It is now well-established that long-term memory (LTM) knowledge, such as semantic knowledge, supports the temporary maintenance of verbal information in working memory (WM). This is for instance characterized by the recall advantage observed for semantically related (e.g. leaf - tree - branch) over unrelated (e.g. mouse - wall - sky) lists of items in immediate serial recall tasks. However, the exact mechanisms underlying this semantic contribution remain unknown. In this study, we demonstrate through a convergent approach involving computational and behavioral methods that semantic knowledge can be efficiently used to save attentional WM resources, thereby enhancing the maintenance of subsequent to-be-remembered items. These results have critical theoretical implications, and support models considering that WM relies on temporary activation within the LTM system.

Are mental additions of single-digit numbers solved through direct retrieval from long-term memory or through persistent use of an automatized counting procedure along a mental line? In this paper, we present an experiment based on small additions along an artificial mental line, which tends to show that for very small addends (+2 to +4), counting may still be used at the end of a 3-week training. To investigate this issue, we developed the AutoCoP computational model, which describes how small additions could be solved, based on attention, working memory and experience. The simulations of AutoCoP based on this experiment showed that the effects detected at the behavioral level are reproduced and consistent with the theory, which assumes the use of a counting procedure in experts.