心理表征的可视化途径：基于噪音的反向相关图像分类技术

doi:10.3724/SP.J.1042.2019.00465

摘要/Abstract

摘要：

社会心理学对图像的心理表征研究一直难以将心理活动的内容准确刻画出来。近10年来出现了一种新心理物理学方法——“反向相关图像分类技术”, 该技术假定观察者的反应与视觉噪音存在相关关系, 且反应是依照观察者的社会判断标准进行而非随机做出; 通过对其做出反应的相应噪音模式的足够次数的权重计算与视觉代码显现, 从而将观察者内在的评估特点可视化。该技术已在特质研究、种族和群际偏见等领域取得了一些成果, 但是未来仍需解决实验次数过多, 分离混杂的噪音以及被试的表现等问题, 才能获得更为真实的心理表征。

关键词: 面孔, 心理表征, 反向相关图像分类技术

Abstract:

Studies of the mental representation of images in social psychology have encountered difficulty in accurately portraying psychological activity. Over the past decade, reverse correlation image classification has emerged as a new psychophysical method that assumes there is a relationship between an observer’s response and visual noise, and that the response is based on the observer’s social judgment criteria, and are not random. Performing a sufficient number of weight calculations on the corresponding noise patterns of the observer’s reaction allows us to visualize the intrinsic evaluation characteristics of the observer. The use of reverse correlation image classification technology has achieved some results in the areas of trait research, ethnicity, and intergroup bias. In the future, however, it is necessary to solve the problems of excessive experimental trials, separation of mixed noise, and subjects’ performance, in order to achieve more realistic mental representations.

Key words: face, mental representation, reverse correlation image classification technology

中图分类号:

B841

侯春娜, 刘志军. (2019). 心理表征的可视化途径：基于噪音的反向相关图像分类技术. 心理科学进展 , 27(3), 465-474.

HOU Chun-Na, LIU Zhi-Jun. (2019). Visualization of mental representation: Noise-based reverse correlation image classification technology. Advances in Psychological Science, 27(3), 465-474.

图/表 4

图1 RCIC生成表征图像与聚类分析示意图注：(1)生成表征图像将被试多次trials中所选择出的噪音模式通过权重计算并进行高斯平滑过滤得到CI图像, 将其覆盖在多张面孔平均合成的基本面孔(Base face)上, 从而获得表征图像; (2)聚类分析将CI图像卷摺过滤(Filter)处理后与面孔有效区域(Mask)合成并进行标准化(Field map), 再依照RFT理论将超过固定阈值点进行簇聚, 将结果覆盖在Base face上得到诊断区域。 Base face来自侯春娜(2017); RFT处理结果来自刘志军(2017)。

图2 随机噪音生成示意图注：生成随机噪音：(1)以2个周期截断的正弦曲线图像片段在6个不同朝向(0°, 30°, 60°, 90°, 120°和150°)×2个相位(0, π/2)的12种图像变化融合成噪音单元; (2)将之依照5种空间尺度(2, 4, 8, 16和32个周期)进行排列, 形成包含4092个正弦函数参数的随机噪音图像。文献来源：Mangini和Biederman (2004)

图3 双图片迫选任务研究范式示意图注：双图片迫选任务范式(2IFC)：(1)双图片刺激生成将RCIC生成的原始噪音以及与之相应的负性噪音, 分别叠加至基本面孔(base face)生成两种噪音模式相反的刺激图像; (2)双图片迫选任务每次试验中将两种相反噪音模式的刺激图像并排呈现, 要求被试选择出最接近目标类别的面孔。文献来源：Brinkman, Todorov和Dotsch (2017)

图4 基于相同基本面孔获得的摩洛哥人与中国人的面孔表征注：Dotsch等人(2008)以高加索人(白种人)为基本面孔, 采用2IFC方式, 实验不仅生成摩洛哥人的心理表征图像, 也生成出中国人的心理表征图像。实验结果表明：基本面孔的选择不会影响最终的面孔分类表征图像。文献来源：Dotsch, Wigboldus, Langner和van Knippenberg(2008)

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