Progress Toward Adaptive Integration and Optimization of Automated and Neural Processing Systems: Establishing Neural and Behavioral Benchmarks of Optimized Performance

Report No. ARL-TR-7147
Authors: Anthony J Ries, Laurie Gibson; Jon Touryan; Kaleb McDowell; Hubert Cecotii; Barry Giesbrecht
Date/Pages: November 2014; 42 pages
Abstract: Technical advances intended to improve situational awareness by providing more information about the tactical environment place high demands on the Soldier's limited capacity cognitive and neural systems. Information display technologies have been developed that filter information to prevent performance failures due to information overload. However, these technologies are typically rigid with respect to changes in the operator's physical and cognitive state. The objective of the project described in this report is to develop an adaptive framework that adjusts filtering algorithms to optimize human performance in a variety of operational contexts. The work adopts a unique approach that integrates measures of behavior and brain activity with automated information processing and display algorithms. It leverages basic science research conducted at the Institute for Collaborative Biotechnologies that uses machine learning algorithms to detect performance failures during difficult attentional tasks based on brain activity, work done at Science Applications International Corporation using pattern classification algorithms to detect threats based on brain activity, and work done at the US Army Research Laboratory aimed at understanding the cognitive constraints on performance in crew stations.
Distribution: Approved for public release
  Download Report ( 1.396 MBytes )
If you are visually impaired or need a physical copy of this report, please visit and contact DTIC.

Last Update / Reviewed: November 1, 2014