Psychophysiological Markers of Performance and
Learning during Simulated Marksmanship in
Immersive Virtual Reality

Sicong Liu1, Jillian M. Clements1, Elayna P. Kirsch1, Hrishikesh M. Rao2,
David J. Zielinski1, Yvonne Lu1, Boyla O. Mainsah1, Nicholas D. Potter1,3,
Marc A. Sommer1, Regis Kopper4, and Lawrence G. Appelbaum1

Astratto

■ The fusion of immersive virtual reality, kinematic movement
tracking, and EEG offers a powerful test bed for naturalistic neuro-
science research. Here, we combined these elements to investigate
the neuro-behavioral mechanisms underlying precision visual–
motor control as 20 participants completed a three-visit, visual–
motor, coincidence-anticipation task, modeled after Olympic
Trap Shooting and performed in immersive and interactive virtual
reality. Analyses of the kinematic metrics demonstrated learning of
more efficient movements with significantly faster hand RTs, earlier
trigger response times, and higher spatial precision, leading to an
average of 13% improvement in shot scores across the visits. As

revealed through spectral and time-locked analyses of the EEG
beta band (13–30 Hz), power measured prior to target launch
and visual-evoked potential amplitudes measured immediately
after the target launch correlated with subsequent reactive kine-
matic performance in the shooting task. Inoltre, both launch-
locked and shot/feedback-locked visual-evoked potentials became
earlier and more negative with practice, pointing to neural mecha-
nisms that may contribute to the development of visual–motor pro-
ficiency. Collectively, these findings illustrate EEG and kinematic
biomarkers of precision motor control and changes in the neuro-
physiological substrates that may underlie motor learning. ■

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INTRODUCTION

Humans are adept at interacting skillfully in dynamically
changing environments. We are able to maintain internal
and external representations of our bodies, extrapolate
motion of objects, and engage physically with these ob-
jects to achieve complex and precise results. These skills
are largely because of the remarkable visual–motor inte-
gration enabled by our brains, occhi, and bodies.

Studies addressing visual–motor integration have led to a
wealth of knowledge about the mechanisms that translate
goals and states into motor commands (Elliott, Hayes, &
Bennett, 2012; Wolpert, Ghahramani, & Jordan, 1995),
motor learning ( Wolpert & Flanagan, 2016; Schmidt &
Lee, 2011), and the neural characteristics of expertise in
highly practiced individuals (Yarrow, Brown, & Krakauer,
2009). Visually guided movements have been studied in a
range of contexts to characterize the influence of visual
information on movement control, demonstrating a bal-
ance between preprogrammed ballistic movements and
visual-feedback-mediated refinements (Urbin, Stodden,
Fischman, & Weimar, 2011; Elliott et al., 2010; Desmurget
& Grafton, 2000; Meyer, Keith-Smith, Kornblum, Abrams,

1Duke University School of Medicine, 2MIT Lincoln Laboratory,
Lexington, MA, 3US Olympic Committee, Colorado Springs,
CO, 4University of North Carolina at Greensboro

© 2021 Istituto di Tecnologia del Massachussetts

& Wright, 1990), as classically characterized by Fitts’ Law
(Fitts, 1954).

Scalp-recorded EEG has been particularly instrumental
in understanding the cortical activity involved in visual–
motor integration. This high-temporal resolution approach
has allowed for the identification of processes essential for
visual scene (Appelbaum, Ales, & Norcia, 2012; Anokhin
et al., 2006) and motion perception ( Jensen, Kaiser, &
Lachaux, 2007; Kuba, Kubová, Kremlácek, & Langrová,
2007), such as is characterized by contralateralized visual-
evoked potentials (VEPs) within the first ∼200 msec after
stimulus presentation. Inoltre, EEG has been widely
used to understand attention (Appelbaum & Norcia, 2009;
Jensen et al., 2007) and memory (Robitaille et al., 2010;
Klimesch, 1999) processes necessary for goal-oriented
behaviors, as well as error-related responses that have been
shown to reflect learning through reweighting of sensory
informazione (Cohen, Elger, & Ranganath, 2007; Hajcak,
Moser, Holroyd, & Simons, 2006). These studies have led
to a broad understanding of the chronometry of processes
within the visual–motor cascade, including evidence that
higher attentional states, indexed by less default-mode pro-
cessing and lower alpha power, are associated with better
processing of visual stimuli (per esempio., Macdonald, Mathan, &
Yeung, 2011). These findings and the ease-of-use of EEG
have led to a growing number of studies evaluating expertise

Journal of Cognitive Neuroscience 33:7, pag. 1253–1270
https://doi.org/10.1162/jocn_a_01713

in activities such as marksmanship (Berka, Behneman, Kintz,
Johnson, & Raphael, 2010; Janelle & Hatfield, 2008;
Hatfield, Haufler, Hung, & Spalding, 2004; Hillman,
Apparies, Janelle, & Hatfield, 2000), golf putting (Arns,
Kleinnijenhuis, Fallahpour, & Breteler, 2008; Babiloni et al.,
2008), table tennis (Hülsdünker, Ostermann, & Mierau,
2019), badminton (Hülsdünker, Strüder, & Mierau, 2017),
and archery (Seo et al., 2012; Landers, Han, Salazar, &
Petruzzello, 1994). Inoltre, because the EEG power spec-
tra and ERPs associated with sensory processing, decision
making, and error recognition in these tasks correlate with
trial-by-trial response speed (Hülsdünker et al., 2019) E
different levels of accomplishment (Hatfield et al., 2004),
they may provide potentially useful biomarkers in closed-
loop neurofeedback and neurostimulation approaches
(Gruzelier, 2014; Paulus et al., 2009).

The prospect of investigating brain dynamics of actively
behaving participants in complex 3-D environments has
been challenging. In most natural environments, conditions
cannot be controlled and are difficult to replicate. In
aggiunta, studies using noninvasive modalities for recording
brain activity, such as EEG, usually aim to minimize artifact-
producing muscle activity by limiting mobility. Despite this,
real-world tasks involve dynamic movements that must be
achieved to fully engage perception, planning, and motor
control for executing complex objectives. Studies that limit
mobility may not capture all the cognitive and neural pro-
cesses involved in performing natural, full-body movements,
thereby creating a need for more advanced platforms for
naturalistic brain and body monitoring.

