The role of low-level image features in the affective categorization of rapidly presented scenes
The role of low-level image features in the affective categorization of rapidly presented scenes
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DOI：
10.1371/journal.pone.0215975
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发表时间：
2019-05
期刊：
影响因子：
3.7
通讯作者：
L. Jack Rhodes;Matthew Ríos;Jacob M. Williams;Gonzalo Quiñones;Prahalada K. Rao;V. Miskovic
中科院分区：
文献类型：
--
作者：
L. Jack Rhodes;Matthew Ríos;Jacob M. Williams;Gonzalo Quiñones;Prahalada K. Rao;V. Miskovic
目前尚不清楚视觉系统如何能够从复杂的场景中提取情感内容，即使是在极其短暂（< 100 毫秒）的曝光下。机器视觉的研究结果表明，一种可能性是，诸如非局部化二维 (2-D) 傅立叶光谱之类的低级特征可以诊断场景内容。为了确定傅里叶图像幅度是否携带有关场景情感质量的任何信息，我们首先通过支持向量机 (SVM) 模型验证了图像类别差异的存在，该模型能够使用仅幅度特征作为输入，以约 70% 的准确度区分完整的厌恶图像和中性图像。该模型使我们能够确认属于不同情感类别的场景可以仅根据幅度谱在数学上进行区分。下一个问题是人类视觉系统是否也利用了这些相同的功能。随后，我们测试了观察者对情感和中性自然场景的快速分类，简要呈现（约 33.3 毫秒）并用合成纹理向后屏蔽。我们测试了三种不同实验条件下的分类准确性，使用：（i）原始图像，（ii）在单个情感图像类别内交换振幅谱的图像（例如，其振幅谱已与另一个厌恶图像交换的厌恶图像）或（iii）在情感类别之间交换振幅谱的图像（例如，包含中性图像的振幅谱的厌恶图像）。尽管具有辨别潜力，人类视觉系统似乎并没有使用傅里叶幅度差异作为一目了然地对场景进行情感分类的主要策略。图像幅度对情感分类的贡献在很大程度上取决于与相位谱的相互作用，尽管不可能完全排除非局部二维幅度测量的残余作用。
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.