The Cognitive Benefit of Dynamic Representations on Procedural Skill Acquisition: A Computational Modeling Approach

Abstract

Cognitive computational modeling is a viable methodology for further investigation of the hitherto inconclusive findings on the cognitive benefits of dynamic versus static visualization components of instructions. This is more so as contemporary cognitive architectures such as the Adaptive Control of Thought–Rational (ACT–R) 6.0 are increasingly applied to traditional cognitive psychology research problems. The application of this methodology is, however, restricted by the limited capability of existing architectures for implementing detailed atomic motor actions such as those involved in complex skill acquisition and performance. This article presents a 2-component computational modeling methodology for investigating the cognitive processes involved in the acquisition and performance of skilled motor tasks. The approach specifies a novel combination of a sequence-of-point technique with a movement control mechanism to implement variously acquired cognitive mental task representations and their intertwined role in postlearning performance as evident in the atomic control of motor actions. This paradigm is validated for 2 experiments using incrementally developed cognitive models developed in ACT–R 6.0. The model's quantitative outputs correlate significantly with equivalent empirical human data. This has implications for multimedia instructional design, especially where rapid, transferrable skill acquisition is desired on initial exposure.

ACKNOWLEDGEMENTS

We thank Gareth Watson, Joe Butterfield, Ricky Curran, and Cathy Craig for granting access to the experimental materials and empirical data referenced in Experiment 2 of this study.

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