Forsythe Alex, Street Nichola, Helmy Mai
Department of Psychology, University of Liverpool, Liverpool, UK.
Department of Psychology, Staffordshire University, Stoke-on-Trent, UK.
Behav Res Methods. 2017 Aug;49(4):1484-1493. doi: 10.3758/s13428-016-0808-z.
Differences between norm ratings collected when participants are asked to consider more than one picture characteristic are contrasted with the traditional methodological approaches of collecting ratings separately for image constructs. We present data that suggest that reporting normative data, based on methodological procedures that ask participants to consider multiple image constructs simultaneously, could potentially confounded norm data. We provide data for two new image constructs, beauty and the extent to which participants encountered the stimuli in their everyday lives. Analysis of this data suggests that familiarity and encounter are tapping different image constructs. The extent to which an observer encounters an object predicts human judgments of visual complexity. Encountering an image was also found to be an important predictor of beauty, but familiarity with that image was not. Taken together, these results suggest that continuing to collect complexity measures from human judgments is a pointless exercise. Automated measures are more reliable and valid measures, which are demonstrated here as predicting human preferences.
当要求参与者考虑多个图片特征时收集的标准评分，与分别针对图像结构收集评分的传统方法进行了对比。我们提供的数据表明，基于要求参与者同时考虑多个图像结构的方法程序来报告标准数据，可能会混淆标准数据。我们提供了关于两个新图像结构的数据，即美感以及参与者在日常生活中遇到刺激的程度。对这些数据的分析表明，熟悉度和接触程度反映了不同的图像结构。观察者接触一个物体的程度可预测人类对视觉复杂性的判断。还发现接触一幅图像是美感的一个重要预测因素，但对该图像的熟悉度则不是。综合来看，这些结果表明，继续从人类判断中收集复杂性度量是一项毫无意义的工作。自动度量是更可靠和有效的度量，在此表明其能够预测人类偏好。
