Abstract
Spatial relations between objects can be represented in a categorical and in a coordinate manner. Categorical representations reflect abstract relations, like ‘left of’ or ‘under’, whereas coordinate representations concern exact metric distances between objects. These two types of spatial relations are thought to be linked to a left hemisphere and a right hemisphere advantage, respectively. This lateralization pattern was examined in a visual search task, making use of natural scenes, in patients with unilateral brain damage and healthy controls. In addition, all participants performed a low-level spatial relation processing task. The results suggest that the lateralization pattern commonly found for spatial relation processing in low-level perceptual tasks is also applicable to the processing of complex visual scenes.
Highlights
Spatial relations between objects can be represented in a categorical and in a coordinate manner
A large cross size promoted categorical processing, while coordinate processing benefitted from a small cross size
Analysis of z-scores based on both Acc and RT showed that LH patients were impaired in the coordinate large cross and categorical small cross conditions
Summary
Spatial relations between objects can be represented in a categorical and in a coordinate manner. Categorical representations reflect abstract relations, like ‘left of’ or ‘under’, whereas coordinate representations concern exact metric distances between objects. These two types of spatial relations are thought to rely on at least partially separate processing mechanisms, reflected in dissociated lateralization patterns. The large majority of spatial relation studies have made use of very basic, low-level task designs, using lines, dots and crosses We studied visual search performance in stroke patients with either left or right hemisphere damage, and a matched sample of control participants. In addition to the visual search task, all participants performed one of the often used low-level task designs, the cross dot task, to directly compare high-level spatial relation processing to low-level spatial relation processing
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