The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
One year of musical training affects development of auditory cortical-evoked fields in young children
Open accessAbstract
Auditory evoked responses to a violin tone and a noise-burst stimulus were recorded from 4- to 6-year-old children in four repeated measurements over a 1-year period using magnetoencephalography (MEG). Half of the subjects participated in musical lessons throughout the year; the other half had no music lessons. Auditory evoked magnetic fields showed prominent bilateral P100m, N250m, P320m and N450m peaks. Significant change in the peak latencies of all components except P100m was observed over time. Larger P100m and N450m amplitude as well as more rapid change of N250m amplitude and latency was associated with the violin rather than the noise stimuli. Larger P100m and P320m peak amplitudes in the left hemisphere than in the right are consistent with left-lateralized cortical development in this age group. A clear musical training effect was expressed in a larger and earlier N250m peak in the left hemisphere in response to the violin sound in musically trained children compared with untrained children. This difference coincided with pronounced morphological change in a time window between 100 and 400 ms, which was observed in musically trained children in response to violin stimuli only, whereas in untrained children a similar change was present regardless of stimulus type. This transition could be related to establishing a neural network associated with sound categorization and/or involuntary attention, which can be altered by music learning experience.
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Cites
The role of deliberate practice in the acquisition of expert performance
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993) · Psychological Review · doi:10.1037/0033-295x.100.3.363 · Cited by 9,564 · cited 1×
“Becoming a successful musician often takes more than a decade of training commencing at an early age and includes regular practice for several hours daily (Ericsson et al., 1993; Sloboda and Davidson, 1996).”
BackgroundStructural maturation of neural pathways in children and adolescents: in vivo study
Paus, T., Zijdenbos, A., Worsley, K., Collins, D. L., Blumenthal, J., Giedd, J. N., Rapoport, J. L., & Evans, A. C. (1999) · Science · doi:10.1126/science.283.5409.1908 · Cited by 1,322
Recanzone, G., Schreiner, C., & Merzenich, M. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00087.1993 · Cited by 1,266
Anvari, S. H., Trainor, L. J., Woodside, J., & Levy, B. A. (2002) · Journal of Experimental Child Psychology · doi:10.1016/s0022-0965(02)00124-8 · Cited by 670
Cited by
Short-Term Music Training Enhances Verbal Intelligence and Executive Function
Moreno, S., Bialystok, E., Barac, R., Schellenberg, E. G., Cepeda, N. J., & Chau, T. (2011) · Psychological Science · doi:10.1177/0956797611416999 · Cited by 803
Music acquisition: Effects of enculturation and formal training on development
Hannon, E. E., & Trainor, L. J. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.08.008 · Cited by 493
The effects of musical training on verbal memory
Franklin, M. S., Sledge Moore, K., Yip, C.-Y., Jonides, J., Rattray, K., & Moher, J. (2008) · Psychology of Music · doi:10.1177/0305735607086044 · Cited by 240
The Musical Ear Test, a new reliable test for measuring musical competence
Wallentin, M., Nielsen, A. H., Friis-Olivarius, M., Vuust, C., & Vuust, P. (2010) · Learning and Individual Differences · doi:10.1016/j.lindif.2010.02.004 · Cited by 225
Nan, Y., Liu, L., Geiser, E., Shu, H., Gong, C. C., Dong, Q., Gabrieli, J. D. E., & Desimone, R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1808412115 · Cited by 133
Metadata
- Authors
- Fujioka, T.
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-07-15
- Volume
- 129
- Issue
- 10
- Pages
- 2593-2608
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2158619533
- Cited by
- 349
- References
- 4
- FWCI
- 6.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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