Physiological-based tools for virtual environment fidelity design guidance

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,856.00
Award Year:
2007
Program:
STTR
Phase:
Phase I
Contract:
N00014-07-M-0315
Award Id:
83462
Agency Tracking Number:
N074-028-0345
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1221 E. Broadway, Suite 110, Oviedo, FL, 32765
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
075104708
Principal Investigator:
Kelly Hale
Director, Human Systems Integration
(407) 706-0977
Kelly@designinteractive.net
Business Contact:
John Stanney
CFO
(407) 706-0977
john@designinteractive.net
Research Institution:
UNIV. OF CENTRAL FLORIDA
Christopher Geiger
IEMS
PO Box 162993
Orlando, FL, 32816 2993
(407) 823-0221
Nonprofit college or university
Abstract
While early theory suggested training systems would only transfer knowledge and skills to the degree that they were similar to the real world, recent research suggests required fidelity of training systems is dependent upon skills and behaviors associated with targeted training objectives -- fidelity of cues not associated with those objectives can be decreased without affecting training transfer. The Training Effectiveness Evaluation with Neurophysiological Metrics: Fidelity Assessment of VE Training Systems (TEE-FAST) framework herein proposed will build upon current theoretical frameworks for assessing VE system fidelity. The TEE-FAST framework will be grounded in a contextually-rich task analysis to derive training objectives, cue and cue fidelity experienced in the real work, and will incorporate behavioral and physiological measures to empirically evaluate operator performance and experience in a VE compared to the operational environment. Phase I work will collect cue fidelity information from operational and VE, identify physiological markers that encapsulate affective responses, develop the TEE-FAST framework to drive fidelity evaluations, and develop initial algorithms that will 1) compare the degree that there are theoretical mismatches, 2) compare the degree that there are response mismatches, and 3) identify potential causes of response mismatches and provide system design guidance to decrease the mismatch.

* information listed above is at the time of submission.

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