Recent improvements in simulation technology, motion
tracking, and mobile EEG have contributed to the develop-
ment of mobile brain/body imaging (MoBI), an approach
that investigates the links between distributed brain dynam-
ics and natural behavior (Gramann et al., 2011; Makeig,
Gramann, Jung, Sejnowski, & Poizner, 2009). Integration
of simulation technologies, such as immersive and interac-
tive virtual reality (VR) with brain and behavioral measure-
menti, allow for complex tasks to be performed in
controlled, indoor 3-D environments with high ecological
validity. In particular, previous studies have used the
MoBI approach to investigate physical interactions with
dynamically moving objects ( Jungnickel & Gramann,
2016), cognitive control during locomotion ( Wagner,
Makeig, Gola, Neuper, & Müller-Putz, 2016), and physical
exertion during high-intensity cycling (Enders et al.,
2016). Inoltre, skill training in VR translates successfully
to many real-world tasks that require procedural (Ragan,
2010), motor (dos Santos Mendes et al., 2012), and athletic
(Gray, 2017; Miles, Pop, Watt, Lawrence, & John, 2012; Fink,
Foo, & Warren, 2009; Aggarwal et al., 2006) apprendimento, and can
be used to simulate critical elements of stress and pressure
providing an avenue for widespread application (Sanz,
Multon, & Lécuyer, 2015; Stinson & Bowman, 2014).

In the current study, we combined these elements to in-
vestigate the neural and behavioral mechanisms underlying
visual–motor skill learning during a three-visit simulated

marksmanship training protocol based on the rules,
physics, and timing of Olympic Trap Shooting. In this
coincidence-anticipation task, individuals shot a simulated
flying clay pigeon with a firearm game controller while
kinematic movement tracking and 13-channel EEG were
recorded within a six-sided immersive and interactive VR
ambiente. Building upon our previous findings demon-
strating kinematic changes in the ballistic and refinement
stages of movement on this task (Rao et al., 2018), the goals
of the current study were to characterize the cascade of
kinematic and electrophysiological activity that contribute
to the successful shooting of targets. In particular, the pri-
mary aim of this study was to test if EEG biomarkers early in
the neural processing cascade correlated with ensuing be-
havioral outcomes, including RTs and eventual shot preci-
sion. As such, this study sought to establish biomarkers of
performance that unfold over time by leveraging the high
temporal resolution of EEG and the characteristic sequence
of actions involved in the trap shooting event. Therefore,
based on the temporal dependence of this trap shooting
task, we sought to establish brain states early in a trial
(either preparatory prior to the launch of the pigeon or
early in the evoked cascade of sensory responses) Quello
are able to reliably capture variance in measured kinematics
and EEG occurring later in the trial. Secondary goals were to
characterize changes in the latency and amplitude of kine-
matic and electrophysiological responses that occur as par-
ticipants practice this task and to determine if error-related
responses at the end of a trial contribute to performance in
the next trial, as would be expected from reinforcement
learning theories (Walsh & Anderson, 2012).

Based on the timing and movement constructs of this
trap shooting simulation and the literature describing
neuro-kinematic processes involved in visual–motor or-
ienting, several hypotheses were formulated and tested.
A central expectation was that VEPs would be present
contralateral to the launch direction of the target and that
larger amplitudes for this potential would correlate with
better shooting performance. A second expectation in this
study was that EEG spectral power over para-Rolandic,
motor control areas during the preparatory interval prior
to the launch of each target would correlate with move-
ment outcomes of the trial. Finalmente, although highly ex-
ploratory, it was hypothesized that the occurrence of
misses, versus hits, on each trial would elicit characteris-
tic feedback-related negativity (FRN; Holroyd & Coles,
2002). Such findings would add to understanding of
motor system physiology and aid in the development of
a naturalistic neuroscience approaches that can be used
for evaluation and training in applied contexts.

METHODS

Participants

Twenty-four participants (mean age = 25 ± 4.28 years,
13 women) were recruited to take part in this study. Four

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participants were excluded from subsequent analyses for
the following reasons: Two participants were left-handed,
creating discrepancies in the lateralized EEG analyses; one
participant had extensive marksmanship experience; E
one participant did not complete the entire protocol. IL
remaining 20 participants constituted a homogenous set of
right-handed novice marksmen, with normal or corrected-
to-normal vision, no history of neurological deficits or
family history of photosensitive epileptic seizures, and no
previous experience with the protocol presented in this
study. Participation was voluntary, and participants were
compensated $10/hr for their involvement. The experi-
mental protocol was approved by the Duke University
institutional review board.

Equipment

VR and Motion Tracking

The study was conducted in the Duke immersive Virtual
Environment (DiVE; Figure 1A), a six-sided cave automatic
virtual environment (CAVE) VR system (Cruz-Neira,
Sandin, & DeFanti, 1993), where participants stood in
the center of a room-size 3 × 3 × 3 m cube with projectors
directed at each of the cube’s six walls. Projectors were
run at 120 Hz with a total resolution of 1920 × 1920 pixels
per wall. Participants wore 3-D shutter glasses operating at

60 Hz to view stereoscopic graphics. A head tracking de-
vice was mounted on the glasses to control the system
viewpoint according to the participant’s head movements.
An Intersense IS-900 tracking system was used to record
the 3-D position and orientation of the controller and head
throughout the experiment. Data from both the controller
and head tracking sensors were sampled at 60 Hz.

An Xbox Top Shot Elite firearm game controller was used
for target shooting. The controller, furnished with a six
degrees-of-freedom tracking sensor, was held in the partic-
ipant’s right hand and stabilized with the opposite hand
placed along the barrel of the controller, as shown in
Figure 1B. Target acquisition was performed using a ray
casting technique, but rather than a visible ray, only a white
dot was shown at the target depth to mimic a laser sight.

EEG Recording

EEG signals were recorded using a BrainVision actiCAP sys-
tem with 13 active gel-based electrode channels, Quale
were placed on the scalp according to the 10–20 system
at locations F3, Fz, F4, C3, Cz, C4, T5, T6, P3, Pz, P4, O1,
and O2, with mastoid references. Two additional elec-
trodes were placed on the right and left outer canthi of
the eyes to record horizontal eye movements via HEOG.
All electrode impedances were kept below 10 kΩ to record
high-quality signals that were sampled at 1000 Hz. IL

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Figura 1. (UN) Rendering of the DiVE immersive and interactive VR facility where participants took part in the study. (B) Picture of an individual
participating in the marksmanship simulation wearing EEG and holding firearm controller, while target is in mid-flight. Each trial began when the
controller was aimed at the green trap house, after which the pigeon was launched and the participant was given one chance to shoot and hit the
moving target. (C) Screen immediately following a missed shot, showing the position of the target and controller, and feedback if it was a hit (green)
or miss (red), as well as the remaining trials and running accuracy in that task block. (D) Sequential screenshots illustrating paths of the six target
trajectories. (E) Schematic illustration of the time course in a given trial, including event actions, bodily kinematics, and derived EEG markers.

Liu et al.

1255

dominant environmental artifact at 60 Hz (power line
frequency) was attenuated using a 0.1- to 30-Hz bandpass
filter.

Experimental Design

Simulated Marksmanship Task

The task presented inside the DiVE was modeled after the
Olympic Trap Shooting event (International Shooting
Sport Federation, 2013), where the goal for the participant
was to shoot a digital clay pigeon that was launched from
behind a trap house. The design of the simulation abided
by the laws of physics for projectile motion (including
gravitational pull, air resistance, and lift force) to mimic
realistic projectile flight.

