Devon Lantagne

Education, Licensure and Certification (2)

Ph.D.: Biomedical Engineering, Marquette University 2025

B.S.: Biomedical Engineering, Milwaukee School of Engineering 2016

Biography

Devon Lantagne is a visiting assistant professor in the Electrical, Computer and Biomedical Engineering department and has been a faculty member at MSOE since 2023. He is a dedicated educator and lifelong learner with 8+ years of teaching experience in embedded systems and biomedical instrumentation. His research has focused on human motor control and the use of subconscious memories to guide future movements. He has developed expertise in low-level embedded systems, data processing in MATLAB, state machines, biomedical device design, signal processing, and linear and nonlinear modeling.

Areas of Expertise (5)

Engineering Education

Signal Processing

Biomedical Instrumentation

Linear Circuits

Embedded Systems

Accomplishments (3)

Outstanding Teaching Assistant in Biomedical Engineering (professional)

2021

Program Chairman’s Award (professional)

2019 Rocky Mountain Bioengineering Symposium and Great Lakes Biomedical Conference

Vincent R. Canino Outstanding Biomedical Engineer Senior Student Award

2016

Affiliations (4)

• American Society for Engineering Education (ASEE)
• Order of the Engineer
• Institute of Electrical and Electronics Engineers (IEEE)
• Biomedical Engineering Society (BMES)

Social

Event and Speaking Appearances (5)

Memory use during implicit learning varies across sensory feedback conditions, but is not impacted by interposed self- assessments

Society for Neuroscience  Washington, D.C.

Does motor memory usage change in concussed individuals performing a sensorimotor task?

Society for Neuroscience  San Diego, CA

Assessing Balance and Motor-Memory Deficits After Concussion

Marquette University Forward Thinking Session  Milwaukee, WI

Performance Suppression on Implicit Sensorimotor Adaptation

Society for Neuroscience  Chicago, IL

Assessing Balance and Motor-Memory Deficits After Concussion

Marquette University Forward Thinking Session  Milwaukee, WI

Teaching Areas (2)

Embedded Systems

The implementation of small computers to serve a function. This includes measuring signals, interpreting them, and then performing an action.

Linear Circuits

The study of basic circuit components and how they interact.

Research Interests (1)

Human Neuromotor Control

The study of how humans sense their environment and produce appropriate movements.

Selected Publications (2)

Contribution of implicit memory to adaptation of movement extent during reaching against unpredictable spring-like loads: insensitivity to intentional suppression of kinematic performance

Experimental Brain Research

Devon D Lantagne, Leigh Ann Mrotek, James B Hoelzle, Danny G Thomas, Robert A Scheidt

2023-07-28

We examined the extent to which intentionally underperforming a goal-directed reaching task impacts how memories of recent performance contribute to sensorimotor adaptation. Healthy human subjects performed computerized cognition testing and an assessment of sensorimotor adaptation, wherein they grasped the handle of a horizontal planar robot while making goal-directed out-and-back reaching movements. The robot exerted forces that resisted hand motion with a spring-like load that changed unpredictably between movements. The robotic test assessed how implicit and explicit memories of sensorimotor performance contribute to the compensation for the unpredictable changes in the hand-held load. After each movement, subjects were to recall and report how far the hand moved on the previous trial (peak extent of the out-and-back movement). Subjects performed the tests under two counter-balanced conditions: one where they performed with their best effort, and one where they intentionally sabotaged (i.e., suppressed) kinematic performance. Results from the computerized cognition tests confirmed that subjects understood and complied with task instructions. When suppressing performance during the robotic assessment, subjects demonstrated marked changes in reach precision, time to capture the target, and reaction time. We fit a set of limited memory models to the data to identify how subjects used implicit and explicit memories of recent performance to compensate for the changing loads. In both sessions, subjects used implicit, but not explicit, memories from the most recent trial to adapt reaches to unpredictable spring-like loads. Subjects did not "give up" on large errors, nor did they discount small errors deemed "good enough". Although subjects clearly suppressed kinematic performance (response timing, movement variability, and self-reporting of reach error), the relative contributions of sensorimotor memories to trial-by-trial variations in task performance did not differ significantly between the two testing conditions. We conclude that intentional performance suppression had minimal impact on how implicit sensorimotor memories contribute to adaptation of unpredictable mechanical loads applied to the hand.

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Contributions of implicit and explicit memories to sensorimotor adaptation of movement extent during goal-directed reaching

Experimental Brain Research

Devon D Lantagne, Leigh Ann Mrotek, Rebecca Slick, Scott A Beardsley, Danny G Thomas, Robert A Scheidt

2021-06-09

We examined how implicit and explicit memories contribute to sensorimotor adaptation of movement extent during goal-directed reaching. Twenty subjects grasped the handle of a horizontal planar robot that rendered spring-like resistance to movement. Subjects made rapid “out-and-back” reaches to capture a remembered visual target at the point of maximal reach extent. The robot’s resistance changed unpredictably between reaches, inducing target capture errors that subjects attempted to correct from one trial to the next. Each subject performed over 400 goal-directed reaching trials. Some trials were performed without concurrent visual cursor feedback of hand motion. Some trials required self-assessment of performance between trials, whereby subjects reported peak reach extent on the most recent trial. This was done by either moving a cursor on a horizontal display (visual self-assessment), or by moving the robot’s handle back to the recalled location (proprioceptive self-assessment). Control condition trials performed either without or with concurrent visual cursor feedback of hand motion did not require self-assessments. We used step-wise linear regression analyses to quantify the extent to which prior reach errors and explicit memories of reach extent contribute to subsequent reach performance. Consistent with prior reports, providing concurrent visual feedback of hand motion increased reach accuracy and reduced the impact of past performance errors on future performance, relative to the corresponding no-vision control condition. By contrast, we found no impact of interposed self-assessment on subsequent reach performance or on how prior target capture errors influence subsequent reach performance. Self-assessments were biased toward the remembered target location and they spanned a compressed range of values relative to actual reach extents, demonstrating that declarative memories of reach performance systematically differed from actual performances. We found that multilinear regression could best account for observed data variability when the regression model included only implicit memories of prior reach performance; including explicit memories (self-assessments) in the model did not improve its predictive accuracy. We conclude therefore that explicit memories of prior reach performance do not contribute to implicit sensorimotor adaptation of movement extent during goal-directed reaching under conditions of environmental uncertainty.

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