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Computer Science > Computer Vision and Pattern Recognition

arXiv:1906.12213 (cs)
[Submitted on 27 Jun 2019]

Title:On the notion of number in humans and machines

Authors:Norbert Bátfai, Dávid Papp, Gergő Bogacsovics, Máté Szabó, Viktor Szilárd Simkó, Márió Bersenszki, Gergely Szabó, Lajos Kovács, Ferencz Kovács, Erik Szilveszter Varga
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:1906.12213 [cs.CV]
  (or arXiv:1906.12213v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1906.12213

Submission history

From: Norbert Bátfai Ph.D. [view email]
[v1] Thu, 27 Jun 2019 09:26:22 UTC (174 KB)
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