Improving Visuomotor Control of a Cognitive Architecture

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.