Abstract
Significant objects in a scene can make a great contribution to scene recognition. Besides the three scene-selective regions: parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA), some neuroimaging studies have shown that the lateral occipital complex (LOC) is also engaged in scene recognition processing. In this study, the multivariate pattern analysis was adopted to explore the object-scene association in scene recognition when different amounts of significant objects were masked. The scene classification only succeeded in the intact scene in the ROIs. In addition, the average signal intensity in LOC [including the lateral occipital cortex (LO) and the posterior fusiform area (pF)] decreased when there were masked objects, but such a decrease was not observed in scene-selective regions. These results suggested that LOC was sensitive to the loss of significant objects and mainly involved in scene recognition by the object-scene semantic association. The performance of the scene-selective areas may be mainly due to the fact that they responded to the change of the scene's entire attribute, such as the spatial information, when they were employed in the scene recognition processing. These findings further enrich our knowledge of the significant objects' influence on the activation pattern during the process of scene recognition.
Highlights
Scene recognition is a common and important brain activity that can help us access environmental information
We focused on the influence of masking objects on the regions of interests (ROIs) in scene recognition by the multivariate pattern analysis (MVPA)
When we classified scenes based on the activation pattern in the lateral occipital cortex (LO), the results showed that the classification accuracy is not significant higher than the chance level (25%) when there was object masked in the scene picture (NM: 49.44%, t13 = 8.13, p < 0.001; M1: 21.83%, t13 = −2.75, p = 0.017; M2: 27.12%, t13 = 0.89, p = 0.391)
Summary
Scene recognition is a common and important brain activity that can help us access environmental information. Previous studies have indicated that scene recognition relies on intrinsic properties of scenes related to 3D spatial structure, such as expanse or degree of openness (Kravitz et al, 2011; Park et al, 2011; Lowe et al, 2016), and the deepness of scene (Greene and Oliva, 2009; Amit et al, 2012; Park et al, 2015). As an important part of the scene, the objects in the scene are of great significance to the scene recognition. Many studies have suggested that the nature of scene recognition is the information integration of objects in the scene
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