Cognitive Models of Training Principles and the Instance-Based Learning Tool
Date of Original Version
Abstract or Description
This chapter reviews computational representations of human behavior involving three training principles discussed in preceding chapters (especially chapters 2, 3, and 5): Speed–accuracy trade-off attributable to fatigue, training diffi culty, and stimulus-response compatibility. Effects of these three training principles were modeled using the ACT-R cognitive architecture (Anderson & Lebiere, 1998) and the instance-based learning (IBL) theory (Gonzalez, Lerch, & Lebiere, 2003). The use of similar memory principles in all three projects resulted in the implementation of an IBL tool (Dutt & Gonzalez, 2011), which provides a computational framework that facilities building computational models using ACT-R and IBL theory. The last section of this chapter summarizes the IBL tool and concludes with the benefi ts of using computational representations of learning and training principles: to develop an understanding of the learning process in a variety of tasks; to predict learning effects from training principles; and most importantly, to demonstrate the generality of computational principles and representations from the ACT-R architecture and IBL theory
Training Cognition: Optimizing Efficiency, Durability, and Generalizability, 181-200.