Predicted Architectural Differences via Systems Factorial Technology Across Category Learning Tasks

The brain has an innate ability to group sensory information together into discriminable categories. Therefore the ability to create these categories and understand the cognitive mechanisms behind their creation is of great interest to cognitive psychologists. There are three classic category learning tasks: rule-based, information-integration, and prototype. We investigate architectural differences in visual and decisional processing across these category learning task. The theoretical approach used to address this architectural question has been referred to as Systems Factorial Technology which is a set of analytic tools that makes use of response time patterns to distinguish serial, parallel, and other cognitive mechanisms. In this presentation we explore SFT predictions across the three category learning tasks.