September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Identifying Diagnostic Features in Rapid Affective Image Categorization
Author Affiliations
  • L. Jack Rhodes
    Department of Psychology, SUNY Binghamton
  • Matthew Ríos
    Department of Psychology, SUNY Binghamton
  • Jacob Williams
    Computer Science and Engineering, University of Nebraska-Lincoln
  • Gonzalo Quinones
    Department of Psychology, SUNY Binghamton
  • Prahalada Rao
    Mechanical and Materials Engineering, University of Nebraska-Lincoln
  • Vladimir Miskovic
    Department of Psychology, SUNY Binghamton
Journal of Vision September 2018, Vol.18, 138. doi:
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      L. Jack Rhodes, Matthew Ríos, Jacob Williams, Gonzalo Quinones, Prahalada Rao, Vladimir Miskovic; Identifying Diagnostic Features in Rapid Affective Image Categorization. Journal of Vision 2018;18(10):138. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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We assayed the contributions of image Fourier amplitude spectra (AS) and color in two experiments focusing on rapid categorization of affective versus neutral natural scenes. Focusing on the initial feed-forward sweep of activation through the visual hierarchy, we used briefly flashed (~33 ms) scenes that were immediately backward masked with visual textures. Previous studies hint that low-level AS information might guide rapid detection of some image categories (e.g., human faces). In Experiment 1, we used a method developed by Gaspar and Rousselet (2009) to determine whether AS information is used in image categorization, running 3 separate groups: (i) original images, (ii) images with AS information swapped within category and (iii) images with AS swapping between category. A linear support vector machine (SVM) using AS information only was able to discriminate aversive vs. neutral images with ~70% accuracy. Findings from human observers indicate that AS information contributes to affective image categorization only insofar as it destroys image amplitude-phase interactions. In Experiment 2, we focused on the role of color for rapid affective image categorization. Trichromacy provides putative advantages in food detection, detection of social cues in red-skinned conspecifics, enhanced edge and object parsing ability, and enhanced memory encoding and retrieval for some (color-diagnostic) objects. Participants viewed affective and neutral natural scenes either in (i) true color, (ii) red-green (R-G) inverted false color, (iii) blue-yellow (B-Y) inverted false color or (iv) monochromatic viewing conditions. Accuracy findings suggest that false color (particularly R-G inversion) and monochromatic images impaired performance for emotional but not for neutral content, suggesting that chromatic information may help guide affective image categorization.

Meeting abstract presented at VSS 2018


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