Information Salience

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$99,841.00
Award Year:
2012
Program:
STTR
Phase:
Phase I
Contract:
N00014-12-M-0396
Agency Tracking Number:
O11B-TD1-4004
Solicitation Year:
2011
Solicitation Topic Code:
OSD11-TD1
Solicitation Number:
2011.B
Small Business Information
Discerning Technologies, LLC
302 Washington St., Suite 630, San Diego, CA, 92103-2110
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
962759143
Principal Investigator:
Joseph Lappin
Co-Founder&COO
(941) 323-5489
JSL@DiscernTek.com
Business Contact:
R. Templeton
Co-Founder&CEO
(619) 297-7311
RMT@DiscernTek.com
Research Institution:
Vanderbilt University
John Childress
2301 Vanderbilt Place
PMB 407749
Nashville, TN, 37235-7749
(615) 322-3977
Nonprofit college or university
Abstract
The Department of Defense has growing needs to identify and understand methods for determining information value as a basis for future decision support systems. Understanding how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant or of unknown relevance. Recent research on human perception and cognition, along with research in information science regarding context-based modeling, provides us with a theoretical basis for developing automated systems for managing large amounts of information and enhancing the human recognition of salient information and facilitating its use by human planners and decision makers. A formalized representation of human perception and cognition is a first step toward the goal of bridging from cognitive science to computer science in this multi-disciplinary research topic. Once this is accomplished, it should be tractable to apply information contextual models and other techniques leading to development of generalized models and subsequent automation of human cognition-like processes. This STTR Phase I effort will focus on the development of an empirical and then a mathematical framework for representing human perception and cognition, and the correlation of empirical and mathematical approaches using a representative data set.

* information listed above is at the time of submission.

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