Statistical learning effects in musicians and non-musicians: An MEG study

Paraskevopoulos, Evangelos; Kuchenbuch, Anja; Herholz, Sibylle C.; Pantev, Christo

doi:10.1016/j.neuropsychologia.2011.12.007

Cited by 66 publications

(94 citation statements)

References 58 publications

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“…In addition, these statistical learning effects reflected in neural responses could be observed in both the ERPs and the magnetic counterparts of ERPs. This neural effect could be observed in the responses at approximately 50 ms (i.e., P1/P1m) (Paraskevopoulos et al, 2012;Daikoku et al, 2016), 100 ms (i.e., N1/N1m) (Abla et al, 2008;Furl et al, 2011;Daikoku et al, 2014Daikoku et al, , 2015Koelsch et al, 2016), and 200 ms (i.e., P2/P2m) (Furl et al, 2011) after stimulus onset.…”

Section: Neurophysiological Markers Of Statistical Learningmentioning

confidence: 90%

“…Previous studies on auditory statistical learning, using a secondorder Markov chain (Markov, 1971(Markov, , reprinted in 1971Furl et al, 2011;Daikoku et al, 2014Daikoku et al, , 2015 or a word segmentation task (Abla et al, 2008;Paraskevopoulos et al, 2012), have shown that auditory statistical learning can be reflected in neural responses. Previous studies using two different experimental paradigms have shown similar results: neural responses to tones that appear frequently in reference to the most recent tones are more rapidly reduced than responses to tones that appear infrequently.…”

Section: Neurophysiological Markers Of Statistical Learningmentioning

confidence: 99%

“…Previous neurophysiological studies have investigated only a single aspect of language learning at a time, such as the acquisition of words (Abla et al, 2008;Paraskevopoulos et al, 2012) or grammatical structure (Furl et al, 2011;Daikoku et al, 2014Daikoku et al, , 2015. In our real-world learning environments, when humans listen to language sequences, they are exposed to the linguistic traits of word boundary and word order patterns.…”

Section: The Purpose Of the Present Studymentioning

confidence: 99%

“…According to previous studies on statistical learning that used a Markov stochastic model or a word segmentation task, tones that appear with higher transitional probability (i.e., more-predictable tones) lead to reduced amplitude and shortened neural response latencies, whereas tones that appear with lower transitional probability (i.e., less-predictable tones) preserve the neural response amplitude (Abla et al, 2008;Furl et al, 2011;Paraskevopoulos et al, 2012;Daikoku et al, 2014Daikoku et al, , 2015. In the present study, using both the Markov stochastic model and a word segmentation task, we hypothesized that the amplitudes for the final tone within words would be significantly reduced compared to those for the initial tone, after learning word borders (i.e., word segmentation), and that the amplitudes for words that appeared with higher transitional probability would be significantly reduced compared with those with lower transitional probability.…”

Section: The Purpose Of the Present Studymentioning

confidence: 99%

“…When learners perform statistical learning of tone sequences, they can predict a tone that will follow certain preceding tones (Abla et al, 2008;Furl et al, 2011;Paraskevopoulos et al, 2012;Daikoku et al, 2014Daikoku et al, , 2015. With this prediction, tones that appear with higher transitional probability (i.e., more-predictable tones) lead to a reduction in amplitude and an earlier latency of neural responses.…”

Section: Event-related Responses As An Index Of Statistical Learningmentioning

confidence: 99%

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Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

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Section: Neurophysiological Markers Of Statistical Learningmentioning

confidence: 90%

Section: Neurophysiological Markers Of Statistical Learningmentioning

confidence: 99%

Section: The Purpose Of the Present Studymentioning

confidence: 99%

Section: The Purpose Of the Present Studymentioning

confidence: 99%

Section: Event-related Responses As An Index Of Statistical Learningmentioning

confidence: 99%

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Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

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Encoding of nested levels of acoustic regularity in hierarchically organized areas of the human auditory cortex

Recasens

Grimm

Wollbrink

et al. 2014

Human Brain Mapping

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Our auditory system is able to encode acoustic regularity of growing levels of complexity to model and predict incoming events. Recent evidence suggests that early indices of deviance detection in the time range of the middle-latency responses (MLR) precede the mismatch negativity (MMN), a well-established error response associated with deviance detection. While studies suggest that only the MMN, but not early deviance-related MLR, underlie complex regularity levels, it is not clear whether these two mechanisms interplay during scene analysis by encoding nested levels of acoustic regularity, and whether neuronal sources underlying local and global deviations are hierarchically organized. We registered magnetoencephalographic evoked fields to rapidly presented four-tone local sequences containing a frequency change. Temporally integrated local events, in turn, defined global regularities, which were infrequently violated by a tone repetition. A global magnetic mismatch negativity (MMNm) was obtained at 140-220 ms when breaking the global regularity, but no deviance-related effects were shown in early latencies. Conversely, Nbm (45-55 ms) and Pbm (60-75 ms) deflections of the MLR, and an earlier MMNm response at 120-160 ms, responded to local violations. Distinct neuronal generators in the auditory cortex underlay the processing of local and global regularity violations, suggesting that nested levels of complexity of auditory object representations are represented in separated cortical areas. Our results suggest that the different processing stages and anatomical areas involved in the encoding of auditory representations, and the subsequent detection of its violations, are hierarchically organized in the human auditory cortex.

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MEG Studies on Music

Herholz¹,

Pantev²

2019

Magnetoencephalography

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Statistical learning effects in musicians and non-musicians: An MEG study

Cited by 66 publications

References 58 publications

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

Encoding of nested levels of acoustic regularity in hierarchically organized areas of the human auditory cortex

MEG Studies on Music

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