However, emotions are transient states of organisms with relatively short duration, some insurmountable limitations of textual scales have been reported, including low reliability for single measurement or susceptibility to learning effects for multiple repeated use. Numerous studies have extensively employed textual scales for psychological and organizational behavior research. We also observed that the learnt embedding space is easier to interpret in colour opponent models.Įmotion measurement is crucial to conducting emotion research. Our results show, with respect to the baseline network (whose input and output are RGB), colour conversion to decorrelated spaces obtains 1-2 Delta-E lower colour difference and 5-10% higher classification accuracy. We conducted experiments in three benchmark datasets: ImageNet, COCO and CelebA. We further evaluated the quality of reconstructed images at low-level using pixel-wise colour metrics, and at high-level by inputting them to image classification and scene segmentation networks. Our analysis suggests that certain vectors encode hue and others luminance information. We examined the finite embedding space of trained networks in order to disentangle the colour representation in VQ-VAE models. from RGB to CIE L*a*b* (in total five colour spaces were considered). To this end, we trained several instances of VQ-VAE whose input is an image in one colour space, and its output in another, e.g. In this article, we propose colour space conversion, a simple quasi-unsupervised task, to enforce a network learning structured representations. CIE L*a*b* decorrelates chromaticity into opponent axes. While images are often represented in RGB colour space, the specific organisation of colours in other spaces also offer interesting features, e.g. Vector quantised variational autoencoders (VQ-VAE) are characterised by three main components: 1) encoding visual data, 2) assigning $k$ different vectors in the so-called embedding space, and 3) decoding the learnt features. The results can be accounted for within a multiple memory systems framework. Finally, the recognition memory advantage disappears for falsely colored images of natural scenes: The improvement in recognition memory depends on the color congruence of presented images with learned knowledge about the color gamut found within natural scenes. Experiments 4 and 5 exclude the possibility that the superior recognition memory for colored images results solely from attentional factors or saliency. Experiment 2 indicated little influence of contrast once the images were suprathreshold, and Experiment 3 revealed that performance worsened when images were presented in color and tested in black and white, or vice versa, leading to the conclusion that the surface property color is part of the memory representation. Subjects performed 5%-10% better for colored than for black-and-white images independent of exposure duration. The authors used a recognition memory paradigm to assess the influence of color information on visual memory for images of natural scenes.
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