N122-144 TITLE: Technologies for Enabling Warfighter Intuitive Decision Making

TECHNOLOGY AREAS: Battlespace, Human Systems

ACQUISITION PROGRAM: Squad Immersive Training Environment (SITE) program of record

RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports): This topic is "ITAR Restricted". The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130, which control the export of defense-related material and services, including the export of sensitive technical data. Foreign Citizens may perform work under an award resulting from this topic only if they hold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.

OBJECTIVE: Develop innovative technologies that blend rapid data processing capabilities of computers with intuitive decision making skills of humans to improve human information throughput and decision making.

DESCRIPTION: Computers can process vast amounts of data in a short time but programming them to detect patterns is extremely difficult. Humans, on the other hand, are adept at extracting patterns from large amounts of data, yet are limited by the rate at which they can apply these cognitive abilities to large amounts of quickly changing information. Consequently, computers have low success rates when tasked with detecting relevant and important information embedded in complex data streams, while humans are unable to effectively process large amounts of data that are fast becoming a hallmark of today�s decision-making environments. Compounding the problem, traditional support technologies, like decision aids and visualization tools, create an artificial barrier which plays to either the strengths of computers or the strengths of their human operators, but not both.

The process through which humans quickly make sense of partial, incomplete, or rapidly presented information is known as intuition. Intuitive decision-making processes are associated with rapid recognition of patterns among incoming streams of information followed by retrieval of associated knowledge without conscious attention. The neural processes underlying intuition occur on a very rapid timescale and can be captured by non-invasive imaging technologies. Combined with cognitive and behavior-based measures of intuition, neural measures should provide significant improvement in measurement accuracy. In parallel to these discoveries, advances in bio-inspired adaptive data analysis techniques like genetic algorithmic modeling are now being used to filter and sort information presented to humans. This is achieved by first generating "hunches" around likely human responses, then using human behavioral responses to establish the plausibility of such hunches and finally, refining the information being presented through multiple iterations of this process. Consequently, it should be possible to augment the support of these bio-inspired data processing techniques by integrating into them neuro-cognitive measures of intuition, enabling humans to accurately and quickly detect meaningful information from a mass of data.

The requested effort will augment current data processing techniques by developing measures of intuition (e.g., cognitive, behavioral, and neural markers) that can be used to rapidly identify the initial saliency and relevance of information, and then refine the information presented through multiple iterations. This will enable humans to rapidly analyze large amounts of information in complex information environments. This effort will develop a suite of neuro-cognitive measures that can be reliably detected, integrate this measurement suite into decision support and analysis technologies, demonstrate increased amounts of information processed per unit time, show reduced overall decision-making time, and quantify decision-making effectiveness using signal detection theory.

PHASE I: Determine the feasibility of developing a system that will blend rapid data-processing capabilities of computers with intuitive decision-making skills of humans to improve human information throughput and decision making. The performer will propose a prototype system and a preliminary design and architecture, including descriptions of the following: proposed measures of intuition; appropriate sensor technologies for detecting these measures; and, proposed method for linking these measures to decision support and analysis technologies and the planned experimental paradigm and use case. Modeling and simulation are encouraged to guide the development of overall system design as well as to demonstrate the potential effectiveness of the proposed system. A final report will be generated, including system performance metrics and plans for Phase II. Phase II plans should include key component technological milestones and plans for at least one operational test and evaluation event. Phase I should also include the processing and submission of all required human subject use protocols.

PHASE II: Develop a prototype system based on the preliminary design from Phase I. All appropriate testing and a critical design review will be performed to finalize the design. Phase II deliverables will include: (1) a working prototype of the technology, (2) specification for its development, and (3) test data on its performance collected in one or more operational settings.

PHASE III: Deploy the developed system for use in high operations tempo environments, such as Command and Control, Maritime Operations Center or disaster management information centers.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: This technology will have broad application in military as well as commercial settings in which large quantities of information must be quickly and accurately analyzed for effective decision making in high-risk and high-stress operational settings. The military is reducing the number of personnel involved with weapons platforms and Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) systems while increasing the total amount of information that these reduced crews must manipulate. Therefore, systems that help warfighters effectively process this information are urgently needed. Similar trends are occurring in commercial sectors, where fewer personnel are tasked with processing ever-increasing amounts of information (e.g., air traffic control, commercial shipping, manufacturing facilities, power plant control systems, crisis management, and emergency management). For the DoD, this technology will provide a means for ensuring that reduced manpower does not result in reduced readiness and performance. Commercially, this technology will provide a new capability to enable fewer personnel to handle increasingly greater quantities of information across a wide range of domains.

REFERENCES:
1. Qiao, Jinyuan. 2004. "A Simple, Effective and Automatic Method to Detect Design Patterns Implementation in Java Source Code." Thesis, Dalhousie University. Accessed December 2, 2011. http://www.cs.dal.ca/news/presentations/2004-11-25-simple-effective-and-automatic-method-detect-design-patterns-implement.

2. Yang, Guangzheng, and Thomas S. Huang. 1994. "Human Face Detection in a Complex Background." Pattern Recognition 27, no. 1: 53-63. Accessed December 2, 2011. doi: 10.1016/0031-3203(94)90017-5.

3. Szalma, James L., and Peter A. Hancock. 2008. "Task Loading and Stress in Human-Computer Interaction: Theoretical Frameworks and Mitigation Strategies." Handbook for Human-Computer Interaction in Interactive Systems. (pp. 115-132), (2nd Edition). Mahwah, N.J: Erlbaum. Accessed December 2, 2011. doi: 10.1201/9781410615862.ch6.

4. Luu, Phan, Alexandra Geyer, Tracey Wheeler, Gwendolyn Campbell, Don M. Tucker, and Joseph Cohn. 2010. The Neural Dynamics and Temporal Course of Intuitive Decisions. (In Press) Public Library of Science.

5. Hodgkinson, Gerard P., Janice Langan-Fox, and Eugene Sadler-Smith. 2008. "Intuition: A Fundamental Bridging Construct in the Behavioural Sciences." British Journal of Psychology. 99, no. 1:1-27. Accessed December 2, 2011. doi: 10.1348/000712607X216666.

6. Norton, Quinn. 2006. "Software Helps Develop Hunches." Wired, March 13. Accessed December 2, 2011. http://www.wired.com/science/discoveries/news/2006/03/70388.

7. Lawrenz, Claudia, and Frank Westerhoff. 2003. "Modeling Exchange Rate Behavior with a Genetic Algorithm." Computational Economics 21, no. 3: 209-229. Accessed December 2, 2011. http://hdl.handle.net/10.1023/A:1023943726237.

8. Anthes, Gary. 2006. "Deconstructing Complexity: A family of related concepts may help companies make sense of the murky." COMPUTERWORLD, October 2. Accessed December 2, 2011. http://www.computerworld.com/action/article.do?command=viewArticleBasic&taxonomyId=18&articleId=263766intsrc=hm_topic _31 August 2010.

KEYWORDS: Intuition; Decision Making; Decision Support; Neural Imaging; Human Machine Interaction; Hunch

** TOPIC AUTHOR (TPOC) **

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