To begin the task, participants aimed the controller
toward a digital trap house, which was displayed as a rect-
angle above the ground, 54 ft in front of them in simulated
spazio. After an initial 500-msec waiting period, the trap
house changed color from red to green and a second
waiting period began that was variable between 0.5 E
1.0 sec. During the initial waiting period, if the participant
aimed the controller away from the trap house before the
color changed from red to green, the timer was reset and
did not begin until the participant aimed the controller
back toward the trap house. At the end of the second wait-
ing period, a digital target (displayed as an orange sphere
of radius 1 piedi) was launched in one of six directions with the
initial speed of 95.34 ft/sec.

Participants were given one chance to shoot the target.
The longer a participant took to pull the trigger, IL
further the target moved away from them, making it more
difficult to hit because of the change in depth and corre-
sponding reduction in visual angle. Once the trigger was
pulled, the screen immediately froze to display feedback
that included the participant’s shot location relative to
the location of the target, their cumulative shot success
(percent hits), and the number of trials remaining, COME
shown in Figure 1C. If the controller’s ray was in contact
with the target at the time of the shot, the target changed
color from orange to green, indicating a hit. If not, IL
color changed from orange to red to indicate a miss. IL
feedback remained on the screen until the controller was
returned to a position over the trap house, which started
the next trial sequence.

The six possible target trajectories, illustrated in
Figure 1D, consisted of three horizontal directions relative
to the center of the trap house (left = −45°, center = 0°,
right = 45°) and two elevations relative to the ground
plane (upper = 25.17°, lower = 12.95°). To increase eco-
logical validity (per esempio., fluctuations in outdoor environmental
conditions such as wind currents), random horizontal
noise ranging from −3° to 3° was added to the launch
angle. The maximum flight times for the target were
1.772 sec and 3.085 sec for the lower and upper trajectory
elevations, rispettivamente.

Training Protocol

Participants practiced the simulated marksmanship task
on three separate visits within 1 week. During each visit,
which lasted approximately 2 hr, the practice session
was split into eight blocks of 60 trials. Within each block,
all six target trajectories were presented 10 times in a ran-
dom order. During the first visit, participants were given
two practice rounds to become acquainted with the task
(approximately 7 min total). The first practice round con-
sisted of trials where the target was launched only in the
upper right direction, with the target at a larger size than in
the real simulation. The second practice round consisted
of six trials where a normal-sized target was launched in
each possible direction. The second practice round was
administered twice if the participants’ hit rate was below
50%. After completing the practice rounds, participants
began the eight blocks of training and had the option to
take a break between Blocks 2, 4, E 6 to prevent fatigue.

Psychophysiological Measures

The shooting task presented in this study is particularly
useful for investigating psychophysiological skill learning
because it produces discrete measures of performance
(participants either hit or miss the target) while still re-
quiring complex visual–motor coordination. In order to
determine the types of skill learning that occurred through
practice, dependent variables were extracted from the
kinematic motion tracking and EEG data to analyze
changes that occurred over time and to study the relation-
ships between motor proficiency and neural activity.
Figure 1E illustrates the sequence of event actions within
a single trial from which the EEG and kinematic variables
were extracted. Gray shaded regions indicate approximate
intervals and biomarkers during which kinematic (blue) E
EEG (orange) responses were calculated. These psycho-
physiological measures are discussed individually below.

Behavioral and Kinematic Measures

Shot success and shot error were used as indicators to de-
termine performance improvements through practice.
Shot success was calculated as the percentage of target
hits out of the total number of shots taken. Shot error
was measured as the Euclidean distance (in millimeters)
between the location of the shot and the center of the
target at the time of the trigger pull and therefore reflects
the precision of the shot attempt. High shot success rates
and lower shot error (defined by shorter distances to the
target) signify superior performance that is reproducible
and consistent.

The timings of key behavioral events, such as the onset of
movement, were extracted from the motion tracking and
HEOG data. RT—the elapsed time from the target launch
to the onset of movement—was calculated for the move-
ment of the eyes, controller, and head. Controller RT and

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head RT were calculated using 10% of the peak acceleration
trace, as measured with the Intersense motion trackers (van
Donkelaar & Franks, 1991). Acceleration was computed as
the derivative of the speed trace, after the speed trace was
smoothed with a seventh order finite impulse response
filter. An example acceleration trace collected for a single
trial is shown in Figure 2A. Oculomotor RT was calculated
using a rectified sum of the corneoretinal potential mea-
sured from the two HEOG channels for the left and right
horizontal target trajectories, as shown in Figure 2B. IL
corneoretinal potential occurred when the positive part of
the eye (the cornea) rotated closer to either the left or right
HEOG electrode, resulting in an upward voltage deflection
for the closer electrode and a downward deflection for the
opposite electrode. The final oculomotor RT was recorded
when a threshold of 3 SDs above a baseline mean (100 msec
postlaunch) was exceeded. Trigger response time was
calculated as the elapsed time between the target launch
and when the trigger was pulled to shoot. Trials were ex-
cluded from the analyses if the participant did not pull
the trigger to shoot (1.67%, 482 trials) or the movement
was initiated too quickly for a given trial, defined as a con-
troller RT of less than 16.667 msec, or one rendered video
frame (0.39%, 23 trials).

EEG Measures

EEG data were divided into three separate segments of the
task for analysis: (1) the prelaunch preparatory EEG
phase, which is thought to represent the participant’s
mental state prior to target launch; (2) the postlaunch
VEP that reflects visual-cortical processing of the target
prior to kinematic movement; E (3) the postshot FRN
that reflects the participants’ response to hits versus mis-
ses on each trial.

Preparatory EEG signals were extracted from a
500-msec window in the EEG data after the trap house

turned green. Baseline drifts were corrected by subtract-
ing the mean value of the signal in the preceding 50-msec
finestra. The preparatory phase was characterized by
spectral power. The baseline-corrected EEG signals were
transformed to compute the power spectral density esti-
mates using Welch’s method (window size = 250 msec,
overlap = 50%) and divided into the standard EEG fre-
quency bands for analysis: delta (0.1–4 Hz), theta (4–8 Hz),
alpha (8–13 Hz), and beta (13–30 Hz). Trials containing
excess muscle artifact were rejected by removing voltages
that exceeded a threshold of 200 μV (0.97%, 279 out of
28,800 trials).

