Scaling Up High-Fidelity Cognitive Modeling to Real-World Applications
Abstract
The approach presented in this paper addresses the question of the proper scientific basis for Human Factors modeling and proposes an architectural framework for integrating federated models and simulations. It is intended to be applicable to a broad range of scenarios across domains. Indeed, broad applicability and integration of modeling and simulation techniques are essential to their effectiveness and validation. Our approach is grounded in the concept of unified theories of cognition, implemented computationally as cognitive architectures. However, cognitive architectures also need to be constrained by our knowledge of neural processes in order to properly account for all cognitive, perceptual and motor factors. Despite that attention to the small-scale basis of cognition, cognitive modeling can scale up to social situations and large-scale network settings through a process of abstraction and integration. Key to that process is the availability of easily accessible resources in the form of existing cognitive models, implemented tasks, simulation environments adhering to a common standard, and human performance data to constrain and validate models. Investment in that infrastructure is essential to ensure growth and scalability in the application of cognitive models and their proper integration in military simulations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2010
- Accession Number
- ADA590893
Entities
People
- Andrea Stocco
- Christian J. Lebiere
- David Reitter
- Ion Juvina
Organizations
- Carnegie Mellon University