Factorization of Force and Timing in Sensorimotor Performance: Long‐Range Correlation Properties of Two Different Task Goals

Balasubramaniam, Ramesh; Hove, Michael J.; Médé, Butovens

doi:10.1111/tops.12301

Cited by 6 publications

(4 citation statements)

References 52 publications

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“…Specifically, the resulting posterior was a multivariate t-distribution t(b|m, S L −1 , y), where b is the vector of regression coefficients, The validity of this setting is confirmed by the prior of the linear coefficient for L, namely, b L 1 . The estimate of b L 1 indeed coincides with the estimate of the scaling exponent γ, which has been shown to tend to unity by a vast corpus of behavioral studies (e.g., Balasubramaniam et al, 2018;Dotov et al, 2016;Fine et al, 2015;Gilden, 2001;Gilden et al, 1995;Hausdorff et al, 1995;Lemoine et al, 2006;Wijnants et al, 2009). In addition, γ → 1 is consistent with our setting b L 1 = 0.9.…”

Section: Distinguishing Between Mono-and Multifractality: Model Compa...supporting

confidence: 77%

A Bayesian classifier for fractal characterization of short behavioral series.

Solfo¹,

Leeuwen²

2023

Psychological Methods

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Serial tasks in behavioral research often lead to correlated responses, invalidating the application of generalized linear models and leaving the analysis of serial correlations as the only viable option. We present a Bayesian analysis method suitable for classifying even relatively short behavioral series according to their correlation structure. Our classifier consists of three phases. Phase 1 distinguishes between monoand possible multifractal series by modeling the distribution of the increments of the series. To the series labeled as monofractal in Phase 1, classification proceeds in Phase 2 with a Bayesian version of the evenly spaced averaged detrended fluctuation analysis (Bayesian esaDFA). Finally, Phase 3 refines the estimates from the Bayesian esaDFA. We tested our classifier with very short series (viz., 256 points), both simulated and empirical ones. For the simulated series, our classifier revealed to be maximally efficient in distinguishing between mono-and multifractality and highly efficient in assigning the monofractal class. For the empirical series, our classifier identified monofractal classes specific to experimental designs, tasks, and conditions. Monofractal classes are particularly relevant for skilled, repetitive behavior. Short behavioral series are crucial for avoiding potential confounders such as mind wandering or fatigue. Our classifier thus contributes to broadening the scope of time series analysis for behavioral series and to understanding the impact of fundamental behavioral constructs (e.g., learning, coordination, and attention) on serial performance.

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Section: Distinguishing Between Mono-and Multifractality: Model Compa...supporting

confidence: 77%

A Bayesian classifier for fractal characterization of short behavioral series.

Solfo¹,

Leeuwen²

2023

Psychological Methods

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“…When explicitly cueing force, an early experimental study found that greater variability of a finger-tapping sequence led to longer reaction times, and cueing greater force modulated the timing of taps around the target tap in a sequence (Semjen & Garcia-Colere, 1986). Cueing force and timing simultaneously revealed that performance on either task is internally consistent, but one does not predict performance on the other (Balasubramaniam et al, 2018), contrary to the view that periodic entrainment fixates other kinematic aspects of movement (Thaut et al, 2015). Moreover, the timing of taps appears most accurate not at initial contact, but at a point of peak force, a fraction of a second thereafter (Du et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Tap Harder: Cognitive Inhibition and Induced Emotions Predict Tapping Force in Young and Older Adults.

Mudarris¹,

Schaefer²

2022

Preprint

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Research examining movement to music typically examines dance, or the less demanding task of finger-tapping along to a rhythm. Previous studies have focused on timing accuracy of finger-taps, largely neglecting the force aspect of tapping, which is both important for understanding rhythmic movement, and relevant to clinical applications. In two experiments on healthy young and older adults, we examined the role of i) cognition, ii) subjective factors, and iii) properties of music that influence the force of finger-tapping to music of varying genre, tempi, and complexity. Our results revealed that switching/inhibition ability inversely predicted tapping force, whereas liking of the music, and music expressing positive emotions predicted greater tapping force. Despite known declines of motor and cognitive functions in aging, we found that older adults were no different than young adults on these factors, suggesting that mechanisms of tapping force may be spared in healthy aging. We examined other relevant factors based on previous findings, including working memory, familiarity with the music, and whether the music was activating, but none were predictive of applied force. Clinical implications are that for increased effort or engagement, developers and practitioners of music therapy may seek to assess aspects of cognition that influence participation, tailor the music to the patient’s preference, and pre-select musical samples that express positive emotions.

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“…Studies of human behavior more broadly have shown 1/f fluctuation in tasks such as grip force [19], self-paced tapping/time estimation [20–22], simultaneous force and time estimation [23], stride interval [24], synchronization tapping [25, 26], word naming [27], reaction times [28], other cognitive processes [3, 29], and also in brain activity during image observation [30]. 1/ f fluctuations are also seen in routine stable standing balance [31, 32], suggesting that this pattern of noise may be a fundamental aspect of the human cognitive and motor system, and compatible with healthy behavior.…”

Section: Introductionmentioning

confidence: 99%

Origins of 1/f noise in human music performance from short-range autocorrelations related to rhythmic structures

Colley

Dean

2019

PLoS ONE

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1/ f fluctuations have been described in numerous physical and biological processes. This noise structure describes an inverse relationship between the intensity and frequency of events in a time series (for example reflected in power spectra), and is believed to indicate long-range dependence, whereby events at one time point influence events many observations later. 1/ f has been identified in rhythmic behaviors, such as music, and is typically attributed to long-range correlations. However short -range dependence in musical performance is a well-established finding and past research has suggested that 1/ f can arise from multiple continuing short-range processes. We tested this possibility using simulations and time-series modeling, complemented by traditional analyses using power spectra and detrended fluctuation analysis (as often adopted more recently). Our results show that 1/ f -type fluctuations in musical contexts may be explained by short-range models involving multiple time lags, and the temporal ranges in which rhythmic hierarchies are expressed are apt to create these fluctuations through such short-range autocorrelations. We also analyzed gait, heartbeat, and resting-state EEG data, demonstrating the coexistence of multiple short-range processes and 1/ f fluctuation in a variety of phenomena. This suggests that 1/f fluctuation might not indicate long-range correlations, and points to its likely origins in musical rhythm and related structures.

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Factorization of Force and Timing in Sensorimotor Performance: Long‐Range Correlation Properties of Two Different Task Goals

Cited by 6 publications

References 52 publications

A Bayesian classifier for fractal characterization of short behavioral series.

A Bayesian classifier for fractal characterization of short behavioral series.

Tap Harder: Cognitive Inhibition and Induced Emotions Predict Tapping Force in Young and Older Adults.

Origins of 1/f noise in human music performance from short-range autocorrelations related to rhythmic structures

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