VEPs were extracted from the 200-msec window imme-
diately following a target launch in the left or right direc-
tions to assess the contralateral/ipsilateral visual response
in the brain. Baseline correction was performed by sub-
tracting the average voltage in the 50-msec prelaunch
finestra. Electrodes P3 and P4 were selected for analysis
because of their posterior locations over the left and right
hemispheres of the visual cortex, rispettivamente, E
because of their higher signal quality as compared to
Channels O1 and O2, which tended to include more noise
because of cap fitting issues and neck muscle artifacts
produced during movement. Single-trial VEP amplitudes
were calculated using the average over a 20-msec window
centered on the participant’s peak VEP latency for that
visit. Trials were excluded from the averages if they ex-
ceeded a threshold of ± 35 μV (3.02%, 580 out of 19,200
trials) or contained data outside of 5 SDs of the joint prob-
ability distribution observed at each time point (0.35%, 67
out of 19,200 trials). There were no systematic differences
in rejection rates across the different trajectory directions.
The use of EEG artifact removal techniques (as opposed to
rejection) would have been challenging because of the
lack of clear biological templates (per esempio., ocular artifact cor-
rection) to base exclusion on. Inoltre, based on the low
prevalence of rejected trials (3.37% total), it was evident

Figura 2. (UN) Example of an acceleration trace measured from the firearm controller during a single trial. Kinematic dependent variables are
indicated by the triangle, star, and square markers. (B) Example of eye movement signals for a target launched rightward, captured via HEOG and
time-locked to the target launch at 0 msec that are used to calculate oculomotor RT, indicated here by the circle.

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that the signal under consideration offered an unimpeded
view of the neural activity that was meant to be scrutinized
in the planned hypothesis tests.

The FRN was calculated by extracting data epochs span-
ning from 200 msec before the trigger pull to 800 msec
after the trigger pull at channel Cz. These “shot-locked
potentials” were baseline corrected by subtracting the
average of a 100-msec preshot window. Individual trial
data were separately averaged for all successful shots (hits)
and for all unsuccessful shots (misses). Preliminary analy-
ses determined nonsignificant differences over the shot
elevations (high vs. low) and shot directions (left vs. center
vs. right). Therefore, the FRN was calculated using the
average over all trajectories, separately on each visit of
the protocol for all 20 participants. Single-trial FRN ampli-
tudes were calculated using the average of the 40 msec
surrounding the peak latency for that visit, whereas single
trial onset latencies were calculated as the latency at 1/3
peak amplitude.

Statistical Analysis

The purpose of this study was twofold: (1) to test if EEG
biomarkers early in the cascade of processes induced
during the coincidence anticipation task correlated with
ensuing behavioral outcomes, including shooting move-
ment RTs and eventual shot precision, E (2) to analyze
skill learning as a function of practice by exploring changes
in both EEG and behavior over time to determine if error-
related responses at the end of a trial contributed to per-
formance in the next trial.

All the preprocessing of EEG recordings was performed
in MATLAB (The MathWorks) using the EEGLab toolbox
(Delorme & Makeig, 2004). To investigate relationships
between EEG variables and motor behavior variables in a
data set showing substantial clustering structure (cioè., In-
traclass coefficient estimates are in [0.36, 0.92]), linear
mixed-effects models (LMEMs) were fit and tested using
the lme4 and lmerTest package in R Version 3.5.3
(R Core Team, 2019). LMEMs were fit to EEG and behav-
ioral data that included trial-level observations (task block
and visit [as a statistical nuisance factors to offset variable
discontinuities], EEG measures [preparatory delta/theta/
alpha/beta band power and VEP amplitude], launch direc-
zione), nested within individual participants. In different
models, controller RT or shot error served as outcome var-
iables while allowing for the intercept parameter to vary
among participants. The LMEMs involving controller RT
or shot error also included an exponential function term
of trial number (cioè., e-Trial Number), whose slope parameter
was treated as a random effect and allowed to vary among
participants. All the LMEMs reached normal convergence
using restricted maximum likelihood estimator. Nel
LMEMs, the statistical significance of the parameter esti-
mates was tested using t distribution with degree of freedom
adjusted using Satterthwaite’s method (see Kuznetsova,
Brockhoff, & Christensen, 2017).

Repeated-measures analysis of variance (rANOVA) era
performed in SPSS v25 to test skill learning across time
on both EEG and behavioral variables. A given rANOVA in-
volved either the main effect of Visit (cioè., Visit 1–3) or the
main effects of Block (cioè., Block 1–8) and interactions
from Visit and Block, depending on the specific question
being evaluated. Mauchly’s method was used to examine
the assumption of sphericity in the data. In case(S) Di
assumption violation, model outcomes based on the
Greenhouse–Geisser correction were adopted.

A final analysis was performed on the FRN to test its role
in reinforcement learning, where epochs were resegmen-
ted to create the sequential categories: hits following hits,
hits following misses, misses following hits, and misses
following misses. Single-trial mean amplitudes within the
40 msec surrounding the peak FRN for each of the four
segment classes were submitted to rANOVA to evaluate
main effects and interactions between the Shot Success
(cioè., hit or miss) of successive trial pairs. Effects were
considered statistically significant if multiple-comparison
adjusted p values were less than .05.

RESULTS

The data set in this study consists of metrics characterizing
task performance, movement kinematics, and electro-
physiology that change over the course of learning and il-
lustrate relationships between the brain and body. Nel
following sections, results from these three classes of met-
rics are presented to infer the detailed chronometry and
influence of psychophysiological activity associated with
performance on the marksmanship task.

