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C-DAT: The Cognitive Desktop Analysis Trainer

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-12-M-6283
Agency Tracking Number: O113-CR2-1146
Amount: $149,931.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: OSD11-CR2
Solicitation Number: 2011.3
Solicitation Year: 2011
Award Year: 2012
Award Start Date (Proposal Award Date): 2012-05-07
Award End Date (Contract End Date): N/A
Small Business Information
408 Sharts Drive Suite 7
Springboro, OH -
United States
DUNS: 601628717
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Michael McCloskey
 (937) 743-0361
Business Contact
 Michael McCloskey
Title: President
Phone: (937) 743-0361
Research Institution

The emergence of new technologies in the Air Force GEOINT analyst's arsenal of resources represents an evolutionary advance in potential real-time and forensic analysis capabilities, including more accurate and effective pattern of life development, more timely identification of insurgent networks and cells, and more accurate and targeted products for customers. But with these new capabilities come challenges. The ability to effectively exploit these datasets will be based on an analyst's abilities to understand important contextual factors of the operating environment and sift through massive datasets. Cognitively-based tools are needed to train analysts in optimally exploiting ISR datasets. We will leverage innovative simulation interview and cognitive task analysis methodologies to identify the cognitive challenges associated with conducting effective analyses in complex, uncertain environments. An analyst-centric focus will drive the development of an adaptive desktop trainer to support imagery analysts in better managing their ambiguous and time-critical tasking that flows from irregular warfare. Training modules will support the following objectives: increasing awareness of the importance of understanding contextual factors for intelligence analysis, identifying significant patterns of life, and developing effective, expert-based strategies for making sense of ambiguous analysis tasks. Such concepts will be generalizable across domains where imagery exploitation is critical.

* Information listed above is at the time of submission. *

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