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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: 10.1167/18.10.138.
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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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