Encoding of complexity, shape, and curvature by macaque infero-temporal neurons

Multivariate Patterns in the Human Object-Processing Pathway Reveal a Shift from Retinotopic to Shape Curvature Representations in Lateral Occipital Areas, LO-1 and LO-2

Representations in early visual areas are organized on the basis of retinotopy, but this organizational principle appears to lose prominence in the extrastriate cortex. Nevertheless, an extrastriate region, such as the shape-selective lateral occipital cortex (LO), must still base its activation on the responses from earlier retinotopic visual areas, implying that a transition from retinotopic to "functional" organizations should exist. We hypothesized that such a transition may lie in LO-1 or LO-2, two visual areas lying between retinotopically defined V3d and functionally defined LO. Using a rapid event-related fMRI paradigm, we measured neural similarity in 12 human participants between pairs of stimuli differing along dimensions of shape exemplar and shape complexity within both retinotopically and functionally defined visual areas. These neural similarity measures were then compared with low-level and more abstract (curvature-based) measures of stimulus similarity. We found that low-level, but not abstract, stimulus measures predicted V1-V3 responses, whereas the converse was true for LO, a double dissociation. Critically, abstract stimulus measures were most predictive of responses within LO-2, akin to LO, whereas both low-level and abstract measures were predictive for responses within LO-1, perhaps indicating a transitional point between those two organizational principles. Similar transitions to abstract representations were not observed in the more ventral stream passing through V4 and VO-1/2. The transition we observed in LO-1 and LO-2 demonstrates that a more "abstracted" representation, typically considered the preserve of "category-selective" extrastriate cortex, can nevertheless emerge in retinotopic regions.

SIGNIFICANCE STATEMENT

Visual areas are typically identified either through retinotopy (e.g., V1-V3) or from functional selectivity [e.g., shape-selective lateral occipital complex (LOC)]. We combined these approaches to explore the nature of shape representations through the visual hierarchy. Two different representations emerged: the first reflected low-level shape properties (dependent on the spatial layout of the shape outline), whereas the second captured more abstract curvature-related shape features. Critically, early visual cortex represented low-level information but this diminished in the extrastriate cortex (LO-1/LO-2/LOC), in which the abstract representation emerged. Therefore, this work further elucidates the nature of shape representations in the LOC, provides insight into how those representations emerge from early retinotopic cortex, and crucially demonstrates that retinotopically tuned regions (LO-1/LO-2) are not necessarily constrained to retinotopic representations.

What are the Visual Features Underlying Rapid Object Recognition?

Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically plausible computational models of (bottom-up) pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task.