Motivation, Learning, and Performance: Developing a Detailed and Comprehensive Computational Theory (Area 1 - Learning in Formal and Informal Environments)
Abstract
This project aims to develop a comprehensive theory and a detailed computational model of how motivation (and its correlates such as anxiety, emotion, and so on) affects learning and performance. The overarching goal of the project is to produce comprehensive and detailed understanding of the relationship between motivation and learning/performance in a wide variety of circumstances. This work will investigate these issues through exploring a variety of human data (including new human experiments) and through the use of a computational cognitive architecture. Exploring human data on the basis of the Clarion cognitive architecture, this work aims for a more precise theory, which in turn enriches and substantiates the cognitive architecture. Detailed simulations based on the cognitive architecture and comparisons between human and simulation data will help to shed light on mechanisms and processes of motivation-cognition interaction in a precise and detailed way, leading to a unified theory. The major contributions of this work will include a new, comprehensive, precise, and detailed (mechanistic, process-based) theory/model that explains a wide range of human data concerning motivation-cognition interaction in a unified way. Based on this theory/model, the practical implications in terms of enhancing learning, training, and performance will also be made clear. This work may advance substantially the state of the art in the field.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 16, 2018
- Source ID
- W911NF1710236
Entities
People
- Ron Sun
Organizations
- Army Contracting Command
- Rensselaer Polytechnic Institute
- United States Army