Department of Psychology, University of York, York, UK.
School of Psychology, The University of Nottingham, Nottingham, UK.
Hum Brain Mapp. 2019 Nov 1;40(16):4716-4731. doi: 10.1002/hbm.24732. Epub 2019 Jul 23.
The ventral visual pathway is directly involved in the perception and recognition of objects. However, the extent to which the neural representation of objects in this region reflects low-level or high-level properties remains unresolved. A problem in resolving this issue is that only a small proportion of the objects experienced during natural viewing can be shown during a typical experiment. This can lead to an uneven sampling of objects that biases our understanding of how they are represented. To address this issue, we developed a data-driven approach to stimulus selection that involved describing a large number objects in terms of their image properties. In the first experiment, clusters of objects were evenly selected from this multi-dimensional image space. Although the clusters did not have any consistent semantic features, each elicited a distinct pattern of neural response. In the second experiment, we asked whether high-level, category-selective patterns of response could be elicited by objects from other categories, but with similar image properties. Object clusters were selected based on the similarity of their image properties to objects from five different categories (bottle, chair, face, house, and shoe). The pattern of response to each metameric object cluster was similar to the pattern elicited by objects from the corresponding category. For example, the pattern for bottles was similar to the pattern for objects with similar image properties to bottles. In both experiments, the patterns of response were consistent across participants providing evidence for common organising principles. This study provides a more ecological approach to understanding the perceptual representations of objects and reveals the importance of image properties.
腹侧视觉通路直接参与物体的感知和识别。然而，该区域中物体的神经表示是否反映了低水平或高水平的特性仍未解决。解决这个问题的一个问题是，在典型的实验中只能展示在自然观察过程中经历的一小部分物体。这可能导致对物体的不均匀采样，从而影响我们对它们如何被表示的理解。为了解决这个问题，我们开发了一种数据驱动的刺激选择方法，该方法涉及根据物体的图像属性来描述大量物体。在第一个实验中，从这个多维图像空间中均匀选择了物体的聚类。尽管聚类没有任何一致的语义特征，但每个聚类都引起了明显的神经反应模式。在第二个实验中，我们询问是否可以通过具有相似图像属性但属于其他类别的物体来引出高级、类别选择性的反应模式。基于与来自五个不同类别的物体（瓶子、椅子、脸、房子和鞋子）的相似图像属性，选择物体聚类。每个同形聚类物体的反应模式与来自相应类别的物体的反应模式相似。例如，瓶子的模式与具有与瓶子相似图像属性的物体的模式相似。在两个实验中，每个参与者的反应模式都是一致的，这为共同的组织原则提供了证据。这项研究提供了一种更具生态性的方法来理解物体的感知表示，并揭示了图像属性的重要性。
