Knowing when not to swing: EEG evidence that enhanced perception–action coupling underlies baseball batter expertise

Muraskin, Jordan; Sherwin, Jason; Sajda, Paul

doi:10.1016/j.neuroimage.2015.08.028

Cited by 38 publications

(53 citation statements)

References 61 publications

(79 reference statements)

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“…This behavioral paradigm consists of a Go/No-Go task using simulated baseball pitch videos that have been applied previously (Muraskin, et al, 2015; Sherwin, et al, 2015). The task based portion of this experiment is described in Muraskin, et al (in press).…”

Section: Methodsmentioning

confidence: 99%

“…Following our previous research (Muraskin, et al, in press; Muraskin, et al, 2015; Sherwin, et al, 2012; Sherwin, et al, 2015), the pitch simulations were created by solving 6-coupled differential equations (Adair, 1990; Armenti, 1992) in MATLAB 2010a (Mathworks, Natick, MA, USA) using a differential equation solver. The PsychToolbox (Brainard, 1997; Pelli, 1997) was used to present the simulated baseball pitches.…”

Section: Methodsmentioning

confidence: 99%

“…As an example, baseball players are exceedingly good at deciding whether to swing at a pitch, given a split second to observe a baseball's trajectory, spin, and speed. During this split second decision, there is little time for explicit decision-making; instead, the ability to hit a baseball relies on largely implicit mechanis ms that must link perception to action as a fast perceptual decision (Muraskin, et al, in press; Muraskin, et al, 2015; Nakamoto and Mori, 2008; Radlo, et al, 2001). This process is largely dependent on the efficiency of globally distributed networks throughout the brain (Forstmann, et al, 2010; Heekeren, et al, 2004); however, precisely which functional and structural components of these networks foster expert level performance remains a mystery.…”

Section: Introductionmentioning

confidence: 99%

“…We recruited Division 1 collegiate baseball players as an expert group as well as a matched control group, and we perform a seed-based connectivity analysis. We use seeds in right and left supplementary motor area (SMA) regions, including the preSMA, since we have previously shown in an EEG-only study (Muraskin, et al, 2015) and during the task portion of this dataset (Muraskin, et al, in press) that task-related differences between baseball players and controls were found in the SMA, and interestingly not in other areas associated with Go/No-Go tasks. A whole brain analysis with respect to these seed regions was performed to investigate group differences in the post-task resting state that may reflect expertise effects with respect to motor learning, modulatory attention, and cognitive processing related to motor inhibition.…”

Section: Introductionmentioning

confidence: 99%

“…In our experiment, both baseball experts and control participants perform a surrogate pitch discrimination task in a Go/No-Go paradigm (Muraskin, et al, in press; Muraskin, et al, 2015; Sherwin, et al, 2015) while simultaneous fMRI/EEG neuroimaging is collected, and we examine expertise effects in brain dynamics of the post-task resting state as well as related structural connectivity differences from DTI data. We examine three estimates of SMA region activity, that include the pre-SMA, from the averaged fMRI BOLD activity and from EEG sources, including mean alpha and beta power.…”

Section: Introductionmentioning

confidence: 99%

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Brain dynamics of post‐task resting state are influenced by expertise: Insights from baseball players

Muraskin

Dodhia

Lieberman

et al. 2016

Human Brain Mapping

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Post-task resting state dynamics can be viewed as a task-driven state where behavioral performance is improved through endogenous, non-explicit learning. Tasks that have intrinsic value for individuals are hypothesized to produce post-task resting state dynamics that promote learning. We measure simultaneous fMRI/EEG and DTI in Division-1 collegiate baseball players and compare to a group of controls, examining differences in both functional and structural connectivity. To examine functional differences, participants performed a surrogate baseball pitch Go/No-Go task before a resting state scan, and we compared post-task resting state connectivity using a seed-based analysis from the supplementary motor area (SMA), an area whose activity discriminated players and controls in our previous results using this task. Though both groups where equally trained on the task, the experts showed differential activity in their post-task resting state consistent with motor learning. Specifically, we found (1) differences in bilateral SMA – L Insula functional connectivity between experts and controls that may reflect group differences in motor learning, (2) differences in BOLD-alpha oscillation correlations between groups suggests variability in modulatory attention in the post-task state, and (3) group differences between BOLD-beta oscillations that may indicate cognitive processing of motor inhibition. Structural connectivity analysis identified group differences in portions of the functionally derived network, suggesting that functional differences may also partially arise from variability in the underlying white matter pathways. Generally, we find brain dynamics in the post-task resting state differ as a function of subject expertise and potentially result from differences in both functional and structural connectivity.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Brain dynamics of post‐task resting state are influenced by expertise: Insights from baseball players

Muraskin

Dodhia

Lieberman

et al. 2016

Human Brain Mapping

Self Cite

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Early Trajectory Prediction in Elite Athletes

et al. 2018

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Cerebellar plasticity is a critical mechanism for optimal feedback control. While Purkinje cell activity of the oculomotor vermis predicts eye movement speed and direction, more lateral areas of the cerebellum may play a role in more complex tasks, including decision-making. It is still under question how this motor-cognitive functional dichotomy between medial and lateral areas of the cerebellum plays a role in optimal feedback control. Here we show that elite athletes subjected to a trajectory prediction, go/no-go task manifest superior subsecond trajectory prediction accompanied by optimal eye movements and changes in cognitive load dynamics. Moreover, while interacting with the cerebral cortex, both the medial and lateral cerebellar networks are prominently activated during the fast feedback stage of the task, regardless of whether or not a motor response was required for the correct response. Our results show that cortico-cerebellar interactions are widespread during dynamic feedback and that experience can result in superior task-specific decision skills.

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A multimodal encoding model applied to imaging decision-related neural cascades in the human brain

et al. 2018

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Perception and cognition in the brain are naturally characterized as spatiotemporal processes. Decision-making, for example, depends on coordinated patterns of neural activity cascading across the brain, running in time from stimulus to response and in space from primary sensory regions to the frontal lobe. Measuring this cascade is key to developing an understanding of brain function. Here we report on a novel methodology that employs multi-modal imaging for inferring this cascade in humans at unprecedented spatiotemporal resolution. Specifically, we develop an encoding model to link simultaneously measured electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to infer high-resolution spatiotemporal brain dynamics during a perceptual decision. After demonstrating replication of results from the literature, we report previously unobserved sequential reactivation of a substantial fraction of the pre-response network whose magnitude correlates with a proxy for decision confidence. Our encoding model, which temporally tags BOLD activations using time localized EEG variability, identifies a coordinated and spatially distributed neural cascade that is associated with a perceptual decision. In general the methodology illuminates complex brain dynamics that would otherwise be unobservable using fMRI or EEG acquired separately.

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Knowing when not to swing: EEG evidence that enhanced perception–action coupling underlies baseball batter expertise

Cited by 38 publications

References 61 publications

Brain dynamics of post‐task resting state are influenced by expertise: Insights from baseball players

Brain dynamics of post‐task resting state are influenced by expertise: Insights from baseball players

Early Trajectory Prediction in Elite Athletes

A multimodal encoding model applied to imaging decision-related neural cascades in the human brain

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