Emergence of Frequency-Dependent Motor Variability Within Supra-Second Auditory Cueing

Abstract

Motor variability (MV) is an essential feature of the sensory motor system, and it plays an important role in sensory-motor learning. MV facilitates motor adaptation during auditory-motor synchronization (AMS). In AMS, individuals receive a series of similar auditory stimuli that come in a periodic manner at a fixed interval called an inter-stimulus interval (ISI). Peri-second ISI (1 second range) and supra-second ISI (>1 second) are differently processed, since these intervals involve different amount of cognitive resources. Supra-second ISI involves more top-down attention as compared to peri-second ISI. In this study we examined the effect of tone frequency (perceptual property of auditory stimuli) on predictive tapping and MV under peri-second and supra-second ranges. We examined the effect of tone frequency (a perceptual property of auditory stimuli) on predictive tapping and motor variability (MV) under short (peri-second) and long (supra-second) inter-stimulus intervals. Among 30 healthy participants (aged 18–35 years, M = 24.6 years), we randomly assigned equal numbers of these two inter-stimulus conditions to isochronous sound sequences. In their attempt to synchronize their motor responses with the tone, participants reproduced the ISI in their inter-tap intervals (ITIs). We analyzed their predictive tapping in terms of negative asynchrony (in which the tap occurs before the tone) and small positive asynchrony (0-100 ms), whereas we analyzed MV using the coefficient of variation (CV) of the ITI. We found that participants showed predictive tapping under short ISI, irrespective of the tone frequency. Moreover, their MV was unaffected by tone frequency. These findings imply that participants expressed MV in a predictive rather than reactive manner under short, but not long, ISI. Under long ISI, tone frequency had a significant effect on MV such that there was higher MV with the low-frequency than with the high-frequency tone. Thus, low-frequency tones are most suitable for auditory-motor learning in the supra-second range.

Keywords

auditory-motor synchronization tone frequency inter-stimulus interval inter-tap interval peri-second range supra-second range motor variability

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References

Bares

Lungu

Liu

Waechter

Gomez

C. M.

Ashe

(2007). Impaired predictive motor timing in patients with cerebellar disorders. Experimental Brain Research, 180(2), 355–365. https://doi.org/10.1007/s00221-007-0857-8

Björklund

Dunnett

S. B.

(2007). Dopamine neuron systems in the brain: An update. Trends in Neurosciences, 30(5), 194–202. https://doi.org/10.1016/j.tins.2007.03.006

Bourgeois

Chelazzi

Vuilleumier

(2016). How motivation and reward learning modulate selective attention. Motivation—Theory, Neurobiology and Applications, 229, 325–342. https://doi.org/10.1016/bs.pbr.2016.06.004

Clamann

(1969). Statistical analysis of motor unit firing patterns in a human skeletal muscle. Biophysical Journal, 9(10), 1233–1251. https://doi.org/10.1016/s0006-3495(69)86448-9

Hamilton

A. F. C.

Jones

K. E.

Wolpert

D. M.

(2004). The scaling of motor noise with muscle strength and motor unit number in humans. Experimental Brain Research, 157(4), 417–430. https://doi.org/10.1007/s00221-004-1856-7

Doyon

Benali

(2005). Reorganization and plasticity in the adult brain during learning of motor skills. Current Opinion in Neurobiology, 15(2), 161–167. https://doi.org/10.1016/j.conb.2005.03.004

Dum

R. P.

Strick

P. L.

(2003). An unfolded map of the cerebellar dentate nucleus and its projections to the cerebral cortex. Journal of Neurophysiology, 89(1), 634–639. https://doi.org/10.1152/jn.00626.2002

Fraisse

(1980). Les synchronisationssensori-motrices aux rythmes [The sensorimotor synchronization of rhythms]. Anticipation et Comportement, 233–257.

Gupta

D. S.

(2014). Processing of Sub- and supra-second intervals in the primate brain results from the calibration of neuronal oscillators via sensory, motor, and feedback processes. Frontiers in Psychology, 5, 816. https://doi.org/10.3389/fpsyg.2014.00816

10.

Halpern, D. L., Blake, R., & Hillenbrand, J. (1986). Psychoacoustics of a chilling sound. Perception & Psychophysics, 39(2), 77–80. https://doi.org/10.3758/bf03211488

11.

Ilie

Thompson

W. F.

(2006). A comparison of acoustic cues in music and speech for three dimensions of affect. Music Perception, 23(4), 319–330. https://doi.org/10.1525/mp.2006.23.4.319

12.

Ivry

R. B.

Spencer

R. M.

(2004). The neural representation of time. Current Opinion in Neurobiology, 14(2), 225–232. https://doi.org/10.1016/j.conb.2004.03.013

13.

Jones

K. E.

Hamilton

A. F.

Wolpert

D. M.

(2002). Sources of signal-dependent noise during isometric force. Journal of Neurophysiology, 88(3), 1533–1544. https://doi.org/10.1152/jn.2002.88.3.1533

14.

Lewis

P. A.

Miall

R. C.

(2003). Distinct systems for automatic and cognitively controlled time measurement: Evidence from neuroimaging. Current Opinion in Neurobiology, 13(2), 250–255. https://doi.org/10.1016/s0959-4388(03)00036-9

15.

Lohse

K. R.

Jones

Healy

A. F.

Sherwood

D. E.

(2014). The role of attention in motor control. Journal of Experimental Psychology. General, 143(2), 930–948. https://doi.org/10.1037/a0032817

16.

Mazzoni

Dannenberg

(2000). Audacity [Software]. The AudacityTeam. https://www.audacityteam.org/

17.

McLaughlin

(1990). “Conscious” versus “unconscious” learning. TESOL Quarterly, 24(4), 617. https://doi.org/10.2307/3587111

18.

Miyake

Onishi

Poppel

(2002). Two modes of timing anticipation in synchronization tapping. Transactions of the Society of Instrument and Control Engineers, 38(12), 1114–1122. https://doi.org/10.9746/sicetr1965.38.1114

19.

Nudo

Milliken

Jenkins

Merzenich

(1996). Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 16(2), 785–807. https://doi.org/10.1523/jneurosci.16-02-00785.1996

20.

Peters

(1989). The relationship between variability of internal intervals and interval duration. Psychological Research, 51(1), 38–42. https://doi.org/10.1007/bf00309274

21.

Repp

B. H.

(2013). Sensorimotor synchronization: A review of recent research (2006–2012). Psychonomic Bulletin & Review, 20(3), 403–452. https://doi.org/10.3758/s13423-012-0371-2

22.

Sanes

J. N.

Donoghue

J. P.

(2000). Plasticity and primary motor cortex. Annual Review of Neuroscience, 23(1), 393–415. https://doi.org/10.1146/annurev.neuro.23.1.393

23.

Scholz

J. P.

Danion

Latash

M. L.

Schöner

(2002). Understanding finger coordination through analysis of the structure of force variability. Biological Cybernetics, 86(1), 29–39. https://doi.org/10.1007/s004220100279

24.

Schultz

(1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1), 1–27. https://doi.org/10.1152/jn.1998.80.1.1

25.

Shadmehr

Smith

M. A.

Krakauer

J. W.

(2010). Error correction, sensory prediction, and adaptation in motor control. Annual Review of Neuroscience, 33(1), 89–108. https://doi.org/10.1146/annurev-neuro-060909-153135

26.

Skinner

(1981). Selection by consequences. Science (New York, N.Y.), 213(4507), 501–504. https://doi.org/10.1126/science.7244649

27.

Strick

P. L.

Dum

R. P.

Fiez

J. A.

(2009). Cerebellum and nonmotor function. Annual Review of Neuroscience, 32(1), 413–434. https://doi.org/10.1146/annurev.neuro.31.060407.125606

28.

Thaut

M. H.

Abiru

(2010). Rhythmic auditory stimulation in rehabilitation of movement disorders: A review of current research. Music Perception, 27(4), 263–269. https://doi.org/10.1525/mp.2010.27.4.263

29.

Van Beers

R. J.

Haggard

Wolpert

D. M.

(2004). The role of execution noise in movement variability. Journal of Neurophysiology, 91(2), 1050–1063. https://doi.org/10.1152/jn.00652.2003

30.

Liu

Ashe

Bushara

K. O.

(2006). Role of the Olivo-cerebellar system in timing. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 26(22), 5990–5995. https://doi.org/10.1523/jneurosci.0038-06.2006

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