MURI: Training Knowledge and Skills for the Networked Battlefield ARO Award No. W9112NF Alice Healy and Lyle Bourne, Principal Investigators Benjamin Clegg, Bengt Fornberg, Cleotilde Gonzalez, Eric Heggestad, Ronald Laughery, Robert Proctor, Co-Investigators

Project goals As defined in the MURI proposal executive summary Construct a theoretical and empirical framework for training that can: 1.Predict the effectiveness of different training methods for military tasks; 2.Point to ways to optimize training for specific tasks.

Technical approach Work will proceed in three interrelated efforts 1.Experimentation and training principle development Breadth, interaction, and generality of training principles Acquisition and retention of basic skill components Effects of levels of automation, individual differences, and team environments on performance 2.Multi-dimensional taxonomic analysis Analyze task types, training methods, performance measures Using training principles, relate task training to performance 3.Computational modeling Derive from data and training principles Monitor and assess for performance and reliability Use to extend the research findings to militarily relevant tasks

MURI personnel and their project roles Overview and Coordination CU, Alice Healy, Principal investigator Lyle Bourne, Co-principal investigator 1. Experimentation/Principles Training Principles inCU, Healy and Bourne simple and complex tasks Basic Components of SkillPurdue, Robert Proctor, Co-investigator Levels of automation, CSU, Benjamin Clegg, Co-investigator individual differences, Eric Heggestad, Co-investigator and team environments 2. Taxonomies CU, William Raymond, Research Associate 3. Modeling ACT-RCMU, Cleotilde Gonzalez, Co-investigator IMPRINTCU, Ron Laughery, Co-investigator Model AssessmentCU, Bengt Fornberg, Co-investigator

Experimentation and training principle development Breadth, interaction, and generality of training principles - Generality of principles across tasks: “Strategic use of knowledge” - Multiple principles in a task: serial position, list length, and chunking effects - Principles in complex, dynamic environments: “Training difficulty hypothesis” Acquisition and retention of basic skill components -Stimulus-response compatibility in response selection -Transfer of response associations to different tasks or environments Effects of levels of automation, individual differences, and team environments on performance -Impact of levels of automation on performance -Interaction of automation level with individual ability (task and transfer)

Taxonomic analysis Task types -Feature decomposition for cognitive tasks, aligned with IMPRINT “taxons” -Model of data entry Training methods -Developing taxonomy from experiments, needed military training coverage Performance measures - Developing taxonomy from prior research Training principles -30 training principles summarized -Aligning training principles with other dimensions

Computational modeling Cognitive modeling using ATC-R -Cognitive model of data entry task -Model interpretations for observed phenomena -Model predictions for optimizing skill retention IMPRINT modeling -Researchers being trained in modeling with IMPRINT Model assessment -IMPRINT and ACT-R suited for intended modeling purposes -“Data mining” (e.g., using radial basis functions) may also be useful