Task Performance
Shot success rate—the percentage of hits out of total shots
taken—was calculated for each block and visit for all 20
participants. The average results are shown in Figure 3A.
As expected, participants significantly improved their suc-
cess rate through practice, with rANOVA demonstrating
main effects of Visit, F(2, 38) = 68.50, P < .001, η2 = .78, and Block on the first visit, F(7, 133) = 22.78, p < .001, η2 = .55, and the second visit, F(7, 133) = 10.04, p < .001, η2 = .346. However, there was not a main effect of Block on the third visit, F(7, 133) = 1.54, p = .16, η2 = .08, suggesting that performance may have begun to pla- teau as participants became more familiar with the task. Shot error—the Euclidean distance between the shot location and the center of the target—significantly de- creased across visits, F(2, 38) = 50.65, p < .001, η2 = .73, as shown in Figure 3B. A significant decrease across block was also observed on the first visit, F(7, 133) = 26.91, p < .001, η2 = .59; the second visit, F(7, 133) = 11.27, p < .001, η2 = .37; and the third visit, F(7, 133) = 3.27, p < .001, η2 = .15. Here, lower values represent shots that were closer to the target. Therefore, decreases in this distance 1258 Journal of Cognitive Neuroscience Volume 33, Number 7 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Figure 3. (A) Increases in shot success rate, measured as the proportion of hits versus misses on each block, and (B) decreases in shot error, measured as the Euclidian distance error from the target (lower is better), both illustrated that performance improved significantly over the course of practice. Error bars denote the within-subject standard deviation for each condition. show that participants continuously enhanced their per- formance as they practiced. Kinematic Behavior In addition to the shot success and shot error outcome measurements described above, kinematic tracking of the participants allowed for calculating onset movement times of eye, hand, and head. As illustrated in Table 1, these movement times proceeded first with oculomotor RTs indexing the start of horizontal eye movements re- corded through the HEOG channels at approximately 194 msec after target launch. This was followed within the first 300 msec by controller RTs and then head RTs, which marked the onset of hand and head movement, respectively. Finally, trigger response time occurred at approximately 1.5 sec, marking the average elapsed time between the target launch and the execution of the shot. Whereas oculomotor RTs were unchanged throughout the study and did not show a main effect of block or visit, other measures sped up throughout task practice. Namely, a significant decrease in controller RT was observed across visits, F(2, 38) = 27.53, p < .001, η2 = .59, and blocks on the first visit, F(7, 133) = 13.27, p < .001, η2 = .41; the second visit, F(7, 133) = 7.13, p < .001, η2 = .27; and the third visit, F(7, 133) = 5.75, p < .001, η2 = .23. These reductions indicate that hand movements became faster as participants practiced the task throughout the entirety of the training protocol. A significant decrease in head RT was also observed across visits, F(2, 38) = 3.92, p < .05, η2 = .17, but was not observed across blocks on any of the visits. Finally, trigger response time significantly decreased across visits, F(2, 38) = 4.70, p < .05, η2 = .20, as participants became faster overall at performing the task. EEG Biomarkers Scalp-recorded EEG provides detailed chronometry of the neural activity associated with performance on the marksmanship task. The following sections characterize the prelaunch preparatory-spectral, visual-evoked, and shot-locked EEG responses that occurred before, during, and after each trial of the task, respectively. Prelaunch Preparatory EEG To assess the relationship between prelaunch preparatory EEG and postlaunch motor performance, spectral power was calculated from channels C3, C4, and Cz using the sig- nals extracted in the 500-msec window after the trap house turned green (prior to the target launch). Time–frequency Table 1. Average Value and Standard Deviation of Reaction and Response Times (in milliseconds) for Each Visit Oculomotor RT (msec) Controller RT (msec) Head RT (msec) Visit 1 194 ± 46 213 ± 46 297 ± 13 Visit 2 194 ± 44 204 ± 39 284 ± 12 Visit 3 193 ± 49 199 ± 39 288 ± 12 Trigger response time (msec) 1,571 ± 399 1,529 ± 364 1,514 ± 337 Liu et al. 1259 analysis of the spectral power time-locked to the moment of trap house turning green produced spectral power estimates that generally decreased at higher frequencies and were broadly distributed over the center of the head, as illustrated in the group average spectrogram in Figures 4A and topography for beta band power in Figure 4B. Two LMEMs were fit to explore changes in preparatory alpha and beta power, over the course of practice with the marksmanship task. Trial number, task block, and visit were included in the LMEMs as fixed-effects terms with the intercept treated as a random effect among partici- pants. In both LMEMs, results showed that alpha and beta power tonically increased with the growth factor of trial number ( p < .001), controlling for other factors. Moreover, an LMEM was fit to test the predictive power of prelaunch preparatory EEG frequency band power, task block, and visit on postlaunch motor behavior measured by controller RT and shot error, respectively, given the ran- dom effects of intercept and nonlinear term (i.e., expo- nential function) among participants. The LMEM run on controller RT showed reasonable fit to the data, account- ing for 4%, 13%, and 52% of the variance associated with the Level 1 residual term, Level 2 intercept term, and Level 2 exponential term, respectively, given that the intraclass coefficient estimates suggested a high proportion of data variance coming from Level 2 random variables (Cohen, Cohen, West, & Aiken, 2013). Table 2 demonstrates pa- rameter estimates for the fixed-effects part of the LMEM, revealing that preparatory beta power was a significant predictor of controller RT, t(27907.30) = −3.53, p < .001. The regression coefficient can be interpreted such that, for each dB increase in beta power, controller RT was faster by 0.34 msec, when controlling for all other fac- tors. Results of LMEM for shot error indicated that EEG preparatory power did not correlate with shot error. Overall, this evidence implicated a functional role of beta band EEG in the marksmanship task toward predicting controller RTs. Figure 4C–4E illustrates the tonic variability pattern of beta power, controller RT, and shot error across the entire sequence of experimental trials in the three-visit l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Figure 4. (A) Time–frequency plot from para-Rolandic channels in a 1000-msec time window time-locked to the moment the trap house turning green with the white rectangle highlighting the extracted beta power signal from the preparatory period. (B) Scalp topography map of beta power during the preparatory period. White triangle highlighting the para-Rolandic channels. Group means and standard deviations across trials with block- and visit-wide regression lines are shown for (C) preparatory period beta power, (D) controller RT (CRT), and (E) shot error. Note that only the upper standard deviation bars were plotted for shot error to avoid possible misleading illustration that shot error can be negative-valued. 