MAMID: Methodology for Analysis and Modeling of Individual Differences

Period of Performance: 01/04/2000 - 01/03/2002

$500K

Phase 2 STTR

Recipient Firm

Psychometrix Assoc., Inc.
8 Silver Hill Rd.
Lincoln, MA 01773
Principal Investigator

Research Institution

Northwestern University
1801 Maple Ave.
Evanston, IL 60201

Abstract

We propose a Phase II program to develop and demonstrate a Methodology for Analysis and Modeling of Individual Differences (MAMID), which provides a generic method for representing a variety of individual differences factors in human performance models. The Methodology consists of: 1) identifying cognitive, affective, and personality factors and their effects on performance; 2) defining a parameterized cognitive architecture capable of modeling these effects; 3) encoding the identified factors in terms the cognitive architecture parameters and knowledge-bases; 4) evaluating resulting model performance to drive an iterative refinement process. Feasibility of the concept was demonstrated under the Phase I effort, by implementing the methodology and architecture within a simulation and modeling environment. The environment supported the modeling of individual differences commander performance variations, determined by distinct individual differences profiles, within a battalion-level, force-on-force demonstration scenario. Under Phase II we plan to develop a full-scope MAMID cognitive architecture; and 3) enhancement of the testbed environment. We propose to investigate compliance with HLA architecture to ensure interoperability across DoD applications. Commercial applications of the generic MAMID methodology exist in a variety of settings, both individual and team, characterized by likelihood of extreme affective states and sensitivity to individual variations in performance (e.g., high-risk crisis-prone environments, training environments). In addition, the MAMID methodology is also applicable to virtual reality training environments designed to compensate for a variety of cognitive or affective biases or disabilities (e.g., persistent decision-making biases, age-related cognitive deficits, anxiety disorders, phobias, etc.).