The visual system has a remarkable ability to extract categorical information from complex natural scenes. In order to elucidate the role of low-level image features for the recognition of objects in natural scenes, we recorded saccadic eye movements and event-related potentials (ERPs) in two experiments, in which human subjects had to detect animals in previously unseen natural images. We used a new natural image database (ANID) that is free of some of the potential artifacts that have plagued the widely used COREL images. Color and grayscale images picked from the ANID and COREL databases were used. In all experiments, color images induced a greater N1 EEG component at earlier time points than grayscale images. We suggest that this influence of color in animal detection may be masked by later processes when measuring reation times. The ERP results of googo and forced choice tasks were similar to those reported earlier. The non-animal stimuli induced bigger N1 than animal stimuli both in the COREL and ANID databases. This result indicates ultra-fast processing of animal images is possible irrespective of the particular database. With the ANID images, the difference between color and grayscale images is more pronounced than with the COREL images. The earlier use of the COREL images might have led to an underestimation of the contribution of color. Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used.
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机译:视觉系统具有从复杂自然场景中提取分类信息的出色能力。为了阐明低级图像特征在自然场景中识别物体的作用,我们在两个实验中记录了眼跳运动和与事件相关的电位(ERP),其中人类受试者必须检测以前看不见的自然动物图片。我们使用了一个新的自然图像数据库(ANID),该数据库不含困扰了广泛使用的COREL图像的某些潜在伪像。使用从ANID和COREL数据库中选取的彩色和灰度图像。在所有实验中,彩色图像在比灰度图像更早的时间点上诱导出更大的N1 EEG分量。我们建议,在测量反应时间时,这种颜色在动物检测中的影响可能会被以后的过程所掩盖。 go / nogo和强制选择任务的ERP结果与之前报道的相似。在COREL和ANID数据库中,非动物刺激均比动物刺激诱导更大的N1。该结果表明,不管特定的数据库如何,都可以对动物图像进行超快速处理。使用ANID图像时,彩色图像和灰度图像之间的差异比使用COREL图像时更为明显。早期使用COREL图像可能导致低估了颜色的贡献。因此,我们得出的结论是,与其他常用数据库相比,ANID图像数据库更适合于调查自然场景。
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