1260 Journal of Cognitive Neuroscience Volume 33, Number 7 Table 2. LMEM Fixed-Effect Results for Predicting Controller RT (Intercept) Alpha power Beta power Delta power Theta power Task block Visit 2 Visit 3 ***p < .001. Estimate (SE) 216.34 (4.52) −0.14 (0.12) −0.34 (0.10) 0.04 (0.04) −0.02 (0.09) −1.21 (0.08) −9.27 (0.46) −13.06 (0.46) t [df] 47.87 [22.37] −1.17 [27897.04] −3.53 [27907.30] 1.00 [27900.42] −0.24 [27900.18] −14.92 [27852.89] −20.34 [27885.91] −28.39 [27884.37] p Value < .001*** .24 < .001*** .32 .81 < .001*** < .001*** < .001*** protocol. Block-wise (red) and visit-wise (green) regres- sion lines are descriptively plotted along with group means and standard deviations. Visually Evoked Potential Based on the lateralization of the visual system, an impor- tant a priori hypothesis in this study was the expectation that visual cortical responses would differ in the hemi- sphere contralateral to left or right trajectory launches. Moreover, based on the expectation that physiological responses in the visual cortex would correlate with behavior, it was expected that this contralateralized potential would be earlier and larger on trials with better behavioral performance. As shown in Figure 5A and 5B, an early ipsilateral positive VEP began at around 100 msec, followed by the onset of a larger contralateral negative VEP that began at 110–120 msec and ended before the onset of oculomotor RT at around 194 msec. Statistical tests performed between 0 and 200 msec, with Bonferroni method to adjust for multiple comparisons, showed that significant differences existed between the VEPs for the left and right trajectory directions in the range of 98–179 msec and 111–173 msec, respectively, as indicated by the black bars below the VEPs in each plot. Figure 5C and 5D illustrate the evolution of the topographic dis- tribution of the evoked responses for left and right tra- jectories, respectively, demonstrating the presence of contralateralized responses over the posterior cortex, prior to the initiation of eye movements. In order to understand the role of the VEP in behavior, LMEMs were fit to test the predictive power of the contra- lateral VEP amplitude, launch direction, task block, and visit on controller RT and shot error, given the random effect of intercept and nonlinear exponential term among participants. In addition, the LMEMs included a fixed- effects factor, launch direction, to account for whether the trial observation came from channel P3 or P4. The LMEM was run with data excluding trials of central launch trajectory (to be consistent with the requirement of calcu- lating contralateral VEP amplitude). Overall, the LMEM showed reasonable fit to the data, explaining 6%, 15%, and 72% of the variance associated with the Level 1 residual term, Level 2 intercept term, and Level 2 exponential term, respectively. Results of the fixed-effects part in the LMEM run on controller RT are shown in Table 3. VEP amplitude was identified as a signif- icant predictor of controller RT, regardless of channel location, t(19076.33) = 11.10, p < .001.1 Namely, for each μV increment in the negative-going VEP amplitude, the controller RT became faster by 0.21 msec, controlling for all other factors. In addition, the results suggested that participants, on average, reacted to targets launched to the right 5.94 msec faster than to those launched to the left, t(19069.34) = −15.70, p < .001. Results of LMEM run on shot error indicated VEP amplitude did not predict shot error. To further evaluate learning that occurred over visits, additional rANOVAs were performed on the individual daily average VEP amplitudes for channels P3 and P4. The rANOVAs revealed significant decreases in amplitude across visits in both channel P3, F(2, 38) = 4.59, p < .05, η2 = .20, and channel P4, F(2, 38) = 9.18, p = .001, η2 = .33. These findings are illustrated for the grand average VEPs in Figure 6A and 6B, where successive decreases in amplitudes can be seen across consecutive visits. Combined Model Predicting Marksmanship Performance To obtain an overall model of the behavioral and EEG mea- sures leading up to shot execution that predict marksman- ship performance, an LMEM was run regressing controller RT on the preshot EEG-based variables (EEG power bands and VEP), task block, visit, and launch direction, while allowing parameters associated with the intercept and nonlinear exponential term to vary among participants. The LMEM was run with identical trial observations used Liu et al. 1261 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Figure 5. (A–B) Grand average VEPs for the left (black) and right (gray) target trajectories in channels P3 and P4. The black bars below the VEPs indicate latencies where the signals differ significantly between trajectory directions. (C–D) Grand average topographic distributions for left (top) and right (bottom) target trajectories, both showing posterior contralateral distributions from approximately 100 to 150 msec. in the previous section of testing VEP amplitude’s predic- tion on controller RT. Overall, the LMEM showed reason- able fit to data, accounting for 6%, 17%, and 66% of the variance associated with the Level 1 residual term, Level 2 intercept term, and Level 2 exponential term, respectively. Results from the fixed-effects part of the LMEM model are shown in Table 4. In particular, the results included previ- ous findings that both beta power, t(19073.51) = −3.14, p = .002, and VEP amplitude, t(19072.35) = 11.05, p < .001, significantly predicted controller RT. Moreover, Table 3. LMEM Fixed-Effect Results for Predicting Controller RT Using VEP Amplitude, Trial Number, Task Block, Visit, and Launch Direction (Intercept) VEP amplitude Right launch direction Task block Visit 2 Visit 3 ***p < .001. Estimate (SE) 214.25 (3.69) 0.21 (0.02) −5.94 (0.38) −1.00 (0.08) −8.22 (0.47) −11.05 (0.46) t [df] 58.00 [15.70] 11.10 [19076.33] −15.70 [19069.34] −12.10 [18992.47] −17.60 [19043.30] −23.80 [19042.31] p Value < .001*** < .001*** < .001*** < .001*** < .001*** < .001*** 1262 Journal of Cognitive Neuroscience Volume 33, Number 7 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Figure 6. Grand average contralateralized VEPs for each visit, time-locked to the target launch. (A) VEPs in channel P3 for targets launched rightward and (B) VEPs in channel P4 for targets launched leftward. Average VEP amplitudes became significantly more negative across consecutive visits, suggesting that the brain’s visual response to the target launch increased with practice. Gray shading illustrates 20-msec window over which amplitudes were averaged. * p < .05. ** p < .01. participants, on average, reacted to targets launched to the right 5.98 msec faster than those launched to the left, t(19038.00) = −15.82, p < .001. FRN and Sequential Trial Effects A final set of exploratory analyses was performed to deter- mine if a response similar to the FRN was present during the time period immediately following the trigger pull. In the present design, the pull of the trigger instantaneously froze the display, turned the target green or red depend- ing on shot success, and displayed the participant’s shot location relative to the location of the target. Because this happened in the context of ongoing movement to intercept the target, a first objective was to assess for the presence of an FRN-like response that is frontal-centrally distributed with greater negative amplitudes for misses, relative to hits. In the presence of such a response, a sec- ond objective was to determine whether the amplitude and/or latency of the FRN changed with practice and whether or not such changes correlated with behavioral learning. Finally, based on past demonstrations that the amplitude of the FRN predicts whether participants will learn to avoid an erroneous response on subsequent attempts (van der Helden, Boksem, & Blom, 2010), sequential-trial behavioral and brain effects were then evaluated under the hypotheses that (1) behavioral mea- sures would be faster for trials following misses than hits Table 4. LMEM Fixed-Effect Results for Predicting Controller RT Using All Preparatory Power Bands, VEP Amplitude, Trial Number, Visit, and Launch Direction (Intercept) Alpha power Beta power Delta power Theta power VEP amplitude Task block Right launch direction Visit 2 Visit 3 **p < .01. ***p < .001. Estimate (SE) 217.34 (3.74) −0.14 (0.12) −0.31 (0.10) 0.002 (0.04) −0.02 (0.09) 0.21 (0.02) −0.98 (0.08) −5.98 (0.38) −8.39 (0.47) −11.47 (0.47) t [df] 58.04 [18.62] −1.13 [19066.90] −3.14 [19073.51] 0.06 [19068.20] 0.24 [19072.72] 11.05 [19072.35] −11.83 [18994.55] −15.82 [19038.00] −17.99 [19038.65] −24.40 [19038.00] p Value < .001*** .26 .002** .96 .81 < .001*** < .001*** < .001*** < .001*** < .001*** Liu et al. 1263 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Figure 7. (A) Grand average ERPs for hits (dashed black line), misses (dotted gray line), and the miss-minus-hit differences (solid black line) at channel Cz, illustrating the FRN-like response. Gray shading indicates ± 20-msec window centered on the peak for this condition. (B) Average topography of FRN 260- to 280-msec posttrigger pull. (C) FRN over visits at channel Cz. (D) Means and SDs of FRN peak latency and amplitude over visits. ** p < .01 pairwise comparison. and (2) that greater FRN amplitudes would be followed by more successful behavior on the subsequent trials. Analysis of the FRN revealed differences between hits (black dashed line) and misses (gray dotted line), shown graphically in Figure 7A. The miss-minus-hit difference wave (black solid line) consists of a broadly distributed negative potential that peaks at approximately 280 msec, similar to the commonly reported FRN in the literature (Cohen et al., 2007; Hajcak et al., 2006), and is relatively unchanged in distribution over visits. The FRN scalp