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The role of low-level image features in the affective categorization of rapidly presented scenes
It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (< 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494199/ https://www.ncbi.nlm.nih.gov/pubmed/31042739 http://dx.doi.org/10.1371/journal.pone.0215975 |
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author | Rhodes, L. Jack Ríos, Matthew Williams, Jacob Quiñones, Gonzalo Rao, Prahalada K. Miskovic, Vladimir |
author_facet | Rhodes, L. Jack Ríos, Matthew Williams, Jacob Quiñones, Gonzalo Rao, Prahalada K. Miskovic, Vladimir |
author_sort | Rhodes, L. Jack |
collection | PubMed |
description | It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (< 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectra can be diagnostic of scene content. To determine whether Fourier image amplitude carries any information about the affective quality of scenes, we first validated the existence of image category differences through a support vector machine (SVM) model that was able to discriminate our intact aversive and neutral images with ~ 70% accuracy using amplitude-only features as inputs. This model allowed us to confirm that scenes belonging to different affective categories could be mathematically distinguished on the basis of amplitude spectra alone. The next question is whether these same features are also exploited by the human visual system. Subsequently, we tested observers’ rapid classification of affective and neutral naturalistic scenes, presented briefly (~33.3 ms) and backward masked with synthetic textures. We tested categorization accuracy across three distinct experimental conditions, using: (i) original images, (ii) images having their amplitude spectra swapped within a single affective image category (e.g., an aversive image whose amplitude spectrum has been swapped with another aversive image) or (iii) images having their amplitude spectra swapped between affective categories (e.g., an aversive image containing the amplitude spectrum of a neutral image). Despite its discriminative potential, the human visual system does not seem to use Fourier amplitude differences as the chief strategy for affectively categorizing scenes at a glance. The contribution of image amplitude to affective categorization is largely dependent on interactions with the phase spectrum, although it is impossible to completely rule out a residual role for unlocalized 2-D amplitude measures. |
format | Online Article Text |
id | pubmed-6494199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64941992019-05-17 The role of low-level image features in the affective categorization of rapidly presented scenes Rhodes, L. Jack Ríos, Matthew Williams, Jacob Quiñones, Gonzalo Rao, Prahalada K. Miskovic, Vladimir PLoS One Research Article It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (< 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectra can be diagnostic of scene content. To determine whether Fourier image amplitude carries any information about the affective quality of scenes, we first validated the existence of image category differences through a support vector machine (SVM) model that was able to discriminate our intact aversive and neutral images with ~ 70% accuracy using amplitude-only features as inputs. This model allowed us to confirm that scenes belonging to different affective categories could be mathematically distinguished on the basis of amplitude spectra alone. The next question is whether these same features are also exploited by the human visual system. Subsequently, we tested observers’ rapid classification of affective and neutral naturalistic scenes, presented briefly (~33.3 ms) and backward masked with synthetic textures. We tested categorization accuracy across three distinct experimental conditions, using: (i) original images, (ii) images having their amplitude spectra swapped within a single affective image category (e.g., an aversive image whose amplitude spectrum has been swapped with another aversive image) or (iii) images having their amplitude spectra swapped between affective categories (e.g., an aversive image containing the amplitude spectrum of a neutral image). Despite its discriminative potential, the human visual system does not seem to use Fourier amplitude differences as the chief strategy for affectively categorizing scenes at a glance. The contribution of image amplitude to affective categorization is largely dependent on interactions with the phase spectrum, although it is impossible to completely rule out a residual role for unlocalized 2-D amplitude measures. Public Library of Science 2019-05-01 /pmc/articles/PMC6494199/ /pubmed/31042739 http://dx.doi.org/10.1371/journal.pone.0215975 Text en © 2019 Rhodes et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rhodes, L. Jack Ríos, Matthew Williams, Jacob Quiñones, Gonzalo Rao, Prahalada K. Miskovic, Vladimir The role of low-level image features in the affective categorization of rapidly presented scenes |
title | The role of low-level image features in the affective categorization of rapidly presented scenes |
title_full | The role of low-level image features in the affective categorization of rapidly presented scenes |
title_fullStr | The role of low-level image features in the affective categorization of rapidly presented scenes |
title_full_unstemmed | The role of low-level image features in the affective categorization of rapidly presented scenes |
title_short | The role of low-level image features in the affective categorization of rapidly presented scenes |
title_sort | role of low-level image features in the affective categorization of rapidly presented scenes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494199/ https://www.ncbi.nlm.nih.gov/pubmed/31042739 http://dx.doi.org/10.1371/journal.pone.0215975 |
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