Replicating the stroop effect using a developmental spatial neuroevolution system

Abstract

We present an approach to the study of cognitive phenomena by using evolutionary computation. To this end we use a spatial, developmental, neuroevolution system. We use our system to evolve ANNs to perform simple abstractions of the cognitive tasks of color perception and color reading. We define these tasks to explore the nature of the Stroop effect. We show that we can evolve it to perform a variety of cognitive tasks, and also that evolved networks exhibit complex interference behavior when dealing with multiple tasks and incongruent data. We also show that this interference behavior can be manipulated by changing the learning parameters, a method that we successfully use to create a Stroop like interference pattern.