The visual information subtending 3D shape perception and recognition in humans

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Visual shape perception is crucial for effective interactions with our environment and it is an essential basis for the recognition of visual objects. In the present application, we propose to use a relatively novel approach for the study of visual perception which aims to determine directly, without theoretical a priori or bias, the visual information subtending the recognition of visual shapes that are rotated in depth in normally functioning adult human observers. *The new knowledge acquired through the approach proposed here should offer significant theoretical advances since it will inform on three major dimensions that distinguish among competing theories: 1- Type of image feature: The class of descriptors used by the visual system to represent shape determines the type of features that is most relevant for performance. 2- Feature stability: Whether the descriptors by which shapes are represented are stable or variable according to the exposure conditions and stimulus context. 3- Feature conjunctions: Whether feature conjunctions are used to code the spatial relations within an object or alternatively, that the spatial relations among features are irrelevant for shape recognition.*We will first use the classification images techniques of Adaptive Bubbles and of Visual Spread to determine the image features that underlie the recognition of objects from different classes of synthetic 3D shapes shown from variable viewpoints. For each object, these effective locations will be cross-referenced across viewpoints to determine features that are either viewpoint invariant (i.e. which occur in more than one view of the object) and viewpoint specific (i.e. which occur in only one view). These features will be compared to those used by an ideal observer model and characterized along theoretically motivated principles to extract general rules that can be applied to predict the image regions that will be critical for the recognition of novel object sets. *Tests will then be conducted to confirm the value of these predictions on already used object sets (verification tests) as well as to assess them in new sets of objects not studied before with classification image techniques (generalization tests). Eye movements will be recorded during the verification and generalization tests as an additional index of the features that are relevant for shape processing.*The object classes studied will vary in terms of their complexity and their capacity to generalize to objects we encounter in daily life. They will be: 1- single geons (Biederman, 1987); 2- assemblies made of four interconnected geons; 3- the "surface shapes" used by Hung et al. (2012) and Yamane et al. (2008) in their study of single-neuron shape selectivity in the macaque inferotemporal area, which are relatively elementary objects that may nevertheless present a rich surface topography; and 4- "medial axial shapes" developed by Hung et al. (2012), which are rather complex objects, many of them reminiscent of animal or insect shapes.*Elucidating the features underlying visual shape perception is of fundamental importance in the field. The current proposal will contribute to advance our knowledge on the issue through its use of sophisticated psychophysical techniques that have proven their capacity for our purposes, and the use of various object classes that will sample a subset of crucial constraints that our visual system must meet for everyday object recognition.