to- pography is displayed in Figure 7B. As displayed in wave- forms in Figures 7C and evidenced in rANOVA results in Figure 7D, the peak latency of this potential occurred ear- lier across visits, F(1.44, 27.24) = 17.158, p < .001, η2 = .475, shifting from 303 msec, to 300 msec, to 270 msec across the three visits. Similarly, rANOVA performed on the peak amplitude extracted within ± 20 msec of this peak also demonstrate significant differences across visits, F(1.77, 33.68) = 57.08, p < .001, η2 = .75, with successive reductions in the amplitude over visits. To better understand sequential trial effects, all trials were reclassified into four conditions: hits following misses, hits following hits, misses following misses, and misses following hits. Based on past research showing that indi- viduals exert greater control following errors (Allain, Burle, Hasbroucq, & Vidal, 2009; Rabbitt, 1966), it was ex- pected that participants would be more likely to hit the target following miss trials relative to hit trials. However, contrary to these expectations, rANOVA comparing the proportion of hits following hits to the number of hits following misses showed that participants were more likely to hit the target following a successful trial than an unsuccessful trial, F(1, 19) = 4.85, p = .04, η2 = .20. Across the three visits, participants were successful 67.5% of the time following hits, but only 65.7% of the time following misses. A 2 (Outcome: hit or miss) × 2 (Subsequent Shot: hit or miss) × 3 (Visit) rANOVA performed on the FRN amplitudes indicated that, although there was a main effect of Shot Outcome on FRN amplitudes, F(1, 18) = 25.21, p < .001, η2 = .58, this did not interact with the sequential shot result, F(1, 18) = 0.07, p = .79, indicating that performance on a given trial could not be predicted based on the amplitude of the shot-locked FRN on the previous trial. As such, the current exploratory analyses provide indication of an FRN response that changes with practice and evidence of behavioral sequential trial effects (akin to the “hot hand” where a person who experiences a successful outcome has a greater chance of success in further attempts), but do not provide support that the amplitude or latency of the FRN indexes behavioral changes across trials. DISCUSSION In this study, a simulated trap shooting task was used to investigate the behavioral and brain processes underlying visual–motor control and learning. Repeated natural movement patterns were measured with kinematic track- ing, whereas brain activity was simultaneously measured with EEG as participants practiced and became, on aver- age, 13.03% more accurate at shooting targets. Analysis of the behavioral measures revealed that performance im- provements were accompanied by faster controller RTs, lower shot errors, and earlier trigger response times, indi- cating that less time was needed for motor planning, exe- cution, and error correction as training progressed. Analysis of the EEG data reveals several markers that cor- related with upcoming motor performance and changed over time with practice. First in this sequence, beta band power measured in the brief 500-msec preparatory period before the target launch correlated with RTs of the upcom- ing shot. Next, following the target launch, a contralateral VEP was observed over posterior cortex, indicating that 1264 Journal of Cognitive Neuroscience Volume 33, Number 7 cortical processing of the target occurred before the onset of eye, hand, and head movements captured by the kinematics. The amplitude of this VEP became more negative with prac- tice and correlated with controller RT, pointing to a neural mechanism that may contribute to the development of visual–motor proficiency. Last, upon execution of the trig- ger pull, there was a broadly distributed potential that was more negative for misses than hits, suggestive of the com- monly reported FRN (Holroyd & Coles, 2002). Although this response became significantly smaller and earlier over the course of practice, it did not differ for subsequent hits versus misses as would be expected based on reinforce- ment learning models that have associated this response with reward prediction. Methodologically, it was shown that an MoBI approach is feasible for recording and analyzing EEG during a simulated trap shooting event and, by extension, other tasks of similar motor demand when conducted in an immersive and interactive CAVE- like VR system. Collectively, these findings build on our previous results demonstrating kinematic motor learning (Rao et al., 2018; Zielinski, Rao, Potter, Appelbaum, & Kopper, 2016) and biomarker identification (Clements et al., 2018), test for changes in brain activity and body movements that accom- pany skill learning, and establish biomarkers that can be used to infer behavioral performance. In the following discussion, we first address the rapidly growing field of MoBI then provide context for the observed learning and brain–behavior correlation findings, before closing with limitations and future directions that can help move this field forward. MoBI in an Immersive and Interactive Virtual Environment Traditional EEG experiments often record with motor responses in the form of minimal movements, such as button presses. In contrast, MoBI experiments simulta- neously record multiple sources of information under naturalistic contexts allowing a more complete representa- tion of the full-body behavior (Rao et al., 2020; Makeig, 2009). The current study used such an MoBI approach to provide important new insights into the brain dynamics accompanying the interception of a moving target in a naturalistic performance context with multiple levels of kinematic and brain measurement. Kinematic tracking performed inside a CAVE-like VR environment provided the exact timing of key events in simultaneous EEG re- cordings, which allowed for calculating brain responses throughout the full cascade of preparatory and execution steps of the target interception task. Importantly, although it might seem counterintuitive, EEG data collection in this immersive and interactive VR environment produced notably clean recordings. This is likely because of the fact that the participants stood in the center of the chamber with image projection from outside the cave onto the fabric walls, so the participants were actually quite far from interfering electrical noise sources. The fact that expected EEG metrics could be measured with quiet baseline inter- vals (demonstrating characteristic 1/f + alpha power spec- trum), low artifact rejection rates (< 3.5% using 35-μV rejection threshold), and significant conditional differences indicates further that the psychophysiological approach used here is conducive to identifying EEG bio- markers of behavior outcomes. Moreover, these findings may offer important information for how to integrate EEG into the emerging use of VR in motor learning appli- cations, performance training (Ahir, Govani, Gajera, & Shah, 2020; Appelbaum & Erickson, 2018), and rehabilita- tion (Levac et al., 2016; Levin, Weiss, & Keshner, 2015). EEG Biomarkers of Motor Behavior One of the main objectives in this research was to identify EEG markers of motor behavior during the simulated marksmanship task. The term “biomarker” commonly re- fers to a measurable indicator of a specific biological state, which can be used to describe a psychophysiological pro- cess underlying behavior. In recent years, there has been an increasing interest in the identification of biomarkers to assess skill in performance-related research (Lee et al., 2017; Cheron et al., 2016; Paulus et al., 2009). Validated biomarkers have the potential to aid coaches, athletes, and researchers in the design of better training programs that can increase their effectiveness. Despite interest in re- search on motor skills during dynamic activities, these studies typically involve minimally active laboratory tasks where the same principles may not apply to motor skills in more complex real-world situations. In this study, the addition of EEG recording in a VR train- ing environment added a new dimension into the investi- gation of the psychophysiological biomarkers that reflects complex motor behaviors. With this framework, candi- dates of behavior biomarker were extracted from EEG data and tested in three different sets of multilevel models (which are capable of accounting for the natural clustering structure in the data); one focused on the preparatory spectral power, one testing contralateral VEP amplitudes, and a combined model that each give different views of the correlational structure of these variables at