A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System

Humans can categorize an object in different semantic levels. For example, a dog can be categorized as an animal (superordinate), a terrestrial animal (basic), or a dog (subordinate). Recent studies have shown that the duration of stimulus presentation can affect the mechanism of categorization in t...

Full description

Bibliographic Details
Main Authors: Mohammad Hossein Karimi, Reza Ebrahimpour, Nasour Bagheri
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Computational Intelligence and Neuroscience
Online Access:http://dx.doi.org/10.1155/2021/8895579
id doaj-0f873a629ca44c1b91654f1f3464bd31
record_format Article
spelling doaj-0f873a629ca44c1b91654f1f3464bd312021-05-10T00:27:38ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52732021-01-01202110.1155/2021/8895579A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual SystemMohammad Hossein Karimi0Reza Ebrahimpour1Nasour Bagheri2Faculty of Electrical EngineeringFaculty of Computer EngineeringFaculty of Electrical EngineeringHumans can categorize an object in different semantic levels. For example, a dog can be categorized as an animal (superordinate), a terrestrial animal (basic), or a dog (subordinate). Recent studies have shown that the duration of stimulus presentation can affect the mechanism of categorization in the brain. Rapid stimulus presentation will not allow top-down influences to be applied on the visual cortex, whereas in the nonrapid, top-down influences can be established and the final result will be different. In this paper, a spiking recurrent temporal model based on the human visual system for semantic levels of categorization is introduced. We showed that the categorization problem for up-right and inverted images can be solved without taking advantage of feedback, but for the occlusion and deletion problems, top-down feedback is necessary. The proposed computational model has three feedback paths that express the effects of expectation and the perceptual task, and it is described by the type of problem that the model seeks to solve and the level of categorization. Depending on the semantic level of the asked question, the model changes its neuronal structure and connections. Another application of recursive paths is solving the expectation effect problem, that is, compensating the reduce in firing rate by the top-down influences due to the available features in the object. In addition, in this paper, a psychophysical experiment is performed and top-down influences are investigated through this experiment. In this experiment, by top-down influences, the speed and accuracy of the categorization of the subjects increased for all three categorization levels. In both the presence and absence of top-down influences, the remarkable point is the superordinate advantage.http://dx.doi.org/10.1155/2021/8895579
collection DOAJ
language English
format Article
sources DOAJ
author Mohammad Hossein Karimi
Reza Ebrahimpour
Nasour Bagheri
spellingShingle Mohammad Hossein Karimi
Reza Ebrahimpour
Nasour Bagheri
A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System
Computational Intelligence and Neuroscience
author_facet Mohammad Hossein Karimi
Reza Ebrahimpour
Nasour Bagheri
author_sort Mohammad Hossein Karimi
title A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System
title_short A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System
title_full A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System
title_fullStr A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System
title_full_unstemmed A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System
title_sort recurrent temporal model for semantic levels categorization based on human visual system
publisher Hindawi Limited
series Computational Intelligence and Neuroscience
issn 1687-5273
publishDate 2021-01-01
description Humans can categorize an object in different semantic levels. For example, a dog can be categorized as an animal (superordinate), a terrestrial animal (basic), or a dog (subordinate). Recent studies have shown that the duration of stimulus presentation can affect the mechanism of categorization in the brain. Rapid stimulus presentation will not allow top-down influences to be applied on the visual cortex, whereas in the nonrapid, top-down influences can be established and the final result will be different. In this paper, a spiking recurrent temporal model based on the human visual system for semantic levels of categorization is introduced. We showed that the categorization problem for up-right and inverted images can be solved without taking advantage of feedback, but for the occlusion and deletion problems, top-down feedback is necessary. The proposed computational model has three feedback paths that express the effects of expectation and the perceptual task, and it is described by the type of problem that the model seeks to solve and the level of categorization. Depending on the semantic level of the asked question, the model changes its neuronal structure and connections. Another application of recursive paths is solving the expectation effect problem, that is, compensating the reduce in firing rate by the top-down influences due to the available features in the object. In addition, in this paper, a psychophysical experiment is performed and top-down influences are investigated through this experiment. In this experiment, by top-down influences, the speed and accuracy of the categorization of the subjects increased for all three categorization levels. In both the presence and absence of top-down influences, the remarkable point is the superordinate advantage.
url http://dx.doi.org/10.1155/2021/8895579
work_keys_str_mv AT mohammadhosseinkarimi arecurrenttemporalmodelforsemanticlevelscategorizationbasedonhumanvisualsystem
AT rezaebrahimpour arecurrenttemporalmodelforsemanticlevelscategorizationbasedonhumanvisualsystem
AT nasourbagheri arecurrenttemporalmodelforsemanticlevelscategorizationbasedonhumanvisualsystem
AT mohammadhosseinkarimi recurrenttemporalmodelforsemanticlevelscategorizationbasedonhumanvisualsystem
AT rezaebrahimpour recurrenttemporalmodelforsemanticlevelscategorizationbasedonhumanvisualsystem
AT nasourbagheri recurrenttemporalmodelforsemanticlevelscategorizationbasedonhumanvisualsystem
_version_ 1721453679734685696