Chapter 4 Modeling variability and dependence in timing

Vorberg, Dirk; Wing, Alan M.

doi:10.1016/s1874-5822(06)80007-1

Cited by 224 publications

(442 citation statements)

References 34 publications

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“…Recall that the ITI variance is known to increase with increasing target interval (e.g., Vorberg and Wing 1996). Figure 5 shows the ITI variance for each condition.…”

Section: Resultsmentioning

confidence: 99%

“…The variability of the clock intervals has been found to increase monotonically with increasing interval duration (or decreasing movement frequency) (Vorberg and Wing 1996), implying that the ITI variance, c 0 ð Þ ¼ r 2 C þ 2r 2 D , increases as well. Note that the motor delay variance has been found to be constant over different tapping frequencies (Wing and Kristofferson 1973;Vorberg and Wing 1996). Hence, the absolute value of the lag-one autocorrelation will decrease with decreasing movement frequency.…”

Section: The Wk-modelmentioning

confidence: 99%

“…The model does not require any assumptions regarding the origin of the clock, only that the timing intervals are independent. Since its inception, the WK-model has been frequently used to analyze tapping sequences in a so-called synchronization-continuation paradigm, in which subjects first synchronize their tapping to a metronome and then continue the previously indicated rhythm after removal of the metronome (Wing and Kristofferson 1973;Musha et al 1985;Vorberg and Wing 1996;Pressing and Jolley-Rogers 1997;Pressing 1998;Ivry and Richardson 2002). The elegantly simple WKmodel provides an adequate description of various statistical properties of tapping sequences during continuation that can stand up to more elaborate models, which typically place more emphasis on long-term correlations in voluntary tapping (e.g., Chen et al 1997;Daffertshofer 1998;Delignières et al 2004).…”

Section: Introductionmentioning

confidence: 99%

“…In isochronous tapping subjects deviate from the fixed target tapping frequency by means of long-term drift (Vorberg and Wing 1996). This unintentional phenomenon has proven detrimental to the fit of the WK-model as it may either result in corrections that yield long-term correlations (e.g., Kaulakys 1999;Ding et al 2002) or, when monotonic, in non-stationary time series causing improper estimates of the autocorrelation function and ultimately in a poor model fit.…”

Section: Introductionmentioning

confidence: 99%

“…1 Instead of regarding drift as a confounding factor, however, it may be studied in its own right and explicitly incorporated in the experimental design. By introducing intentional drift, drift may become steady and hence predictable enough to be corrected by linear detrending (Vorberg and Wing 1996). Such an approach has been used to study synchronization error (Schulze et al 2005) and subjects' ability to sustain drift during the continuation phase of a synchronizationcontinuation paradigm (Madison and Merker 2005).…”

Section: Introductionmentioning

confidence: 99%

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Tapping with intentional drift

2008

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When tapping a desired frequency, subjects tend to drift away from this target frequency. This compromises the estimate of the correlation between inter-tap intervals (ITIs) as predicted by the two-level model of Wing and Kristofferson which consists of an internal timer ('clock') and motor delays. Whereas previous studies on the timing of rhythmic tapping attempted to eliminate drift, we compared the production of three constant frequencies (1.5, 2.0, and 2.5 Hz) to the production of tapping sequences with a linearly decreasing inter-tap interval (ITI) (corresponding to an increase in tapping frequency from 1.5 to 2.5 Hz). For all conditions a synchronization-continuation paradigm was used. Tapping forces and electromyograms of the index-finger flexor and extensor were recorded and ITIs were derived yielding interval variability and model parameters, i.e., clock and motor variances. Electromyographic recordings served to study the influence of tapping frequency on the peripheral part of the tap event. The condition with an increasing frequency was more difficult to perform, as evidenced by an increase in deviation from the intended ITIs. In general, tapping frequency affected force level, inter-tap variability, model parameters, and muscle co-activation. Parameters for the condition with a decreasing ITI were comparable to those found in the constant frequency conditions. That is, although tapping with an intentional drift is different from constant tapping and more difficult to perform, the timing properties of both forms of tapping are remarkably similar and described well by the Wing and Kristofferson model.

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“…Recall that the ITI variance is known to increase with increasing target interval (e.g., Vorberg and Wing 1996). Figure 5 shows the ITI variance for each condition.…”

Section: Resultsmentioning

confidence: 99%

Section: The Wk-modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tapping with intentional drift

2008

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Motor Control

Heuer

2003

Handbook of Psychology

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Skilled movements require the determination of motor outflow which is appropriate for a certain intended motor pattern; this poses an intricate control problem because of the complex transformations on the way from neural activity to actual limb movements. Solutions are reached by combinations of feedforward and feedback processes. Feedforward processes start before the physical movement and embrace a representation which anticipates it. This anticipatory representation is involved in control and can be modeled in different ways, for example, as a generalized motor program or as a neural network. Sensory information is required both to adapt the anticipatory movement representation to the environment and for feedback‐based corrections, but it is not necessarily the same kind of information on which perceptual awareness is based. Many tasks require the coordinated action of different limbs. While the interactions between the limbs involved are largely tuned to the task requirements, there are constraints which are hard or even impossible to overcome. These constraints originate at different levels of motor control. Overall motor control reveals a remarkable degree of flexibility in adjusting to new visuo‐motor transformations and force fields.

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