- Title
- Relevance to the higher order structure may govern auditory statistical learning in neonates
- Creator
- Todd, Juanita; Háden, Gábor P.; Winkler, István
- Relation
- ARC.DP200102346 http://purl.org/au-research/grants/arc/DP200102346
- Relation
- Scientific Reports Vol. 12, Issue 1, no. 5905
- Publisher Link
- http://dx.doi.org/10.1038/s41598-022-09994-0
- Publisher
- Nature Publishing Group
- Resource Type
- journal article
- Date
- 2022
- Description
- Hearing is one of the earliest senses to develop and is quite mature by birth. Contemporary theories assume that regularities in sound are exploited by the brain to create internal models of the environment. Through statistical learning, internal models extrapolate from patterns to predictions about subsequent experience. In adults, altered brain responses to sound enable us to infer the existence and properties of these models. In this study, brain potentials were used to determine whether newborns exhibit context-dependent modulations of a brain response that can be used to infer the existence and properties of internal models. Results are indicative of significant context-dependence in the responsivity to sound in newborns. When common and rare sounds continue in stable probabilities over a very long period, neonates respond to all sounds equivalently (no differentiation). However, when the same common and rare sounds at the same probabilities alternate over time, the neonate responses show clear differentiations. The context-dependence is consistent with the possibility that the neonate brain produces more precise internal models that discriminate between contexts when there is an emergent structure to be discovered but appears to adopt broader models when discrimination delivers little or no additional information about the environment.
- Subject
- hearing; auditory perceptions; humans; acoustic stimulation; newborns; adults
- Identifier
- http://hdl.handle.net/1959.13/1485868
- Identifier
- uon:51712
- Identifier
- ISSN:2045-2322
- Rights
- © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Language
- eng
- Full Text
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