Beyond memory capacity: A probabilistic, dual store model of visuospatial working memory.

Abstract

The last decades have seen a great improvement in our understanding of visuospatial working memory (VSWM). Despite this progress, less is known about how information is stored, retained, and removed from VSWM when novel information is presented sequentially. Here, we present a novel computational model of the dynamics of VSWM that extends and improves classical ideas. Our analysis relies on data from three clinical trials involving neurotypicals and people with autism performing a smartphone based sequential VSWM task. In addition, we applied the model to data from a large clinical trial in prodromal Alzheimer's disease. We demonstrate that visual information in our sequential task is stored in independent pools of contrasting resources, with perfect and imperfect retrieval rates. Our findings illustrate how computational models combined with remote cognitive testing are mature enough for applications in large-scale clinical research.