Demonstration Method In Introduce Secondary Color Learning

Early childhood education is very important for the child's future.[1] early childhood is the age of gold where the development of children into a period of development is very decisive. The mix of primary colors (Red, yellow, blue), will become secondary color, and this is generally not understood by kindergarten children. To introduce the secondary color requires a method of learning that shows directly how to obtain it, such as the method of demonstration learning. The purpose of this study is to improve the ability to recognize the secondary color in children with the method of demonstration learning. This research uses Class Action Research method. The subjects of this study were students of group B kindergarten GMIM Hosiana Tumatangtang amounted to 14 children. The results obtained through two cycles with the application of demonstration methods can improve the ability to introduce secondary color. Keywords : Introduce Secondary Color, Demonstration Method.

Modeling color naming in bilinguals: Computational mechanisms of crosslinguistic influence

When bilingual speakers name stimuli such as colors or objects, their naming patterns can differ from those of monolingual speakers. Three accounts have been proposed to explain these differences – conceptual change, online lexical coactivation, and L1 footprint – yet these have not been empirically evaluated against each other. In this study, we propose a novel computational cognitive model which operationalizes each of these proposals as a mechanism of crosslinguistic influence, such that we can study their individual and combined effects on the model’s behavior. We focus on the domain of color in which we model existing experimental data collected from Navajo and English monolinguals and Navajo–English bilinguals. Our color learning model extends a statistical learning procedure for mixture models to the acquisition of labelled categories, and achieves bilingual learning by maintaining two sets of color categories and associated color words, which are connected in varying ways according to the three crosslinguistic mechanisms. We test the combinations of mechanisms in a color naming task, and analyze the match between the naming patterns of the model and the differences between bilingual and monolingual human speakers. Our results suggest that gradual conceptual change following crosslinguistic transfer at the initial learning stage can best capture the observed differences in human color naming patterns. While lexical coactivation combined with initial transfer can account for some of the empirical data, this mechanism consistently performs less well than that of conceptual change.

Color recognition of design object of manual decoration element based on convolution neural network under the impact of COVID-19

The popularity of covid-19 has led to the introduction of the policy of controlling the flow of personnel, which has a certain impact on the color recognition of the design objects of hand decorative elements. In the past, the research on convolution neural network design and color recognition is still in the traditional method, and the field of computer vision is not really combined with the traditional decorative fabric. This paper proposes a solution based on deep learning. Color learning and main color recognition can be processed as a whole. By introducing convolution neural network, the scheme can learn color features directly from the image itself, and the process of artificial design features is omitted. While simplifying the process of model building and training, the potential information association can be obtained, so as to obtain better recognition effect. In addition, due to the deep depth of the network, this paper uses the initial optimization module to avoid the performance degradation and over fitting in the training process. This method has an important reference value for the color design of modern hand decoration, and can promote the development of hand decoration during the popularity of covid-19.