Considerable debate has arisen about whether brain activity in elite athletes is characterized by an overall quieting, or neural efficiency in brain processes, or whether elite performance is characterized by activation of two simultaneous networks. One network exercises cognitive control using increased theta activation of premotor and cingulate gyrus, whereas the second reduces alpha activation in an inhibitory network that prevents the intrusion of debilitating thoughts emanating from the temporal lobe and other areas. Also, there is controversy about how a long-duration “quiet eye” (QE) can fit within a single efficient neural system, or whether a dual system where both increased cognitive control and reduced inhibitory processes has advantages. The literature on neural efficiency, the QE, and theta cognitive control, suggest that a long-duration QE promotes both an increase in theta band activation of the medial prefrontal cortex and anterior cingulate and reduced activation and inhibition of the temporal regions during high-pressure situations when a high level of focused, cognitive control is essential.
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The Role of Mental Processes in Elite Sports Performance
Joan N. Vickers and A. Mark Williams
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Effects of Early Visual Deprivation
Brigitte Röder and Ramesh Kekunnaya
As a consequence of congenital blindness, compensatory performance in the intact sensory modalities has been documented in humans in many domains, including auditory and tactile perception, auditory localization, voice and language processing, and memory. Both changes of the neural circuits associated with the intact sensory systems (intramodal plasticity) and an activation of deprived visual cortex (crossmodal plasticity) have been observed in blind humans. Compensation in congenitally blind and late-blind individuals involves partially different neural mechanisms. If sight is restored in patients who were born with dense bilateral cataracts (opaque lenses preventing patterned light to reach the retina), considerable visual recovery has been observed in basic visual functions even after long periods of visual deprivation. Functional recovery has been found to be lower for higher-order visual processes, which has been linked to deficits in the functional specialization of neural circuits. First evidence has suggested that crossmodal plasticity largely retracts after sight restoration but that crossmodal activity does not seem to fully dissolve. In contrast, intramodal adaptations in the auditory system have been observed to persist after sight restoration. Except for predominantly subcortically mediated multisensory functions, many multisensory processes have been found to be altered even many years after sight restoration.
On the one hand, research in permanently blind humans has documented a high capability of the human neurocognitive system to adapt to an atypical environment. On the other hand, research in sight recovery individuals who had suffered a transient phase of visual deprivation following birth has demonstrated functional specific sensitive periods in the development of visual and multisensory neural circuits.
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Deep Learning Networks and Visual Perception
Grace W. Lindsay and Thomas Serre
Deep learning is an approach to artificial intelligence (AI) centered on the training of deep artificial neural networks to perform complex tasks. Since the early 21st century, this approach has led to record-breaking advances in AI, allowing computers to solve complex board games, video games, natural language-processing tasks, and vision problems. Neuroscientists and psychologists have also utilized these networks as models of biological information processing to understand language, motor control, cognition, audition, and—most commonly—vision. Specifically, early feedforward network architectures were inspired by visual neuroscience and are used to model neural activity and human behavior. They also provide useful representations of the perceptual space of images. The extent to which these models match data, however, depends on the methods used to characterize and compare them. The limitations of these feedforward neural networks to account for, for example, simple visual reasoning tasks, suggests that feedback mechanisms may be necessary to solve visual recognition tasks beyond image categorization.