U.S. Army Research Lab
Translational Neuroscience Branch
Kaleb McDowell has a B.S in operations research and industrial engineering from Cornell University, Ithaca, NY, USA in 1992, an M.S. in kinesiology from the University of Maryland, College Park, MD, USA in 2000, and a Ph.D. in neuroscience and cognitive science from the University of Maryland, College Park, MD, USA in 2003. He is currently the Chief of the Translational Neuroscience Branch at the U.S. Army Research Laboratory’s Human Research and Engineering Directorate; which focuses on: 1) developing tools to enable real-world neuroimaging for people interested in the laboratory-grade study of brain in ecologically valid settings and people developing brain-computer interaction (BCI) technologies; 2) understanding how individual differences in brain structure and function influence real-world behavior; and 3) developing BCI technologies with broad impact on society. Since joining ARL, Dr. McDowell has developed a strong record of publication and impact within government, industry, and academic research and development communities and he has led several major research and development programs focused on neuroscience, indirect vision systems and vehicle mobility, receiving Army Research and Development Achievement awards in 2007 and 2009 and the ARL Awards for Leadership and Engineering in 2011 and 2013.
From Real-World Neuroimaging to Adaptive Technologies
Significant variability in human performance has been demonstrated both between individuals and within individuals based on continuous changes in intentions as well as physical, cognitive, emotional and social states. This variability arises in large part from modulations in the functioning and intricate coordination of trillions of neurons in the central and peripheral nervous systems. Decades of heavy investment in laboratory-based brain imaging and neuroscience have led to foundational insights into the relationships between these neural modulations and how humans sense, perceive, and interact with the external world. However, it is argued that fundamental differences between laboratory-based and naturalistic human behavior may exist. Here, we discuss efforts to take a multi-aspect approach to develop tools that will enhance our ability to assess brain activity in real-world scenarios, enabling the interpretation of human variability arising from both internal and external sources at multiple spatial and temporal scales. We focus on the future applications that assessment technologies will underlie including providing novel capabilities to decrease time-to-train and enhance training quality, including adaptive paradigms and automated evaluation techniques; augmenting physical, cognitive, and social performance by adapting to the individual and context; and improving human-network interactions by providing robust predictions of human state and intent within social contexts.