Chunking by colors: Assessing discrete learning in a continuous serial reaction-time task

Abstract

Chunk learning (the process by which a sequence is learned and retrieved from memory in smaller, decomposed units of information) has been postulated as the main learning mechanism underlying sequence learning (Perruchet & Pacton, 2006). However, the evidence for chunk formation has been elusive in the continuous serial reaction-time task, whereas other continuous, statistical processes of learning account well for the results observed in this task. This article proposes a new index to capture segmentation in learning, based on the variance of responding to different parts of a sequence. We assess the validity of this measure by comparing performance in a control group with that of another group in which color codes were used to induce a uniform segmentation. Results showed that evidence of chunking was obtained when the color codes were consistently coupled to responses, but that chunking was not maintained after the colors were removed.