Ibarra Frank F, Kardan Omid, Hunter MaryCarol R, Kotabe Hiroki P, Meyer Francisco A C, Berman Marc G
Department of Psychology, University of ChicagoChicago, IL, USA.
School of Natural Resources and Environment, University of MichiganAnn Arbor, MI, USA.
Front Psychol. 2017 Apr 28;8:632. doi: 10.3389/fpsyg.2017.00632. eCollection 2017.
Previous research has investigated ways to quantify visual information of a scene in terms of a visual processing hierarchy, i.e., making sense of visual environment by segmentation and integration of elementary sensory input. Guided by this research, studies have developed categories for low-level visual features (e.g., edges, colors), high-level visual features (scene-level entities that convey semantic information such as objects), and how models of those features predict aesthetic preference and naturalness. For example, in Kardan et al. (2015a), 52 participants provided aesthetic preference and naturalness ratings, which are used in the current study, for 307 images of mixed natural and urban content. Kardan et al. (2015a) then developed a model using low-level features to predict aesthetic preference and naturalness and could do so with high accuracy. What has yet to be explored is the ability of higher-level visual features (e.g., horizon line position relative to viewer, geometry of building distribution relative to visual access) to predict aesthetic preference and naturalness of scenes, and whether higher-level features mediate some of the association between the low-level features and aesthetic preference or naturalness. In this study we investigated these relationships and found that low- and high- level features explain 68.4% of the variance in aesthetic preference ratings and 88.7% of the variance in naturalness ratings. Additionally, several high-level features mediated the relationship between the low-level visual features and aaesthetic preference. In a multiple mediation analysis, the high-level feature mediators accounted for over 50% of the variance in predicting aesthetic preference. These results show that high-level visual features play a prominent role predicting aesthetic preference, but do not completely eliminate the predictive power of the low-level visual features. These strong predictors provide powerful insights for future research relating to landscape and urban design with the aim of maximizing subjective well-being, which could lead to improved health outcomes on a larger scale.
先前的研究已经探讨了如何根据视觉处理层次结构来量化场景的视觉信息，即通过对基本感官输入进行分割和整合来理解视觉环境。在这项研究的指导下，已有研究针对低级视觉特征（如边缘、颜色）、高级视觉特征（传达语义信息的场景级实体，如物体）进行了分类，还研究了这些特征的模型如何预测审美偏好和自然感。例如，在卡丹等人（2015年a）的研究中，52名参与者对307张自然与城市混合内容的图像给出了审美偏好和自然感评分，本研究使用了这些评分。卡丹等人（2015年a）随后开发了一个使用低级特征来预测审美偏好和自然感的模型，并且能够以很高的准确率做到这一点。尚未探索的是高级视觉特征（如地平线相对于观察者的位置、建筑物分布相对于视觉通道的几何形状）预测场景审美偏好和自然感的能力，以及高级特征是否介导了低级特征与审美偏好或自然感之间的某些关联。在本研究中，我们调查了这些关系，发现低级和高级特征解释了审美偏好评分中68.4%的方差以及自然感评分中88.7%的方差。此外，几个高级特征介导了低级视觉特征与审美偏好之间的关系。在多重中介分析中，高级特征中介在预测审美偏好方面占方差的50%以上。这些结果表明，高级视觉特征在预测审美偏好方面发挥着重要作用，但并没有完全消除低级视觉特征的预测能力。这些强大的预测因素为未来与景观和城市设计相关的研究提供了有力的见解，旨在最大限度地提高主观幸福感，这可能会在更大范围内改善健康状况。
