This article includes the description of data information from a visual word rhyming judgment task in native Korean, native Chinese and native English speakers.
From this visual word form area, two-way connections must be made to many other parts of the brain, including those responsible for grammar, memories, association and feelings, so that letters and words acquire their particular meanings for us.
In our study, the greater activation in fusiform gyrus and left inferior parietal lobe with L-dopa in the phonological task may relate to the spread of the activation from the visual word form processing area to the regions involved in the retrieving of the word's sound.
Furthermore, the independence from frequency rules out visual word familiarity as an explanation.
In BoW architecture, local features are extracted from images, and each local feature is assigned to the nearest visual word computed from a pre-trained vocabulary.
Another notable limitation is the disjunctive coding principle of directed topic models where they assume a visual word comes from a single latent topic resulting in a suboptimal representation of images.
We compute the spatio-temporal distance from the i-th combined visual word of fea1 to all the other words of fea2 by {mathcal{D}}_i^{mathrm{fea}1}=frac{{displaystyle {sum}_j{mathrm{DM}}_vleft i,jright)}}{{displaystyle {sum}_i{displaystyle {sum}_j{mathrm{DM}}_vleft i,jright)}}} (9).
This analysis was focused on the ventral visual stream of both hemispheres and was similar to ROI analyses from recent fMRI studies on visual word recognition (Brem et al. 2006; Vinckier et al. 2007; Van der Mark et al. 2009, 2011; Richlan et al. 2010; Schurz et al. 2010; Szwed et al. 2011).
The visible variables represent visual word count extracted from the given image collection, and the hidden variables capture the semantic structures (latent topics) embedded in these visual words.
In general, undirected models are subjected to conjunctive coding principle, and they assume that a visual word always comes from a distribution influenced by all the latent topics.
