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Improving Visuomotor Control of a Cognitive Architecture

Ms. Grace Roessling
Rensselaer Polytechnic Institute ~ Cognitive Science
Dr. Tim Halverson
Aptima, Inc. ~ Performance Assessment Technologies Division
Dr. Chris Myers
Air Force Research Laboratory ~ Cognitive Science, Models, & Agents

Symbolic/hybrid computational cognitive architectures, including the ACT-R framework, are adept at capturing a wide variety of human cognitive processes and behaviors including problem-solving, memory, and language. However, such cognitive architectures do not capture visuomotor behaviors that tightly couple perceptual and motor processes – such as manual tracking. In this study, we aimed to improve the cognitive fidelity of manual tracking behavior within the ACT-R framework by implementing the position control model (PCM) – a continuous, linear control model that effectively captures human tracking behavior (Powers, 1978). We integrated PCM within a MATB task model developed within the ACT-R framework, to examine if the integrated ACT-R/PCM model showed improvement in capturing human tracking performance relative to the Standard ACT-R model. Results indicate that the ACT-R/PCM Integrated model showed improved performance in capturing certain aspects of human tracking behavior, in comparison to the Standard ACT-R model.



manual tracking
visuomotor behavior
Fitt’s law
perceptual control theory
linear control model

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Cite this as:

Roessling, G. C., Halverson, T., & Myers, C. (2023, July). Improving Visuomotor Control of a Cognitive Architecture. Paper presented at MathPsych/ICCM/EMPG 2023. Via