Elsa Scialom¹ (), Zehra Merchant¹, Udo A. Ernst², David Rotermund², Ben Lonnqvist¹, Michael H. Herzog¹; ¹EPFL, Lausanne, Switzerland, ²University of Bremen, Bremen, Germany

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<0.001, partial η2 = 0.12). Additionally, the number of fragments necessary for recognition varied across different categories (F(11,38) = 5.93, p<0.001, partial η2 = 0.65). There was only a weak interaction between fragment type and object category indicating independence between these factors (F(11,38) = 3.66, p<0.001, partial η2 = 0.02). As a consequence, when examining object categories individually, differences in exemplars’ recognition were remarkably stable across the two types of fragmentation (r = 0.75, p < 0.01). The effect sizes observed indicate that the minimum visual information needed to recognize object primarily depends on object category, while local visual information plays a secondary role. Thus, we argue that studying the complexity of object categories is key to determine the minimum visual information needed for object recognition. Our results will help to identify the objects’ parts that are crucial for object recognition in visual prostheses where the number of electrodes is restricted.

Acknowledgements: Authors are funded by ERA-NET NEURON under grant NEURON-051. UAE and DR are also funded by the “Iris und Hartmut Jürgens Stiftung: Chance auf ein neues Leben” and the “Stiftung Bremer Wertpapierbörse (SBWB)”.