Using Convolutional Neural Networks to Explore The Effect of Impaired Subitizing in Dyscalculia

Dyscalculia is a learning disability that impairs a person’s ability to solve mathematics problems. One of the issues many people with dyscalculia face is an impaired ability to subitize, which refers to the ability to look at a group of objects and recognize the number of objects without counting directly. In this article, we explore the ability of a convolutional neural network (CNN) to mimic the subitizing ability of both dyscalculic and non-dyscalculic persons. We look at first whether the model follows the same pattern in accuracy for both small and larger number of objects. Since people generally subitize smaller numbers of objects (e.g., two books, three dots, etc.) better than they subitize larger ones, the CNN should follow the same pattern. To model dyscalculia, we also simulate damaged neural connections (using dropout) and compare the behavior of the damaged network to behavioral data from dyscalculic children reported in [6]. Our methodology involved the creation of custom datasets and the introduction of dropout in the CNN to simulate potential neural deficits associated with dyscalculia. This research not only underscores the potential of CNNs in cognitive modeling but also paves the way for a deeper understanding of dyscalculia and its neural underpinnings.