Many psycholinguistic studies examine how people parse sentences in isolation; however, years of work in discourse processing have shown that sentence-level interpretations are influenced at some stage by discourse-level information. Evidence over the past 20 years remains mixed as to the temporal dynamics of such top-down interactions. In…

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences--and the relations between the elements they comprise--are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the…

Impaired procedural learning has been suggested as a possible cause of developmental dyslexia (DD) and developmental language disorder (DLD). We evaluate this theory by performing a series of meta-analyses on evidence from the six procedural learning tasks that have most commonly been used to test this theory: the serial reaction time, Hebb…

Learning sequential structures is of fundamental importance for a wide variety of human skills. While it has long been debated whether implicit sequence learning is perceptual or response-based, here we propose an alternative framework that cuts across this dichotomy and assumes that sequence learning rests on associative changes that can occur…

Knowledge of sequential relationships enables future events to be anticipated and processed efficiently. Research with the serial reaction time task (SRTT) has shown that sequence learning often occurs implicitly without effort or awareness. Here, the authors report 4 experiments that use a triplet-learning task (TLT) to investigate sequence…

Knowledge concerning domain-specific regularities in sequential structure has long been known to affect recall for serial order. However, very little work has been done toward specifying the exact role such knowledge plays. The present article proposes a theory of serial recall in structured domains, based on Bayesian decision theory and a set of…

An associative model of serial learning is described based on the assumption that the effective stimulus for a serial-list item is generated by adaptation-level coding of the item's ordinal position. How the model can generate predictions of aspects of serial-learning data is illustrated. (SLD)