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Musical expertise generalizes to superior temporal scaling in a Morse code tapping task

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  • Matthew A Slayton
  • Juan L Romero-Sosa
  • Katrina Shore
  • Dean V Buonomano
  • Indre V Viskontas

Abstract

A key feature of the brain’s ability to tell time and generate complex temporal patterns is its capacity to produce similar temporal patterns at different speeds. For example, humans can tie a shoe, type, or play an instrument at different speeds or tempi—a phenomenon referred to as temporal scaling. While it is well established that training improves timing precision and accuracy, it is not known whether expertise improves temporal scaling, and if so, whether it generalizes across skill domains. We quantified temporal scaling and timing precision in musicians and non-musicians as they learned to tap a Morse code sequence. We found that non-musicians improved significantly over the course of days of training at the standard speed. In contrast, musicians exhibited a high level of temporal precision on the first day, which did not improve significantly with training. Although there was no significant difference in performance at the end of training at the standard speed, musicians were significantly better at temporal scaling—i.e., at reproducing the learned Morse code pattern at faster and slower speeds. Interestingly, both musicians and non-musicians exhibited a Weber-speed effect, where temporal precision at the same absolute time was higher when producing patterns at the faster speed. These results are the first to establish that the ability to generate the same motor patterns at different speeds improves with extensive training and generalizes to non-musical domains.

Suggested Citation

  • Matthew A Slayton & Juan L Romero-Sosa & Katrina Shore & Dean V Buonomano & Indre V Viskontas, 2020. "Musical expertise generalizes to superior temporal scaling in a Morse code tapping task," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-9, January.
  • Handle: RePEc:plo:pone00:0221000
    DOI: 10.1371/journal.pone.0221000
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    References listed on IDEAS

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    2. Michael A. Long & Dezhe Z. Jin & Michale S. Fee, 2010. "Support for a synaptic chain model of neuronal sequence generation," Nature, Nature, vol. 468(7322), pages 394-399, November.
    3. Michael A. Long & Michale S. Fee, 2008. "Using temperature to analyse temporal dynamics in the songbird motor pathway," Nature, Nature, vol. 456(7219), pages 189-194, November.
    4. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
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