Online incidental statistical learning of audiovisual word sequences in adults: a registered report

Working, Declarative, and Procedural Memory in Children With Developmental Language Disorder

Purpose Previous research into the working, declarative, and procedural memory systems in children with developmental language disorder (DLD) has yielded inconsistent results. The purpose of this research was to profile these memory systems in children with DLD and their typically developing peers. Method One hundred four 5- to 8-year-old children participated in the study. Fifty had DLD, and 54 were typically developing. Aspects of the working memory system (verbal short-term memory, verbal working memory, and visual-spatial short-term memory) were assessed using a nonword repetition test and subtests from the Working Memory Test Battery for Children. Verbal and visual-spatial declarative memory were measured using the Children's Memory Scale, and an audiovisual serial reaction time task was used to evaluate procedural memory. Results The children with DLD demonstrated significant impairments in verbal short-term and working memory, visual-spatial short-term memory, verbal declarative memory, and procedural memory. However, verbal declarative memory and procedural memory were no longer impaired after controlling for working memory and nonverbal IQ. Declarative memory for visual-spatial information was unimpaired. Conclusions These findings indicate that children with DLD have deficits in the working memory system. While verbal declarative memory and procedural memory also appear to be impaired, these deficits could largely be accounted for by working memory skills. The results have implications for our understanding of the cognitive processes underlying language impairment in the DLD population; however, further investigation of the relationships between the memory systems is required using tasks that measure learning over long-term intervals. Supplemental Material https://doi.org/10.23641/asha.13250180.

What exactly is learned in visual statistical learning? Insights from Bayesian modeling

It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability.