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首页> 外文期刊>Frontiers in Psychology >The China Image Set (CIS): A New Set of 551 Colored Photos With Chinese Norms for 12 Psycholinguistic Variables
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The China Image Set (CIS): A New Set of 551 Colored Photos With Chinese Norms for 12 Psycholinguistic Variables

机译:中国图像集(CIS):一套新的551彩色照片,具有12个精神语言学变量的中国规范

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Normative image sets are widely used in memory, perception, and lan- guage studies. Following the pioneering work of Snodgrass and Vanderwart (1980), a number of normalized image sets with various language norms have been created. However, original image sets that are carefully selected to accommodate Chinese cul- ture and language are still in short supply. In the present study, we provided the China Image Set (CIS), a new set of photo stimuli with Chinese norms. The CIS consists of 551 high-quality colored photo stimuli that cover 21 categories and are normalized on 12 different variables, including name agreement, category agreement, familiarity, visual complexity, object manipulability, manipulation experience, color diagnostic- ity, shape diagnosicity, image variability, age of acquisition, image agreement, and within-category typicality. Of the 12 variables, shape diagnosticity and manipulation experience with the object depicted in a stimulus are the two newly introduced and normalized variables. Multiple regression analysis reveals that name agreement, age of acquisition, image agreement, shape diagnosticity, and image variability are the most robust determinants of picture naming latency. Our normative dataset of the high- quality photo stimuli offers an ecologically more valid tool to study object recognition and language processing within Chinese culture than has previously been available.
机译:规范图像集广泛用于记忆,感知和语言研究。在Snodgrass和Vanderwart(1980)的开创性工作之后,已经创建了许多具有各种语言规范的归一化图像集。但是,仔细选择以容纳中国的CUL-TURE和语言的原始图像集仍在供不应之规。在本研究中,我们提供了中国形象集(CIS),这是一种新的照片刺激与中国规范。 CIS由551个高质量的彩色照片刺激组成,涵盖21个类别,并在12个不同的变量上标准化,包括名称协议,类别协议,熟悉程度,视觉复杂性,对象可操纵性,操纵体验,颜色诊断,形状诊断,形状诊断,形状诊断,形状诊断可变性,收购年龄,图像协议和类别内的典型程度。在12个变量中,与刺激中描绘的物体的形状诊断性和操作经验是两个新引入和归一化的变量。多元回归分析揭示了名称协议,收购年龄,图像协议,形状诊断性和图像变异性是最强大的图片命名延迟的决定因素。我们的高质量照片刺激的规范数据集提供了生态上更有效的工具,可以在中国文化中学习对象识别和语言处理而不是以前可用。

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