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Learning to see (better): Improving visual deficits with perceptual learning

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196036 Year: Pages: 95 DOI: 10.3389/978-2-88919-603-6 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Psychology
Added to DOAB on : 2016-08-16 10:34:25
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Perceptual learning can be defined as a long lasting improvement in a perceptual skill following a systematic training, due to changes in brain plasticity at the level of sensory or perceptual areas. Its efficacy has been reported for a number of visual tasks, such as detection or discrimination of visual gratings (De Valois, 1977; Fiorentini & Berardi, 1980, 1981; Mayer, 1983), motion direction discrimination (Ball & Sekuler, 1982, 1987; Ball, Sekuler, & Machamer, 1983), orientation judgments (Fahle, 1997; Shiu & Pashler, 1992; Vogels & Orban, 1985), hyperacuity (Beard, Levi, & Reich, 1995; Bennett & Westheimer, 1991; Fahle, 1997; Fahle & Edelman, 1993; Kumar & Glaser, 1993; McKee & Westheimer, 1978; Saarinen & Levi, 1995), visual search tasks (Ahissar & Hochstein, 1996; Casco, Campana, & Gidiuli, 2001; Campana & Casco, 2003; Ellison & Walsh, 1998; Sireteanu & Rettenbach, 1995) or texture discrimination (Casco et al., 2004; Karni & Sagi, 1991, 1993). Perceptual learning is long-lasting and specific for basic stimulus features (orientation, retinal position, eye of presentation) suggesting a long-term modification at early stages of visual analysis, such as in the striate (Karni & Sagi, 1991; 1993; Saarinen & Levi, 1995; Pourtois et al., 2008) and extrastriate (Ahissar & Hochstein, 1996) visual cortex. Not confined to a basic research paradigm, perceptual learning has recently found application outside the laboratory environment, being used for clinical treatment of a series of visually impairing conditions such as amblyopia (Levi & Polat, 1996; Levi, 2005; Levi & Li, 2009, Polat et al., 2004; Zhou et al., 2006), myopia (Tan & Fong, 2008) or presbyopia (Polat, 2009). Different authors adopted different paradigms and stimuli in order to improve malfunctioning visual abilities, such as Vernier Acuity (Levi, Polat & Hu, 1997), Gratings detection (Zhou et al., 2006), oculomotor training (Rosengarth et al., 2013) and lateral interactions (Polat et al., 2004). The common result of these studies is that a specific training produces not only improvements in trained functions, but also in other, untrained and higher-level visual functions, such as visual acuity, contrast sensitivity and reading speed (Levi et al, 1997a, 1997b; Polat et al., 2004; Polat, 2009; Tan & Fong, 2008). More recently (Maniglia et al. 2011), perceptual learning with the lateral interactions paradigm has been successfully used for improving peripheral vision in normal people (by improving contrast sensitivity and reducing crowding, the interference in target discrimination due to the presence of close elements), offering fascinating new perspectives in the rehabilitation of people who suffer of central vision loss, such as maculopathy patients, partially overcoming the structural differences between fovea and periphery that limit the vision outside the fovea. One of the strongest point, and a distinguishing feature of perceptual learning, is that it does not just improve the subject’s performance, but produces changes in brain’s connectivity and efficiency, resulting in long-lasting, enduring neural changes. By tailoring the paradigms on each subject’s needs, perceptual learning could become the treatment of choice for the rehabilitation of visual functions, emerging as a simple procedure that doesn’t need expensive equipment.

Manipulative approaches to human brain dynamics

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889194797 Year: Pages: 246 DOI: 10.3389/978-2-88919-479-7 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2016-03-10 08:14:33
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In this EBook, we highlight how newly emerging techniques for non-invasive manipulation of the human brain, combined with simultaneous recordings of neural activity, contribute to the understanding of brain functions and neural dynamics in humans. A growing body of evidence indicates that the neural dynamics (e.g., oscillations, synchrony) are important in mediating information processing and networking for various functions in the human brain. Most of previous studies on human brain dynamics, however, show correlative relationships between brain functions and patterns of neural dynamics measured by imaging methods such as electroencephalography (EEG), magnetoencephalography (MEG), near-infrared spectroscopy (NIRS), positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). In contrast, manipulative approaches by non-invasive brain stimulation (NIBS) have been developed and extensively used. These approaches include transcranial magnetic stimulation (TMS) and transcranial electric stimulation (tES) such as transcranial direct current stimulation (tDCS), alternating current stimulation (tACS), and random noise stimulation (tRNS), which can directly manipulate neural dynamics in the intact human brain. Although the neural-correlate approach is a strong tool, we think that manipulative approaches have far greater potential to show causal roles of neural dynamics in human brain functions. There have been technical challenges with using manipulative methods together with imaging methods. However, thanks to recent technical developments, it has become possible to use combined methods such as TMS–EEG coregistration. We can now directly measure and manipulate neural dynamics and analyze functional consequences to show causal roles of neural dynamics in various brain functions. Moreover, these combined methods can probe brain excitability, plasticity and cortical networking associated with information processing in the intact human brain. The contributors to this EBook have succeeded in showcasing cutting-edge studies and demonstrate the huge impact of their approaches on many areas in human neuroscience and clinical applications.

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