How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task

Sequence learning in the serial response time task (SRTT) is one of the few learning phenomena that are widely agreed to be implicit in nature (i.e., that learning may proceed in the absence of awareness). Evidently, it is also possible to explicitly learn a sequence of events. In the past few decades, research into sequence learning largely focused on the type of representation that may underlie implicit sequence learning, and whether or not two independent learning systems are necessary to explain qualitative differences between implicit and explicit learning. Using the drift-diffusion model, here we take a cognitive-processes perspective on sequence learning and investigate the cognitive operations that benefit from implicit and explicit sequence learning (e.g., stimulus detection or encoding, response selection, or response facilitation). To separate the processes involved in expressing implicit versus explicit knowledge, we manipulated explicit sequence knowledge independently of the opportunity to express such knowledge. Performance data from this experiment was fed into a dynamic drift-diffusion model that allowed us to disentangle the contributions of the above-mentioned processes to sequence-learning effects. Results revealed three findings that are relevant for sequence learning theories. We discuss how the diffusion model may be helpful in future research addressing these theories.