different start (preparation, processing, and reaction) and end points (RT, response time, and shot error). As observed in the LMEMs, beta band power appeared as a reliable correlate of upcoming performance. This may mirror past findings demonstrating a role for beta in visual–motor integration during preparatory motor pro- cesses (Zaepffel, Trachel, Kilavik, & Brochier, 2013; Perez, Lundbye-Jensen, & Nielsen, 2006). In this model, it is thought that phase locking of neural populations within the beta band reflects greater entrainment that facilitates neural information transfer during motor planning and ex- ecution (Rueda-Delgado et al., 2014; van Wijk, Beek, & Daffertshofer, 2012). When interpreted in this context, the finding may indicate the development of an internal Liu et al. 1265 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 mental model that aids participants in shot execution (Monfared, Tenenbaum, & Folstein, 2019). Interestingly, studies comparing expert and novice marksmen have shown that experts exhibit greater alpha band power in the seconds leading up to the trigger pull (Berka et al., 2010; Janelle & Hatfield, 2008; Hatfield et al., 2004; Hillman et al., 2000). This has been interpreted as indicating that expert shooters possess greater default mode network activity and lower active cognition when executing shots (i.e., more automaticity and less top–down control). As such, contrasting experts and novices on alpha power may reflect differences in brain responses at different levels of learning. Nonetheless, because of the limited temporal window under scrutiny (500 msec) in this shooting simula- tion, these data are treated as tonic markers that may not have the same association as phasic changes often described in the literature. Future studies are thus encouraged to slow the pace of shooting tasks to allow better access to these important processes. Through subtle modifications to the shooting task, future studies may therefore be able to explore the connection between tonic and phasic variability in EEG spectral power and how they map to motor control and learning process. A further finding of interest was the observation that the amplitude of the contralateralized VEP time-locked to the target launch correlated positively with the controller RT. This link suggests that, in this time-limited target- interception task, greater processing resources early in the sensory cascade, indicated by more negative-going VEP amplitude, may have led to faster motor actions to in- tercept the target. This evoked potential, elicited to the vi- sual detection of the target launches, is theorized to reflect early visual and attentional processes, occurring in the contralateral occipital–parietal cortex. This is thought to be similar to the N2pc reported in other rapid attentional orienting tasks (An et al., 2012), but motor planning cir- cuits may also contribute to this evoked potential. We therefore encourage forthcoming investigations to further clarify these relevant ERP components in similar naturalis- tic motor tasks. Neural Signatures of Learning An important question in this research concerned the mechanisms of motor skill learning during the simulated marksmanship task. In particular, this task offers an inter- esting view to evaluate how visual-evoked responses and error detection signals may have changed with practice and contributed to skill acquisition. Although novel in its implementation, this approach extends past skill learning research with EEG, which helps exploit the contralaterali- zation of the visual and motor cortex, and to derive later- alized ERPs that changed through practice (van den Berg, Appelbaum, Clark, Lorist, & Woldorff, 2016; Clark, Appelbaum, van den Berg, Mitroff, & Woldorff, 2015). Based on these and other studies (e.g., An et al., 2012), it has been proposed that learning is accompanied by reorganizations at multiple stages of the neural hierarchy with dynamically interacting reorganizations at each stage (Makino, Hwang, Hedrick, & Komiyama, 2016). One observation from the current data was that the am- plitude of the contralateralized VEP became significantly more negative over the course of practice, implying that changes in the brain processes might occur through train- ing. This change in VEP amplitude is mirrored by observa- tions in the LMEM results, which consistently demonstrated significant effects of trial number in that participants were improving over the course of the experiment, a phenome- non descriptively illustrated in Figure 4D and 4E for con- troller RT and shot error, respectively. Finally, although highly exploratory in the context of this study, the FRN, which is known to occur in EEG recordings after a participant recognizes an error, was also found to oc- cur at earlier latencies and with lower mean amplitudes on successive days of practice. Such changes in sensory pro- cessing have been observed with learning in other domains, including perceptual learning (Appelbaum, Wade, Vildavski, Pettet, & Norcia, 2006), visual search (Arns et al., 2008; Aggarwal et al., 2006), and reward learning (dos Santos Mendes et al., 2012). As observed in Figure 7, subsequent differences in the later positive P3 component may also have indexed changes on a trial-by-trial or visit-by-visit basis. However, because these differences may arise from uncontrolled processes (e.g., lingering on the feedback or score), it is not possible to uniquely associate these with error processing. Having demonstrated here that the FRN can be captured, future studies may construct designs that can better index the multiple processes underlying this component. Conclusions The present results highlight a multiscale approach to link kinematic measurements of eye, hand, and head move- ments with EEG recordings during natural dynamic actions in a fully immersive and interactive VR environment. The full-body orienting task required participants to actively in- teract with their environments using fast, accurate move- ments, with evidence for both kinematic and neural learning over the course of task practice. Moreover, both beta band activity in the short preparatory period prior to target launch and contralateral VEP amplitudes in the period immediately after the target launch correlated with faster hand RTs, signaling important aspects of the neural upstream that contributes to motor performance. Taken together, the present protocol demonstrates the ability to quantify neurophysiological substrates of motor control and learning, while providing an empirical platform for the continued development of MoBI for applied uses. Future work should continue to incorporate ecologically valid measures in testing expert marksmen with the even- tual goal of deriving reliable biomarkers in real time to guide motor performance and learning. 1266 Journal of Cognitive Neuroscience Volume 33, Number 7 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 2 5 3 1 9 2 1 2 8 8 / / j o c n _ a _ 0 1 7 1 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Acknowledgments The authors would like to thank all of the participants for their time and effort during this study. Reprint requests should be sent to Lawrence G. Appelbaum, Department of Psychiatry, Duke University, 400 Trent Dr., Durham NC 27710, or via e-mail: greg@duke.edu. Funding Information This research was funded by a grant support to L. G. A. through the United States Army Research Office [W911NF- 15-1-0390]. Diversity in Citation Practices A retrospective analysis of the citations in every article published in this journal from 2010 to 2020 has revealed a persistent pattern of gender imbalance: Although the proportions of authorship teams (categorized by estimated gender identification of first author/last author) publishing in the Journal of Cognitive Neuroscience ( JoCN) during this period were M(an)/M = .408, W(oman)/M = .335, M/ W = .108, and W/ W = .149, the comparable proportions for the articles that these authorship teams cited were M/M = .579, W/M = .243, M/ W = .102, and W/ W = .076 (Fulvio et al., JoCN, 33:1, pp. 3–7). Consequently, JoCN encourages all authors to consider gender balance explicitly when selecting which articles to cite and gives them the opportunity to report their article’s gender citation balance. Note 1. A linear mixed effects model involving the interaction between VEP amplitude and launch direction was tested on con- troller RT, and the interaction was not statistically significant ( p > .14).

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Journal of Cognitive Neuroscience

Volume 33, Numero 7

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