Hayn-Leichsenring Gregor U, Kenett Yoed N, Schulz Katharina, Chatterjee Anjan
Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104 USA.
Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104 USA.
Acta Psychol (Amst). 2020 Jan;202:102936. doi: 10.1016/j.actpsy.2019.102936. Epub 2019 Nov 16.
While global image properties (GIPs) relate to preference ratings in many categories of visual stimuli, this relationship is typically not seen for abstract art paintings. Using computational network science and empirical methods, we further investigated GIPs and subjective preferences. First, we replicated the earlier observation that GIPs do not relate to preferences for abstract art. Next, we estimated the network structure of abstract art paintings using two approaches: the first was based on verbal descriptions and the second on GIPs. We examined the extent to which network measures computed from these two networks (1) related to preference for abstract art paintings and (2) determined affiliation of images to specific art styles. Only semantic-based network predicted the subjective preference ratings and art style. Finally, preference and GIPs differed for sub-groups of abstract art paintings. Our results demonstrate the importance of verbal descriptors in evaluating abstract art, and that it is not useful in empirical aesthetics to treat abstract art paintings as a single category.
虽然全局图像属性（GIPs）与许多视觉刺激类别的偏好评级相关，但这种关系在抽象艺术绘画中通常并不明显。我们使用计算网络科学和实证方法进一步研究了GIPs和主观偏好。首先，我们重复了早期的观察结果，即GIPs与抽象艺术的偏好无关。接下来，我们使用两种方法估计抽象艺术绘画的网络结构：第一种基于文字描述，第二种基于GIPs。我们研究了从这两个网络计算出的网络度量在多大程度上（1）与抽象艺术绘画的偏好相关，以及（2）确定图像与特定艺术风格的归属关系。只有基于语义的网络能够预测主观偏好评级和艺术风格。最后，抽象艺术绘画子组的偏好和GIPs存在差异。我们的结果证明了文字描述符在评估抽象艺术中的重要性，并且在实证美学中将抽象艺术绘画视为单一类别是没有用的。
