Blindsight refers to the ability of some patients with destruction of the primary visual cortex (V1) to respond to stimuli presented in their clinically blind visual field despite lack of visual awareness. Here we tested a rare and well-known patient with blindsight following hemispherectomy, DR, who has had the entire cortex in the right hemisphere removed, and in whom the right superior colliculus is the only post-chiasmatic visual structure remaining intact. Compared to more traditional cases of blindsight after damage confined to V1, the study of blindsight in hemispherectomy has offered the invaluable opportunity to examine directly two outstanding questions: the contribution of the intact hemisphere to visual processing without awareness, and the nature of plastic and compensatory changes in these remaining contralesional visual areas. Population receptive field (pRF) mapping was used to define retinotopic maps, delineate the boundaries between the visual areas, examine changes in the sizes of receptive field centres within each visual area, and their variability as a function of eccentricity. Aside from the dorsal visual areas showing blurred borders between V2d and V3d, not otherwise detected with perimetric mapping, the retinotopic maps of DR did not differ substantially from those of three matched healthy controls. Interestingly, those dorsal compartments showed a significant increase in the RF sizes toward values typical of higher-order processing cortices, while no differences were observed in the corresponding ventral visual areas. Findings showed that whereas receptive field sizes at foveal and parafoveal eccentricities (≤ 4°) were not measurably altered, the pRF size increased by ~ 270% at 4–6° of eccentricity, and the size difference reached ~ 300% between 8° and 10°. We interpret these findings to suggest that an increase in pRF sizes could be indicative of cerebral plasticity involving the retinotopic reorganization of the dorsal visual areas.

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Perceptual skills improve with daily practice (Fahle and Poggio, 2002; Fine and Jacobs, 2002). Practice induces plasticity in task-relevant brain regions during an "offline" consolidation period thought to last several hours, during which initially fragile memory traces become stable (Karni, 1996; Dudai, 2004). Impaired retention of a task if followed by training in another task is considered evidence for the instability of memory traces during consolidation (Dudai, 2004). However, it remains unknown when after training memory traces become stable and resistant against interference, where in the brain the neuronal mechanisms responsible for interference are localized, and how these mechanisms produce interference. Here, we show in human participants strong interference between two visual skill-learning tasks for surprisingly long time intervals between training periods (up to 24 h). Interference occurred during asymptotic learning, but only when stimuli were similar between tasks. This supports a strong contribution to interference of low-level visual cortical areas (Karni and Bertini, 1997; Ahissar and Hochstein, 2004), where similar stimuli recruit overlapping neuronal populations. Our finding of stimulus-dependent and time-independent interference reveals a fundamental limit in cortical plasticity that constrains the simultaneous representation of multiple skills in a single neuronal population, rather than a time-limited consolidation process.

Visual scene perception owes greatly to surface features such as color and brightness. Yet, early visual cortical areas predominantly encode surface boundaries rather than surface interiors. Whether human early visual cortex may nevertheless carry a small signal relevant for surface perception is a topic of debate. We induced brightness changes in a physically constant surface by temporally modulating the luminance of surrounding surfaces in seven human participants. We found that fMRI activity in the V2 representation of the constant surface was in antiphase to luminance changes of surrounding surfaces (i.e., activity was in-phase with perceived brightness changes). Moreover, the amplitude of the antiphase fMRI activity in V2 predicted the strength of illusory brightness perception. We interpret our findings as evidence for a surface-related signal in early visual cortex and discuss the neural mechanisms that may underlie that signal in concurrence with its possible interaction with the properties of the fMRI signal.

Although in traditional attention research the focus of visual spatial attention has been considered as indivisible, many studies in the last 15 years have claimed the contrary.These studies suggest that humans can direct their attention simultaneously to multiple noncontiguous regions of the visual field upon mere instruction.The notion that spatial attention can easily be split is counterintuitive in the light of current neurocognitive models of attention.We examined studies on divided attention against 4 methodological criteria that should be satisfied in order to convincingly demonstrate divided attention, and we found no studies in the current literature that pass this test.On the basis of current theories of attention, we argue that dividing attention may not be easily achievable by naive human observers and that, instead, it is a skill that may be acquired only through training.

Computational neuromodeling may help to further our understanding of how empirical neuroimaging findings are generated by underlying neural mechanisms. Here, we used a simple computational model that simulates early visual processing of brightness changes in a dynamic, illusory display. The model accurately predicted illusory brightness changes in a grey area of constant luminance induced by (and in anti-phase to) luminance changes in its surroundings. Moreover, we were able to directly compare these predictions with recently observed fMRI results on the same brightness illusion by projecting predicted activity from our model onto empirically investigated brain regions. This new approach in which generated network activity and measured neuroimaging data are interfaced in a common representational “brain space” can contribute to the integration of computational and experimental neuroscience.