loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Spatial Simulation of the Müller-Lyer Illusion Genesis with Convolutional Neural Networks

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Bio-inspired models and solutions; Computational Neuroscience; Convolutional Neural Networks; Deep Learning

Authors: Anton Mamaev and Ivan Gorbunov

Affiliation: Department of Psychology, St. Petersburg University, Makarova Embarkment 6, Saint Petersburg, Russian Federation

Keyword(s): Optical Illusions, Depth Perception, Image-source Relationships, Computational Cognitive Science, Computer Vision, Bayesian Statistics, Regression Problem.

Abstract: The Müller-Lyer illusion is a well-known optical phenomenon with several competing explanations. In the current study we reviewed the illusion in a convolutional neural network from a perspective of image-source relationships in the process of visual functions development. To recreate the effect of the illusion we proposed a novel method that lets us simulate the development of visual functions in a controlled spatial environment from the state of ‘blank slate’ to effective spatial problem solving. This process is designed to reflect the development of human visual system and enable us to determine how depth perception can contribute to the appearance of the phenomenon. We were able to successfully reproduce the effect of the classic Müller-Lyer in 30 independent convolutional models and also get similar results with the variants of the illusion that are thought to be unrelated to spatial perception. For the pairs of classic stimuli we conducted additional statistical analysis using both frequentist and Bayesian methods. The methodological and empirical insights of this study may be helpful for subsequent investigation of visual cognition and reconsideration of the image-source relationships in optical illusions. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.224.215.101

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Mamaev, A. and Gorbunov, I. (2022). Spatial Simulation of the Müller-Lyer Illusion Genesis with Convolutional Neural Networks. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 284-291. DOI: 10.5220/0011529100003332

@conference{ncta22,
author={Anton Mamaev. and Ivan Gorbunov.},
title={Spatial Simulation of the Müller-Lyer Illusion Genesis with Convolutional Neural Networks},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA},
year={2022},
pages={284-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011529100003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA
TI - Spatial Simulation of the Müller-Lyer Illusion Genesis with Convolutional Neural Networks
SN - 978-989-758-611-8
IS - 2184-3236
AU - Mamaev, A.
AU - Gorbunov, I.
PY - 2022
SP - 284
EP - 291
DO - 10.5220/0011529100003332
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic