Categorical object properties outweigh local visual information in object recognition

Certain parts of an object are more critical for recognition than others. Despite decades of research, it is not well understood what these parts are and how they interact with each other. This study investigates if and to which extent categorical object properties interact with local visual information in object recognition. Fifty participants classified fragmented objects from 12 categories, each consisting of 4 unique exemplars (48 objects in total). Exemplars differed in orientation and shape. Their outlines were presented as either curved segments or dots, providing high and low local visual information respectively. Spatial frequencies and fragment’s size were comparable. We gradually increased the number of fragments to quantify the minimum number of fragments necessary to recognize objects. A linear mixed model indicates that participants required significantly fewer curved segments than dots to recognize objects (F(1,48) = 248.05, p