrate that the classical distinction between absolute and relative computational mechanisms can be unified under this framework. In addition, we show that neural oscillators may constitute hard-coded physiological priors-in a Bayesian sense-that reduce temporal uncertainty and facilitate the predictive processing of noisy rhythms. Together, the results show that adaptive oscillators provide an elegant and biologically plausible means to subserve rhythmic inference, reconciling previously incompatible frameworks for temporal inferential processes.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011669&type=printable |
| spellingShingle | Keith B Doelling Luc H Arnal M Florencia Assaneo Adaptive oscillators support Bayesian prediction in temporal processing. PLoS Computational Biology |
| title | Adaptive oscillators support Bayesian prediction in temporal processing. |
| title_full | Adaptive oscillators support Bayesian prediction in temporal processing. |
| title_fullStr | Adaptive oscillators support Bayesian prediction in temporal processing. |
| title_full_unstemmed | Adaptive oscillators support Bayesian prediction in temporal processing. |
| title_short | Adaptive oscillators support Bayesian prediction in temporal processing. |
| title_sort | adaptive oscillators support bayesian prediction in temporal processing |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011669&type=printable |
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