Deficits in visuospatial attention are commonly observed after different kinds of brain lesions. However, the structure–function relationships are not well understood. We investigated whether our response time (RT) model, strategies of visual search (STRAVIS), combined with a linear model of brain lesions, enables us to relate specific impairments in cognitive processes to specific sites of focal brain lesions. In STRAVIS, RTs in overt visual feature search with graded target-distractor similarity are decomposed into the durations of successive search steps. Fitting the model to an observer's RTs yields individual estimates of the parameters “attentional focus size,” “attentional dwell time,” and “movement time of attention or the eyes.” In 28 patients with various focal lesions to the frontal, parietal, and/or temporal cortex and 28 matched controls, we determined with the help of linear models which lesions were most predictive for each parameter. Predictions were validated with a second sample of 12 patients and 12 controls. Critical lesion areas for the STRAVIS focus size were the dorsolateral prefrontal cortex and the temporal lobe, with dorsolateral prefrontal cortex lesions reducing the focus and temporal lesions enlarging it. The STRAVIS dwell time was reduced in patients with lesions to the anterior insula and the superior parietal lobe. Lesions to the frontal eye fields, the superior parietal lobe, and the parieto-occipital cortex were most detrimental to the STRAVIS movement time. Applying linear models to a patient sample with heterogeneous lesions may be a promising new method for investigating how different brain areas interplay in a complex task.

Our visual percepts are not fully determined by the physical stimulus input. That is why we perceive crisp bounding contours even in the absence of luminance-defined borders in visual illusions such as the Kanizsa figure. It is important to understand which neural processes are involved in creating these artificial visual experiences because this might tell us how we perceive coherent objects in natural scenes, which are characterized by mutual overlap. We have already shown using functional magnetic resonance imaging [Maertens, M., & Pollmann, S. fMRI reveals a common neural substrate of illusory and real contours in v1 after perceptual learning. Journal of Cognitive Neuroscience, 17 , 1553–1564, 2005] that neurons in the primary visual cortex (V1) respond to these stimuli. Here we provide support for the hypothesis that V1 is obligatory for the discrimination of the curvature of illusory contours. We presented illusory contours across the portion of the visual field corresponding to the physiological “blind spot.” Four observers were extensively trained and asked to discriminate fine curvature differences in these illusory contours. A distinct performance drop (increased errors and response latencies) was observed when illusory contours traversed the blind spot compared to when they were presented in the “normal” contralateral visual field at the same eccentricity. We attribute this specific performance deficit to the failure to build up a representation of the illusory contour in the absence of a cortical representation of the “blind spot” within V1. The current results substantiate the assumption that neural activity in area V1 is closely related to our phenomenal experience of illusory contours in particular, and to the construction of our subjective percepts in general.

Perceptual learning involves the specific and relatively permanent modification of perception following a sensory experience. In psychophysical experiments, the specificity of the learning effects to the trained stimulus attributes (e.g., visual field position or stimulus orientation) is often attributed to assumed neural modifications at an early cortical site within the visual processing hierarchy. We directly investigated a neural correlate of perceptual learning in the primary visual cortex using fMRI. Twenty volunteers practiced a curvature discrimination on Kanizsa-type illusory contours in the MR scanner. Practice-induced changes in the BOLD response to illusory contours were compared between the pretraining and the posttraining block in those areas of the primary visual cortex (V1) that, in the same session, had been identified to represent real contours at corresponding visual field locations. A retinotopically specific BOLD signal increase to illusory contours was observed as a consequence of the training, possibly signaling the formation of a contour representation, which is necessary for performing the curvature discrimination. The effects of perceptual training were maintained over a period of about 10 months, and they were specific to the trained visual field position. The behavioral specificity of the learning effects supports an involvement of V1 in perceptual learning, and not in unspecific attentional effects.

Using event-related fMRI, we analyzed the functional neuroanatomy of covert reorienting and inhibition of return (IOR). Covert reorienting to a target appearing within 250 msec after an invalid contralateral location cue elicited increased activation in the left fronto-polar cortex (LFPC), right anterior and left posterior middle frontal gyrus, and right cerebellum, areas that have previously been associated with attentional processes, specifically attentional change. In contrast, IOR, which leads to prolonged response times to targets that appear at the cued location at a stimulus-onset-asynchrony (SOA)>250 msec, was accompanied by increased activation in brain areas involved in oculomotor programming, such as the right medial frontal gyrus (supplementary eye field; SEF) and the right inferior precentral sulcus (frontal eye field; FEF), supporting the oculomotor bias theory of IOR. Pre-SEF and pre-FEF areas were involved both in covert reorienting and IOR. The supramarginal gyri were bilaterally involved in IOR, with the right supramarginal gyrus additionally involved in covert reorienting.