Scott E. Kerick

United States Army Combat Capabilities Development Command

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Kaleb McDowell

DEVCOM Army Research Laboratory

Piotr J. Franaszczuk

Johns Hopkins University

Bruce J. West

University of North Texas

Korosh Mahmoodi

University of North Texas

Paolo Grigolini

University of North Texas

Justin Brooks

University of Maryland, Baltimore County

Nilanjan Banerjee

University of Maryland, Baltimore County

Brent J. Lance

DEVCOM Army Research Laboratory

Bradley D. Hatfield

University of Maryland, College Park

Jean M. Vettel

DEVCOM Army Research Laboratory

Cooperative Institutions

DEVCOM Army Research Laboratory

United States Army Combat Capabilities Development Command

University of Maryland, College Park

University of Maryland, Baltimore County

Universidade Federal do Rio de Janeiro

Indian Institute of Technology Kharagpur

National Yang Ming Chiao Tung University

University of California, San Diego

United States Army

The University of Texas at Austin

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Complexity Synchronization

arXiv (Cornell University) (2022)

Korosh Mahmoodi Scott E. Kerick Paolo Grigolini Piotr J. Franaszczuk Bruce J. West

The observational ubiquity of inverse power law spectra (IPL) in complex phenomena entails theory for dynamic fractal phenomena capturing their fractal dimension, dynamics, and statistics. These and other properties are consequences of the complexity resulting from nonlinear dynamic networks collectively summarized for biomedical phenomena as the Network Effect (NE) or focused more narrowly as Network Physiology. Herein we address the measurable consequences of the NE on time series generated by different parts of the brain, heart, and lung organ networks, which are directly related to their inter-network and intra-network interactions. Moreover, these same physiologic organ networks have been shown to generate crucial event (CE) time series, and herein are shown, using modified diffusion entropy analysis (MDEA), to have scaling indices with quasiperiodic changes in complexity, as measured by scaling indices, over time. Such time series are generated by different parts of the brain, heart, and lung organ networks, and the results do not depend on the underlying coherence properties of the associated time series but demonstrate a generalized synchronization of complexity. This high order synchrony among the scaling indices of EEG (brain), ECG (heart), and respiratory time series is governed by the quantitative interdependence of the multifractal behavior of the various physiological organs' network dynamics. This consequence of the NE opens the door for an entirely general characterization of the dynamics of complex networks in terms of complexity synchronization (CS) independently of the scientific, engineering, or technological context.

Multifractal system

10.48550/arxiv.2210.16675

Cite

Citations (0)

Multilevel Longitudinal Analysis of Shooting Performance as a Function of Stress and Cardiovascular Responses

IEEE Transactions on Affective Computing (2020)

Derek P. Spangler Sazedul Alam Saad Ur Rahman Joshua C. Crone Ryan Robucci

Virtual reality (VR) systems are increasingly using physiology to improve human training. However, these systems do not account for the complex intra-individual variability in physiology and human performance across multiple timescales and psychophysiological demands. To fill this gap, we propose a theory of multilevel variability where tractable neurobiological mechanisms generate complex variability in performance over time and in response to heterogeneous sources. Based on this theory, we also present a study that examines changes in cardiovascular activity and performance during a stressful shooting task in VR. We examined physiology and performance at three important levels of analysis: task-to-task, block-to-block, session-to-session. Findings indicated joint patterns of physiology and performance that notably varied by the level of analysis. At the task level, higher task difficulty worsened performance but did not change cardiovascular activation. At the block level, there were nonlinear changes in performance and heart rate variability. At the session level, performance improved while blood pressure decreased and heart rate variability increased across days. Of all the physiological metrics, only heart rate variability was correlated with marksmanship performance. Findings are consistent with our multilevel theory and highlight the need for VR and other affective computing systems to assess physiology across multiple timescales.

