No abstract

The human visual system devotes a significant proportion of its resources to a very small part of the visual field, the fovea. Foveal vision is crucial for natural behavior and many tasks in daily life such as reading or fine motor control. Despite its significant size, this part of cortex is rarely investigated and the limited data have resulted in competing models of the layout of the foveal confluence in primate species. Specifically, how V2 and V3 converge at the central fovea is the subject of debate in primates and has remained "terra incognita" in humans. Using high-resolution fMRI (1.2 ϫ 1.2 ϫ 1.2 mm 3 ) and carefully designed visual stimuli, we sought to accurately map the human foveal confluence and hence disambiguate the competing theories. We find that V1, V2, and V3 are separable right into the center of the foveal confluence, and V1 ends as a rounded wedge with an affine mapping of the foveal singularity. The adjacent V2 and, in contrast to current concepts from macaque monkey, also V3 maps form continuous bands (ϳ5 mm wide) around the tip of V1. This mapping results in a highly anisotropic representation of the visual field in these areas. Unexpectedly, for the centermost 0.75°, the cortical representations for both V2 and V3 are larger than that of V1, indicating that more neuronal processing power is dedicated to second-level analysis in this small but important part of the visual field.

Primate visual cortex contains a set of maps of visual space. These maps are fundamental to early visual processing, yet their form is not fully understood in humans. This is especially true for the central and most important part of the visual field--the fovea. We used functional magnetic resonance imaging (fMRI) to measure the mapping geometry of human V1 and V2 down to 0.5 degrees of eccentricity. By applying automated atlas fitting procedures to parametrize and average retinotopic measurements of eight brains, we provide a reference standard for the two-dimensional geometry of human early visual cortex of unprecedented precision and analyze this high-quality mean dataset with respect to the 2-dimensional cortical magnification morphometry. The analysis indicates that 1) area V1 has meridional isotropy in areal projection: equal areas of visual space are mapped to equal areas of cortex at any given eccentricity. 2) V1 has a systematic pattern of local anisotropies: cortical magnification varies between isopolar and isoeccentricity lines, and 3) the shape of V1 deviates systematically from the complex-log model, the fit of which is particularly poor close to the fovea. We therefore propose that human V1 be fitted by models based on an equal-area principle of its two-dimensional magnification. 4) V2 is elongated by a factor of 2 in eccentricity direction relative to V1 and has significantly more local anisotropy. We propose that V2 has systematic intrinsic curvature, but V1 is intrinsically flat.

This paper discusses four major challenges facing modern vection research. Challenge 1 (Defining Vection) outlines the different ways that vection has been defined in the literature and discusses their theoretical and experimental ramifications. The term vection is most often used to refer to visual illusions of self-motion induced in stationary observers (by moving, or simulating the motion of, the surrounding environment). However, vection is increasingly being used to also refer to non-visual illusions of self-motion, visually mediated self-motion perceptions, and even general subjective experiences (i.e., “feelings”) of self-motion. The common thread in all of these definitions is the conscious subjective experience of self-motion. Thus, Challenge 2 (Significance of Vection) tackles the crucial issue of whether such conscious experiences actually serve functional roles during self-motion (e.g., in terms of controlling or guiding the self-motion). After more than 100 years of vection research there has been surprisingly little investigation into its functional significance. Challenge 3 (Vection Measures) discusses the difficulties with existing subjective self-report measures of vection (particularly in the context of contemporary research), and proposes several more objective measures of vection based on recent empirical findings. Finally, Challenge 4 (Neural Basis) reviews the recent neuroimaging literature examining the neural basis of vection and discusses the hurdles still facing these investigations.