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

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.

Research highlights
► This article proposes a new index to capture segmentation in implicit sequence learning.
► We present an adapted SRT task using colors to help participants to break the sequence into chunks.
► Evidence of chunk learning is observed when the colors are consistently coupled with responses.
► Evidence of continuous, statistical learning is observed when the structuring colors are removed.
► The new index of chunk learning, based on the distribution of variances, is sensitive to these manipulations.