The visual system prioritizes emotional content in natural scenes, but it is unclear whether emotional objects are systematically more salient. We compare emotional maps - created by averaging multiple manual selections of the most meaningful regions in images of negative, positive, and neutral affective valence - with saliency maps generated by Graph-Based Visual Saliency, Proto-object, and SalGAN models. We found that similarity between emotional and saliency maps is modulated by the scenes’ arousal and valence ratings: the more negative and high-arousing content, the less it was salient. Simultaneously, the negative and high-arousing content was the easiest to identify by the participants, as shown by the highest inter-individual agreement in the selections. Our results support the “affective gap” hypothesis, i.e., decoupling of emotional meaning from image’s formal features. The Emotional Maps Database created for this study, proven useful in gaze fixation prediction, is available online for scientific use.

视觉系统优先考虑自然场景中的情感内容，但尚不清楚情感对象是否系统地更显着。我们将情感图（通过对负、正和中性情感效价图像中最有意义区域的多次手动选择进行平均创建）与基于图形的视觉显着性、原型对象和 SalGAN 模型生成的显着性图进行比较。我们发现情绪图和显着性图之间的相似性受场景的唤醒度和效价评级的调节：负面和高唤醒度的内容越多，它的显着性就越低。同时，消极和高唤起的内容最容易被参与者识别，如选择中最高的个体间一致性所示。我们的结果支持“情感差距”假设，即，情感意义与图像形式特征的分离。为这项研究创建的情感地图数据库已被证明可用于注视注视预测，可在线用于科学用途。