Somewhere in the visual system, phenomenal vision--the seeing of colors, brightness, depths, shades, and motion--is generated not only from the distribution of light on the retina, but also when the eyes are closed, in dreams, hallucinations, phosphenes, and (possibly) imagery. Whether these different forms of phenomenal vision share a common substrate although their origins are different (optical, mechanical, electrical, endogenous) is discussed in the light of evidence from neuropsychological and functional imaging studies. Whereas extrastriate visual cortical areas appear to be involved in all types of phenomenal vision that have been studied, the necessity of a contribution from primary visual cortex is demonstrated by the loss of conscious vision that follows its destruction. If both extrastriate and primary cortical activation are needed, the latter may not just provide an indispensable input, but may also need to receive the output of the extrastriate processing via reentrant connections.
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Cureus
October 2024
Ophthalmology, Dr. D.Y. Patil Medical College, Hospital and Research Centre, Dr. D.Y. Patil Vidyapeeth (Deemed to be University), Pune, IND.
Autoimmune retinopathy (AIR) is a rare retinal disorder that causes a gradual loss of vision due to autoantibodies targeting retinal antigens, leading to photoreceptor degeneration. Early diagnosis and timely intervention are critical for preserving visual function in affected patients. Over the course of a year, a 30-year-old woman had bilateral, abrupt, painless, progressive diminution of vision, nyctalopia, photopsia, and a restricted peripheral field of vision.
View Article and Find Full Text PDFBrain Sci
September 2024
Psychology Department, Northeastern University, Boston, MA 02115, USA.
Background: Recent evidence in systems neuroscience suggests that lighting conditions affect the whole chain of brain processing, from retina to high-level cortical networks, for perceptual and cognitive function. Here, visual adaptation levels to three different environmental lighting conditions, (1) darkness, (2) daylight, and (3) prolonged exposure to very bright light akin to sunlight, were simulated in lab to investigate the effects of light adaptation levels on classic cases of subjective contrast, assimilation, and contrast-induced relative depth in achromatic, i.e.
View Article and Find Full Text PDFbioRxiv
October 2024
Department of Organismic and Evolutionary Biology, The Arnold Arboretum, Harvard University, Cambridge, MA 02138, USA.
The radiation of angiosperms is marked by a phenomenal diversity of floral size, shape, color, scent, and reward. Through hundreds of years of documentation and quantification, scientists have sought to make sense of this variation by defining pollination syndromes. These syndromes are the convergent evolution of common suits of floral traits across distantly related species that have evolved by selection to optimize pollination strategies.
View Article and Find Full Text PDFBrain Sci
July 2024
University of Trento, 38122 Trento, Italy.
In recent decades, the debate on consciousness has been conditioned by the idea of bottom-up emergence, which has influenced scientific research and raised a few obstacles to any attempt to bridge the explanatory gap. The analysis and explanation of vision conducted according to the accredited methodologies of scientific research in terms of physical stimuli, objectivity, methods, and explanation has encountered the resistance of subjective experience. Moreover, original Gestalt research into vision has generally been merged with cognitive neuroscience.
View Article and Find Full Text PDFJ Clin Med
June 2024
General Thoracic Surgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
In the digital age, artificial intelligence (AI) is emerging as a transformative force in various sectors, including medicine. This article explores the potential of AI, which is akin to the magical genie of Aladdin's lamp, particularly within thoracic surgery and lung cancer management. It examines AI applications like machine learning and deep learning in achieving more precise diagnoses, preoperative risk assessment, and improved surgical outcomes.
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