Multifractality in postural sway supports quiet eye training in aiming tasks: A study of golf putting

Jacobson, Noah; Berleman-Paul, Quinn; Mangalam, Madhur; Kelty-Stephen, Damian G.; Ralston, Christopher A.

doi:10.1016/j.humov.2020.102752

Cited by 22 publications

(11 citation statements)

References 99 publications

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“…Exploration is always relative to existing constraints, and exploration is employed to determine how or whether to replace old, outdated constraints with new ones (Raja & Anderson, 2021). Similarly, multifractal nonlinearity can assist or hinder goal attainment depending on the task or movement constraints (Jacobson et al, 2021; Kelty-Stephen et al, 2021). Multifractal nonlinearity only refers to the capacity for variability when shifting between scales.…”

Section: Discussionmentioning

confidence: 99%

“…Or can we encode how these droplets might have fallen and spread differently, depending on the spatial patterning of prevailing wind currents? Thus, a novel thread of research in perceptuomotor performance has lately begun to similarly inquire how we might understand the nonlinear nature of suprapostural dexterity through multifractal models (Dixon et al, 2012; Ihlen & Vereijken, 2010; 2013; Jacobson et al, 2021; Kelty-Stephen et al, 2021; Kelty-Stephen & Dixon, 2013; Kelty-Stephen & Mangalam, 2022; Mangalam et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations

et al. 2023

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An adaptive response to unexpected perturbations requires near-term and long-term adjustments over time. We used multifractal analysis to test how nonlinear interactions across timescales might support an adaptive response following an unpredictable perturbation. We reanalyzed torque data from 44 young and 24 older adults who performed a single-leg squat task challenged by an unexpected mechanical perturbation and a secondary visual-cognitive task. We report three findings: (a) multifractal nonlinearity interacted with pre-perturbation torque production and task error to presage greater pre-voluntary feedforward increases and greater voluntary reductions, respectively, in post-perturbation task error; (b) multifractal nonlinearity presaged relatively smaller task error than standard deviations of both pre-perturbation torques and pre-perturbation task error; and (c) increased task demand (e.g., age-related changes in dexterity and dual-task settings) led to multifractal nonlinearity presaging reduced task error. All these results were consistent with our expectations, except that a pre-perturbation knee torque-dependent increase in post-perturbation task error appeared later for older than for younger participants. This correlational multifractal modeling offered theoretical clarity on the possible roles of nonlinear interactions across timescales, moderating both feedforward and feedback processes, and presaging greater stability when the standard deviation is relatively large and task demands are strong. Thus, multifractal nonlinearity usefully describes movement variability even when paired with classical descriptors like the standard deviation. We discuss potential insights from these findings for understanding suprapostural dexterity and developing rehabilitative interventions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations

et al. 2023

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“…Multifractal geometry has repeatedly captured predictive information about how movement systems loosen their constraints to absorb and then propagate perceptual information. For example, this predictive capacity can be seen in haptic [47,48] and visual perception [49,50], as well as how movement systems integrate information across the body [51,52] and across different modalities [53]. In psycholinguistic begin with a long series of repeated measurements larger windows contain, on average, greater proportions of total measurements, P linear slope of log L vs. log P(L) yields singularity strength, a smaller windows contain greater variability in proportions of total measurements slope from linear regression yields Hausdorff dimension, f…”

Section: Multifractality To Portray Cross-scale Interactions Blending Fleeting Behaviours With Longer-term Behavioursmentioning

confidence: 97%

Perceiving and remembering speech depend on multifractal nonlinearity in movements producing and exploring speech

Bloomfield

Lane

Mangalam

et al. 2021

J. R. Soc. Interface.

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Speech perception and memory for speech require active engagement. Gestural theories have emphasized mainly the effect of speaker's movements on speech perception. They fail to address the effects of listener movement, focusing on communication as a boundary condition constraining movement among interlocutors. The present work attempts to break new ground by using multifractal geometry of physical movement as a common currency for supporting both sides of the speaker–listener dyads. Participants self-paced their listening to a narrative, after which they completed a test of memory querying their narrative comprehension and their ability to recognize words from the story. The multifractal evidence of nonlinear interactions across timescales predicted the fluency of speech perception. Self-pacing movements that enabled listeners to control the presentation of speech sounds constituted a rich exploratory process. The multifractal nonlinearity of this exploration supported several aspects of memory for the perceived spoken language. These findings extend the role of multifractal geometry in the speaker's movements to the narrative case of speech perception. In addition to posing novel basic research questions, these findings make a compelling case for calibrating multifractal structure in text-to-speech synthesizers for better perception and memory of speech.

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“…Multifractal geometry has repeatedly captured predictive information about how movement systems loosen their constraints to absorb and then propagate perceptual information. For example, this predictive capacity can be seen in haptic [47,48] and visual perception [49,50], as well as how movement systems integrate information across the body [51,52] and across different modalities [53]. In psycholinguistic domains, greater multifractal nonlinearity in self-paced reading may keep readers' expectations loose and open-minded enough to navigate unexpected plot twists more fluently, (i.e., with a lower increase in subsequent reading-time), indicating a lighter processing load [38].…”

Section: Multifractal Fluctuations In Measurements Of Movement Predict How Movements Support the Pick-up Coordination And Later Use Of Inmentioning

confidence: 97%

Perceiving and remembering speech depend on multifractal nonlinearity in movements producing and exploring speech

Bloomfield

Lane²,

Mangalam³

et al. 2021

Preprint

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Speech perception and memory for speech require active engagement. Gestural theories have emphasized mainly the effect of movements of the speaker on speech perception. They fail to address the effects of listener movement, focusing on communication as a boundary condition constraining movement among interlocutors. The present work attempts to break new ground by using multifractal geometry of physical action as a common currency for supporting both sides of the speaker-listener dyads. Participants self-paced their listening to a narrative, after which they completed a test of memory querying their narrative comprehension and the ability to recognize words from the story. The multifractal evidence of nonlinear interactions across timescales predicted the fluency of speech perception. Self-pacing movements that enabled listeners to control the presentation of speech sounds constituted a rich exploratory process. The multifractal nonlinearity of this exploration supported several aspects of memory for the perceived spoken language. These findings extend the role of multifractal geometry in the actions of the speaker to the narrative case of speech perception. In addition to posing novel basic research questions, these findings make a compelling case for imbuing multifractal structure in text-to-speech synthesizers for better perception and memory of speech.

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Multifractality in postural sway supports quiet eye training in aiming tasks: A study of golf putting

Cited by 22 publications

References 99 publications

Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations

Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations

Perceiving and remembering speech depend on multifractal nonlinearity in movements producing and exploring speech

Perceiving and remembering speech depend on multifractal nonlinearity in movements producing and exploring speech

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