  In this paper, we performed two types of software experiments to study the
numerosity classification (subitizing) in humans and machines. Experiments
focus on a particular kind of task is referred to as Semantic MNIST or simply
SMNIST where the numerosity of objects placed in an image must be determined.
The experiments called SMNIST for Humans are intended to measure the capacity
of the Object File System in humans. In this type of experiment the measurement
result is in well agreement with the value known from the cognitive psychology
literature. The experiments called SMNIST for Machines serve similar purposes
but they investigate existing, well known (but originally developed for other
purpose) and under development deep learning computer programs. These
measurement results can be interpreted similar to the results from SMNIST for
Humans. The main thesis of this paper can be formulated as follows: in machines
the image classification artificial neural networks can learn to distinguish
numerosities with better accuracy when these numerosities are smaller than the
capacity of OFS in humans. Finally, we outline a conceptual framework to
investigate the notion of number in humans and machines.
