Object file system software experiments about the notion of number in humans and machines

Authors

Norbert Bátfai¹*, Dávid Papp², Gergő Bogacsovics¹, Máté Szabó¹, Viktor Szilárd Simkó¹, Márió Bersenszki¹, Gergely Szabó¹, Lajos Kovács¹, Ferencz Kovács¹, Erik Szilveszter Varga¹

¹Department of Information Technology, University of Debrecen, Hungary
²Department of Psychology, University of Debrecen, Hungary

Abstract

In this paper, we present two types of software experiments, which were performed to study the numerosity classification (subitizing) in humans and machines. These experiments focus on the fields of subitizing and numerosity estimation, 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 (OFS) in humans. In this type of experiment, the measurement result is well in agreement with the value indicated in cognitive psychology literature. The experiments called “SMNIST for Machines” serve similar purposes but they investigate existing, well-known deep learning computer programs that are under development (and which were originally developed for other purposes). 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.

Keywords: numerosity classification, object file system, machine learning, MNIST, esport

PAGES:257-280

doi:10.24193/cbb.2019.23.15

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