Working Memory and Its Benefits

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What makes our everyday life very simple? What helps us to know what is happening now, what we are thinking now and what we are doing now? We are aware of the present moment or any changes in this moment, and this ability helps us in functioning effectively to face immediate environmental changes in our everyday life. This ability is called the Working Memory. The term working memory was coined by Miller, Galanter, and Pribram in 1960 (Baddeley, 2003). It refers to the temporary storage in the brain for manipulation of necessary information to execute cognitive tasks. According to Baddeley and Hitch’s study (1974), working memory comprises three main components, a control system, the central executive and two storage systems, the visuospatial sketchpad and the phonological loop (as cited in Baddeley, 2003). The phonological loop stores and processes the auditory inputs while visuospatial sketchpad stores and processes visual inputs in working memory. The visuospatial sketchpad can be divided into visual subsystems and spatial subsystems. It constructs and manipulates visual images and it represents Visual Working Memory (VWM).
Many recent studies focused to study neural correlation and functional organization of working memory encoding through different tasks. They were able to locate the activities in the specific parts of the brain during visual working memory task in sighted participants using Electrophysiological recording. They observed activation of brain areas associated with the processing of visual stimuli and it fascinated many researchers to observe the brain activation for the different modality tasks in blind participants. During their studies they reported crossmodal activation of occipital brain structures in blin...

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...l MRI
In addition to the MEG data, T1-weighted structural MRIs were obtained for all the participants. The data were acquired on a 3 Tesla system (Siemens Magnetom Trio). For source reconstruction, individual single-shell models were derived from the segmentation of these structural MRIs.
Source Reconstruction:
All kinds of data were analyzed using Matlab with custom scripts and the open source Matlab-toolboxes Field Trip and SPM2. A linear beamforming approach was applied to estimate the spectral amplitude and phase of neural population signals at the cortical source level (Gross, Kujala, Hamalainen, Timmermann, Schnitzler and Salmelin, 2001; Siegel et al, 2008). This source reconstruction technique used an adaptive spatial filter, which passed activity from one specific location of interest with unit gain and maximally suppresses activity from other locations.

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