Psychophysiology

10.1109/taffc.2020.2995769

Cite

Citations (17)

Temporal Complexity Measure of Reaction Time Series: Operational vs. Chronological Time

Research Square (Research Square) (2022)

Korosh Mahmoodi Scott E. Kerick Paolo Grigolini Bruce J. West Piotr J. Franaszczuk

Abstract Detrended Fluctuation Analysis (DFA) is a well-known method to evaluate scaling indices of time series, categorizing the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time RT(n) time series, where n is the trial number. Herein we propose treating each RT(n) as a duration time that changes the representation from operational (trial) time n to chronological (temporal) time t, or RT(t). To do this, we fill each time interval of RT(n) with fixed noise of magnitude 1 and with a randomly determined sign. The fixed noise represents the rigidity (order) and the random change of sign represents the flexibility (randomness) of the generated time series RT(t). Then the DFA algorithm was applied to RT(t) time series to evaluate scaling indices. We show that this new perspective leads to better results in: 1) differentiating scaling indices between low vs. high time-stress conditions and 2) predicting task performance outcomes. The dataset we analyzed is based on a Go-NoGo shooting task which was performed by 30 participants under low and high time-stress conditions in each of six repeated sessions over a three week period.

10.21203/rs.3.rs-1617624/v1

Cite

Citations (0)

Independent component analysis of dynamic brain responses during visuomotor adaptation

NeuroImage (2004)

José L. Contreras-Vidal Scott E. Kerick

Infomax

Sensorimotor rhythm

10.1016/j.neuroimage.2003.10.037

Cite

Citations (77)

Affective Physiological State Analysis and Interpretable Predictive Modeling of Marksmanship in Go/NoGo VR Shooting Difficulty Task

Sawon Pratiher Karuna P. Sahoo Mrinal Acharya Ananth Radhakrishnan SAZEDUL ALAM

This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Overburdening an individual’s limited cognitive resources, especially when engaged in critical operations, may result in disastrous mishaps. Regular assessments of individuals’ physiological states and associated performance become vital to improving their mission readiness in such scenarios. As a key step towards a field-ready system, this treatise discusses the experimental findings pertinent to affective physiological state modulation and predictive modeling of marksmanship during a Go/NoGo shooting task in an immersive virtual reality environment. The shooting exercise requires the participants to hit the enemy targets and spare the friendly targets. The shooting difficulty levels (SDLs) are introduced by modulating the subject-specific target exposure time. The physiological signals used for analysis comprise electrocardiogram (ECG), 64-channel electroencephalogram (EEG), and standard shooting performance scores from 31 subjects. Experimental results with ECG features encompass involuntary physiologic process regulation and the interplay between the autonomic nervous system (ANS) components varying with SDL. Similarly, EEG features highlight the variations in brain region activations with SDLs. Predictive modeling of shooting performance (enemy hit, friendly spare, overall score) and behavioral response (mean enemy reaction time) from physiological (ECG and EEG) features evince the potency of physiological sensing for marksmanship estimation in operational contexts. Moreover, interpretable Shapley value analysis of the predictive models comprehend the (positive/negative) marginal impact of the underlying physiological features on marksmanship. This multimodal physiological sensing framework may assess the alterations in psychophysiological affective states and cognitive effects for performance analysis in operational contexts.

Spare part

10.36227/techrxiv.21669248

Cite

Citations (1)

The Effects of Mental Workload Stress on Upper Trapezius Muscle EMG Activity in Computer Work.

PsycEXTRA Dataset (2013)

Lawrence M. Schleifer Thomas W. Spalding Bradley D. Hatfield Scott E. Kerick Jeffrey R. Cram

Trapezius muscle

Mental stress

10.1037/e335232004-001

Cite

Citations (0)

Cerebral Cortical Adaptations Associated with Visuomotor Practice

Medicine & Science in Sports & Exercise (2004)

