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This study examined how humans spontaneously merge a sequence of discrete actions into a rhythmic pattern, even when periodicity is not required. Two experiments used a virtual throwing task, in which subjects performed a long sequence of discrete throwing movements, aiming to hit a virtual target. In experiment 1, subjects performed the task for 11 sessions. Although there was no instruction to perform rhythmically, the variability of the interthrow intervals decreased to a level comparable to that of synchronizing with a metronome; furthermore, dwell times shortened or even disappeared with practice. Floquet multipliers and decreasing variability of the arm trajectories estimated in state space indicated an increasing degree of dynamic stability. Subjects who achieved a higher level of periodicity and stability also displayed higher accuracy in the throwing task. To directly test whether rhythmicity affected performance, experiment 2 disrupted the evolving continuity and periodicity by enforcing a pause between successive throws. This discrete group performed significantly worse and with higher variability in their arm trajectories than the self-paced group. These findings are discussed in the context of previous neuroimaging results showing that rhythmic movements involve significantly fewer cortical and subcortical activations than discrete movements and therefore may pose a computationally more parsimonious solution. Such emerging stable rhythms in neuromotor subsystems may serve as building blocks or dynamic primitives for complex actions. The tendency for humans to spontaneously fall into a rhythm in voluntary movements is consistent with the ubiquity of rhythms at all levels of the physiological system. NEW & NOTEWORTHY When performing a series of throws to hit a target, humans spontaneously merged successive actions into a continuous approximately periodic pattern. The degree of rhythmicity and stability correlated with hitting accuracy. Enforcing irregular pauses between throws to disrupt the rhythm deteriorated performance. Stable rhythmic patterns may simplify control of movement and serve as dynamic primitives for more complex actions. This observation reveals that biological systems tend to exhibit rhythmic behavior consistent with a plethora of physiological processes.
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Applying Functional Data Clustering for Analyzing Cycles of Periodic Activity of Honeybees
—In this paper, we analyze the periodic cycle of honeybees when they have between 7 and 9 days of age. The circadian clock of the bees present very erratic behavior that it is a challenge to detect cycles. In signal processing, there are several methods to detect periodic patterns. In here, we will use a well-known test, named periodogram, to evaluate rhythmicity and estimate the period. Besides, to determine whether or no rhythmicity exists, we estimate the time when the bees behavior starts to be rhythmic. Also, it can occur that the bees behavior never gets rhythmic. The test of rhythmicity is applied consecutively until find out periodicity, if this exists. Furthermore, we carry out the periodicity test for the time series obtained from the actogram. We find out that for bees which time series is visually periodic, our method detects correctly the starting time. However, for bees which time series does not show a cyclic pattern our method fails due to a very erratic time series and that the consecutive test results also will show this erratic behavior. Finally, we classify the bees according to theirs beginning of a periodic cycle, using functional data analysis.
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- PAR ID:
- 10095893
- Date Published:
- Journal Name:
- ICDM 2018
- Page Range / eLocation ID:
- 1423-1429
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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