M. J. Spivey, M. Grosjean, and G. Knoblich showed that in a phonological competitor task, participants' mouse cursor movements showed more curvature toward the competitor item when the competitor and target were phonologically similar than when the competitor and target were phonologically dissimilar. Spivey et al. interpreted this result as evidence for continuous cascading of information during the processing of spoken words. Here we show that the results of Spivey et al.need not be ascribed to continuous speech processing. Instead, their results can be ascribed to discrete processing of speech, provided one appeals to an already supported model of motor control that asserts that switching movements from 1 target to another relies on superposition of the 2nd movement onto the 1st. The latter process is a continuous cascade, a fact that indirectly strengthens the plausibility of continuous cascade models. However, the fact that we can simulate the results of Spivey et al.with a continuous motor output model and a discrete perceptual model shows that the implications of Spivey et al.'s experiment are less clear than these authors supposed.

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Studies using a variety of experimental tasks have established that when humans repeatedly produce an action, fluctuations in action output are highest at the lowest frequencies and fluctuation magnitude (power) systematically declines as frequency increases. Such time series structure is termed pink noise. However, the appearance of pink noise seems to be limited to tasks where action is executed in the absence of task-related feedback. A few studies have demonstrated that when action was executed in the presence of task-related feedback, power was evenly distributed across all spectral frequencies--i.e., white noise was revealed. Here, participants produced cyclical aiming movements under visual feedback conditions and we sought to determine whether variations of both the movement amplitude requirement (A) and the target width (W)--in the form of the index of difficulty [ID = log2(2A/W)]--would predict the structure of movement amplitude (MA) time series. There were two ID levels, and there was a small- and large-scale version of each ID: The A and W values of the large-scale version were twice those used for the small-scale version. Given that increases in ID are known to induce increased reliance on the available visual feedback, we predicted an ID-induced shift in MA time series structure from pink to white noise. Indeed, that is what we found. Further, there were no changes in MA structure when scale level changed within each ID level. Such scale invariance of MA time series structure reinforces the notion that MA structure depends on the combined influence of A and W.

Studies using a variety of experimental tasks have established that when humans repeatedly produce an action, the amount of variability in system output is distributed across a range of time scales or frequencies. A finding of particular interest is that fluctuations in the output of cognitive systems are the highest at the lowest frequencies with fluctuation magnitude (power) systematically declining as frequency increases. Such time-series structure--captured by spectral analysis--is termed pink noise. However, the appearance of pink noise seems to be limited to tasks where action is executed in the absence of external, task-related feedback. In contrast, a few studies have demonstrated that when action was executed in the presence of external, task-related feedback, power was evenly distributed across all spectral frequencies--that is, a white-noise time-series structure was revealed. Here, we sought to determine if the time-series structure of movement amplitude values would change when movement amplitude requirements increased (6.35, 12.70, 25.40, 50.80, and 101.60 mm) under conditions of full visual feedback. Given that increases in movement amplitude requirements are known to induce increased reliance on the available visual feedback, we predicted an amplitude-requirement-induced shift in time-series structure from pink to white noise. Indeed, those results were revealed. Last, the main findings were captured by a computer simulation that was based on established principles of motor control.

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