The Dynamic-Processing Model of Working Memory

Recent shifts in the understanding of how the mind and brain retain information in working memory (WM) call for revision to traditional theories. Evidence of dynamic, "activity-silent," short-term retention processes diverges from conventional models positing that information is always retained in WM by sustained neural activity in buffers. Such evidence comes from machine-learning methods that can decode patterns of brain activity and the simultaneous administration of transcranial magnetic stimulation (TMS) to causally manipulate brain activity in specific areas and time points. TMS can "ping" brain areas to both reactivate latent representations retained in WM and affect memory performance. On the basis of these findings, I argue for a supplement to sustained retention mechanisms. Brain-decoding methods also reveal that dynamic levels of representational codes are retained in WM, and these vary according to task context, from perceptual (sensory) codes in posterior areas to abstract, recoded representations distributed across frontoparietal regions. A dynamic-processing model of WM is advanced to account for the overall pattern of results.