We present the results of 51 stroke patients with free central visual fields of which about half suffer from clear deficits of midlevel vision undetected by standard clinical tests. These patients yield significantly elevated thresholds for detection and/or discrimination between forms defined by motion, colour, or line orientation ('texture'). As demonstrated by voxel-based lesion-symptom mapping (VLSM) the underlying lesions involve mainly area human V4 (hV4) located in the posterior third of the fusiform gyrus and extending into the lingual gyrus. Patient's detection thresholds correlate only very weakly between the submodalities tested, indicating partly separate neural networks on mid-level vision for colour, motion, and texture detection. Correlations are far stronger for form discrimination tasks, indicating partly shared mechanisms for even simple form discrimination of distinct visual submodalities. We conclude that deficits of visual perception are far more common after strokes in visual brain areas than is apparent in clinical practice. Our results further clarify the functional organization of midlevel visual cortical areas.
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http://dx.doi.org/10.1016/j.cortex.2020.06.006 | DOI Listing |
Sci Rep
January 2025
Bates College Program in Neuroscience, Bates College, Lewiston, ME, USA.
Nat Commun
December 2024
Department of Psychology, Cornell University, Ithaca, NY, USA.
Subjective feelings are thought to arise from conceptual and bodily states. We examine whether the valence of feelings may also be decoded directly from objective ecological statistics of the visual environment. We train a visual valence (VV) machine learning model of low-level image statistics on nearly 8000 emotionally charged photographs.
View Article and Find Full Text PDFAtten Percept Psychophys
December 2024
Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany.
Studies suggest that mid-level features could underlie object animacy perception. In the current research, we tested whether ensemble animacy perception is based on high- or mid-level features. We used five types of images of animals and inanimate objects: color, grayscale, silhouettes, texforms - unrecognizable images that preserve mid-level texture and shape information - and scrambled images.
View Article and Find Full Text PDFJ Neurosci
November 2024
Department of Biological Structure and Washington National Primate Research Center, University of Washington, Seattle, WA 98195, USA
Heliyon
October 2024
College of Electronic Engineering, Xi'an Shiyou University, Xi'an, 710000, Shaanxi, China.
X-ray non-destructive testing (NDT) technology is extensively utilized in the welding industry for the detection of weld defects. This paper proposes a novel defect segmentation algorithm to address the challenges of X-ray defect detection images, including low contrast, blurred edges, significant noise, and pronounced background variations. Traditional detection methods often struggle to extract low-contrast defects from weld images, so this approach integrates both underlying and mid-level image information to enhance accuracy.
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