The visual cortex retains the capacity for experience-dependent changes, or plasticity, of cortical function and cortical circuitry, throughout life. These changes constitute the mechanism of perceptual learning in normal visual experience and in recovery of function after CNS damage. Such plasticity can be seen at multiple stages in the visual pathway, including primary visual cortex. The manifestation of the functional changes associated with perceptual learning involve both long term modification of cortical circuits during the course of learning, and short term dynamics in the functional properties of cortical neurons. These dynamics are subject to top-down influences of attention, expectation and perceptual task. As a consequence, each cortical area is an adaptive processor, altering its function in accordance to immediate perceptual demands.
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http://dx.doi.org/10.1113/jphysiol.2009.171488 | DOI Listing |
Neuropsychol Rehabil
January 2025
School of Psychological Sciences, Macquarie University, Marsfield, NSW 2109, Australia.
Prosopagnosia is a neurological disorder; characterized by an impairment in facial recognition. It can occur from acquired prosopagnosia (occurring in approximately 5.6% of the population), or from developmental prosopagnosia (occurring in approximately 2% of the population).
View Article and Find Full Text PDFJ Multidiscip Healthc
January 2025
Department of Teacher Education, NLA University College, Oslo, Norway.
Introduction: Motor learning, in addition to influencing the practice of physical activity, affects cognitive skills related to prediction and decision. One key principle in sports training is designing exercise programs that optimize cognitive-motor performance, based on the Challenge Point Framework (CPF). The aim of this study is to investigate the effect of different levels of work difficulty on cognitive-perceptual indicators in table tennis beginners.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science and Technology, Donghua University, Shanghai, 201620, China.
Extracting high-order abstract patterns from complex high-dimensional data forms the foundation of human cognitive abilities. Abstract visual reasoning involves identifying abstract patterns embedded within composite images, considered a core competency of machine intelligence. Traditional neuro-symbolic methods often infer unknown objects through data fitting, without fully exploring the abstract patterns within composite images and the sequential sensitivity of visual sequences.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Plastic, Craniofacial and Hand Surgery, Sidra Medicine, and Weill Cornell Medical College, C1-121, Al Gharrafa St, Ar Rayyan, Doha, Qatar.
Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken.
View Article and Find Full Text PDFNeural Netw
January 2025
Image Processing Lab., Universitat de València, 46980 Paterna, Spain. Electronic address:
There is an open debate on the role of artificial networks to understand the visual brain. Internal representations of images in artificial networks develop human-like properties. In particular, evaluating distortions using differences between internal features is correlated to human perception of distortion.
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