Scott E. Kerick Larry W. Douglass Brad D. Hatfield

Electroencephalographic (EEG) recordings were examined at the temporal (T3, T4) regions of the cerebral cortex in novice pistol shooters (N = 11) over a training period of 12-14 wk to determine changes in activation. Mean alpha power and its rate of change were hypothesized to increase in the left temporal region during aiming from early to late season as participants improved their accuracy and reduced cognitive effort.Event-related alpha II power (ERAP; 11-13 Hz) was examined over a 5-s period preceding the trigger pull during shooting (SH) and two control conditions (resting baseline, BL; and postural simulation, PS) at early (time 1), middle (time 2), and late (time 3) practice.Mean levels of ERAP increased at T3 from the beginning to the end of the training period during both SH and PS, but not BL, whereas no such change in mean level of ERAP was noted at T4 during any of the three conditions. The practice-related cortical adaptation during SH covaried with an increase in shooting percentage over the season. A higher rate of increase in ERAP during the 5-s aiming period of SH relative to that at PS and BL was also observed throughout training at both T3 and T4. Exploratory analysis of global power (sites F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4) revealed that ERAP increased during SH from time 1 to time 3 at all sites except Fz and Pz, whereas only one site (C4) revealed an increase during BL.The reduction in cortical activity is likely due to sensorimotor integration and less cognitive effort due to automaticity.

10.1249/01.mss.0000106176.31784.d4

Cite

Citations (139)

Physiology & Behavior (2015)

Justin Brooks Scott E. Kerick

Previously we derived a new measure relating the driver's steering wheel responses to the vehicle's heading error velocity. This measure, the relative steering wheel compensation (RSWC), changes at times coincident with an alerting stimulus, possibly representing shifts in control strategy as measured by a change in the gain between visual input and motor output. In the present study, we sought to further validate this novel measure by determining the relationship between the RSWC and electroencephalogram (EEG) activity in brain regions associated with sensorimotor transformation processes. These areas have been shown to exhibit event-related spectral perturbation (ERSP) in the alpha frequency band that occurs with the onset of corrective steering wheel maneuvers in response to vehicle perturbations. We hypothesized that these regions would show differential alpha activity depending on whether the RSWC was high or low, reflecting changes in gain between visual input and motor output. Interestingly, we find that low RSWC is associated with significantly less peak desynchronization than larger RSWC. In addition we demonstrate that these differences are not attributable to the amount the steering wheel is turned nor the heading error velocity independently. Collectively these results suggest that neural activity in these sensorimotor regions scales with alertness and may represent differential utilization of multisensory information to control the steering wheel.

Alertness

Stimulus (psychology)

10.1016/j.physbeh.2015.05.026

Cite

Citations (13)

Brain Structure-function Couplings (FY11)

Jean M. Vettel Amy M. Dagro Stephen Gordon Scott E. Kerick Reuben H. Kraft

Abstract : In FY11, a collaborative team of researchers began a new Director's Strategic Initiative (DSI) to examine brain structure-function couplings. The effort aims to develop a multidisciplinary, multiscale understanding of the relationship between the brain's physical structure, its dynamic electrochemical functioning, and human behavior. Here, brain structure refers to the architecture of the brain, namely, the grey matter regions in the brain and the white matter fiber tracts that connect them, and brain function indicates the neuron activity that enables communication between those regions. Combined, the individual variations in brain structure and function are thought to underlie and predict individual differences in task performance and human behavior. One of the broad, far-reaching goals of this initiative is to understand the set of circumstances under which individual differences in brain structure can be leveraged to account, predict, or enhance the measurement of brain function at varying time scales. The initiative has four main research areas--electrochemical modeling, biomechanical structural changes, electrochemical data collection and analysis, and time-evolving functional connectivity--and this year-end report captures some of the research highlights and accomplishments during the first year of the program.

Brain Function

Structure function

10.21236/ada556969

Cite

Citations (1)

Understanding Brain, Cognition, and Behavior in Complex Dynamic Environments

Lecture notes in computer science (2009)

Scott E. Kerick Kaleb McDowell

Brain research

10.1007/978-3-642-02812-0_5

Cite

Citations (5)