The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition system’s optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions in agreement with the available data. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.Author Summary: Domain-specific regions, like the fusiform face area, are a prominent feature of ventral visual cortex organization. Despite decades of interest from a large number of investigators employing diverse methods, there has been surprisingly little theoretical work on “why” the ventral stream may adopt this modular organization. In this study we propose a computational account of the role played by domain-specific regions in ventral stream function. It follows from a new theoretical analysis of the recognition problem which highlights the importance of building representations that are robust to class-specific transformations. These results provide a unifying account linking neuroimaging and neuropsychology-based ideas of domain-specific regions to the psychophysics and electrophysiology-oriented literature on view-based object recognition and invariance.