Estimating the distribution of sensorimotor synchronization data: A Bayesian hierarchical modeling approach

Bååth, Rasmus

doi:10.3758/s13428-015-0591-2

Cited by 12 publications

(12 citation statements)

References 41 publications

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“…For each tap, tone-to-tap asynchrony was calculated as the time difference between the tone and the tap, a negative asynchrony indicating that the tap preceded the tone and vice versa. Asynchrony SD was taken as a measure of timing variability and it was estimated for each participant and ISI level using the Bayesian hierarchical method described in Bååth (2015). Timing variability is here used as a measure of performance in the sensorimotor synchronization task with low variability taken to indicate high performance.…”

Section: Discussionmentioning

confidence: 99%

“…For ISIs shorter than 1500 ms participants tend to produce few reactive taps (Repp and Doggett, 2007) and the Bayesian estimates will be highly similar to sample SD estimates. At longer ISIs participants tend to produce more reactive taps and the sample SD will underestimate the timing variability (Bååth, 2015). The Bayesian method corrects for this by discarding the information from the reactive taps and produces an estimate of asynchrony SD using only the information from the anticipatory taps.…”

Section: Discussionmentioning

confidence: 99%

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The role of executive control in rhythmic timing at different tempi

2016

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We investigated the role of attention and executive control in rhythmic timing, using a dual-task paradigm. The main task was a finger tapping task in which participants were asked to tap their index finger in time with metronome sequences. The tempo of the sequences ranged from 600 ms to 3000 ms between each beat. The distractor task, chosen so as to engage executive control processes, was a novel covert n-back task. When the tempo was slow, simultaneous performance of the tapping and n-back tasks resulted in significant performance degradation in both tasks. There was also some dual-task interference at the fast tempo levels, however, the magnitude of the interference was much smaller in comparison. The results suggests that, when the tempo is sufficiently slow, performing rhythmic timing demands attentional resources and executive control. This accords with models of time perception that assume that different timing mechanisms are recruited at different time scales. It also accords with models that assume a dedicated mechanism for rhythm perception and where rhythm perception is assumed to have a slower limit. Like visual perception can be divided into such subcategories as color perception, motion perception, and depth perception, so too can time perception. Some aspects of time perception are interval timing, temporal motor coordination, rhythm perception, and meter perception. It is possible to further subdivide time perception by modality and time scale. Much debated is whether all aspects of time perception share a common mechanism and, if not, what aspects of which mechanisms they do share (for a review see Ivry & Schlerf 2008). One influential class of models assumes that timing is governed by a pacemaker-accumulator type mechanism (Ivry, 1996), while more recent theoretical development are dynamical systems models that assume that timing and rhythm perception depend on oscillatory neural circuits (Large & Jones, 1999;Large, 2010). The former have been used successfully to model interval timing but has not proven a good model of responses to more complex stimuli such as musical rhythms, while the latter have been used to model rhythm and meter perception but have not been applied to interval timing (Grondin, 2010). The two mechanisms -pacemaker-accumulator type and oscillatory based -need not stand in opposition; models exist that incorporate both (Teki et al., 2012). KeywordsSome have suggested that time perception relies on different mechanisms, depending on time scale. Lewis and Miall (2006) report evidence that different neural mechanism are responsible for timing intervals shorter versus longer than one second. The timing of sub-second intervals has been termed automatic timing and that of supra-second intervals has been termed cognitive timing. These terms reflect that automatic timing recruits circuits within the motor system and auditory cortex, while cognitive timing depends more on circuits within the prefrontal and parietal cortices (Lewis & Miall, 2003). Interval timing is but one

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The role of executive control in rhythmic timing at different tempi

2016

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“…While the WK model explains two important variances, it does not explain the offset between stimulus and tapping, which also changes with IOI [27,29]. A Bayesian model of human timing [2,41,1] provides an explanation to the varying aim point in synchronisation. It explains this as statistical self-correction occurring between taps when a person is trying to maximise performance for a given loss function.…”

Section: Synchronisationmentioning

confidence: 99%

Modelling Error Rates in Temporal Pointing

Lee

Oulasvirta

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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“…This assumption is a limitation: anticipatory timing does not grow indefinitely as a function of IOI. Experiments where humans synchronize taps with a metronome of IOIs longer than 3500ms show that some taps precede the stimulus while others follow it, reducing the mean asynchrony [3], or even showing mean positive asynchronies for IOIs greater than 5000ms [5]. Hence, the relationship between metronome IOI and human asynchronies are clearly non-linear.…”

Section: Discussionmentioning

confidence: 99%

“…However, the asynchronies vary widely and can be positive, on average, for an individual tapping with IOIs longer than 2000ms [2]. For IOIs greater than 2000ms, asynchronies may show a bimodal distribution; some taps precede the stimulus while others follow it, with longer IOIs resulting in more taps that follow the stimulus and fewer that precede it [3]. The anticipation tendency is influenced by musical experience, as tap timing in musicians is closer to the stimulus than that in non-musicians for IOIs between 1000ms and 3500ms [4].…”

Section: Introductionmentioning

confidence: 99%

Delayed feedback embedded in perception-action coordination cycles results in anticipation behavior during synchronized rhythmic action: A dynamical systems approach

et al. 2019

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Dancing and playing music require people to coordinate actions with auditory rhythms. In laboratory perception-action coordination tasks, people are asked to synchronize taps with a metronome. When synchronizing with a metronome, people tend to anticipate stimulus onsets, tapping slightly before the stimulus. The anticipation tendency increases with longer stimulus periods of up to 3500ms, but is less pronounced in trained individuals like musicians compared to non-musicians. Furthermore, external factors influence the timing of tapping. These factors include the presence of auditory feedback from one’s own taps, the presence of a partner performing coordinated joint tapping, and transmission latencies (TLs) between coordinating partners. Phenomena like the anticipation tendency can be explained by delay-coupled systems, which may be inherent to the sensorimotor system during perception-action coordination. Here we tested whether a dynamical systems model based on this hypothesis reproduces observed patterns of human synchronization. We simulated behavior with a model consisting of an oscillator receiving its own delayed activity as input. Three simulation experiments were conducted using previously-published behavioral data from 1) simple tapping, 2) two-person alternating beat-tapping, and 3) two-person alternating rhythm-clapping in the presence of a range of constant auditory TLs. In Experiment 1, our model replicated the larger anticipation observed for longer stimulus intervals and adjusting the amplitude of the delayed feedback reproduced the difference between musicians and non-musicians. In Experiment 2, by connecting two models we replicated the smaller anticipation observed in human joint tapping with bi-directional auditory feedback compared to joint tapping without feedback. In Experiment 3, we varied TLs between two models alternately receiving signals from one another. Results showed reciprocal lags at points of alternation, consistent with behavioral patterns. Overall, our model explains various anticipatory behaviors, and has potential to inform theories of adaptive human synchronization.

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Estimating the distribution of sensorimotor synchronization data: A Bayesian hierarchical modeling approach

Cited by 12 publications

References 41 publications

The role of executive control in rhythmic timing at different tempi

The role of executive control in rhythmic timing at different tempi

Modelling Error Rates in Temporal Pointing

Delayed feedback embedded in perception-action coordination cycles results in anticipation behavior during synchronized rhythmic action: A dynamical systems approach

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