A Spatiotemporal Map of Reading Aloud

Reading words aloud is a foundational aspect of the acquisition of literacy. The rapid rate at which multiple distributed neural substrates are engaged in this process can only be probed via techniques with high spatiotemporal resolution. We used direct intracranial recordings in a large cohort to create a holistic yet fine-grained map of word processing, enabling us to derive the spatiotemporal neural codes of multiple word attributes critical to reading: lexicality, word frequency and orthographic neighborhood. We found that lexicality is encoded by early activity in mid-fusiform (mFus) cortex and precentral sulcus. Word frequency is also first represented in mFus followed by later engagement of the inferior frontal gyrus (IFG) and inferior parietal sulcus (IPS), and orthographic neighborhood is encoded solely in the IPS. A lexicality decoder revealed high weightings for electrodes in the mFus, IPS, anterior IFG and the pre-central sulcus. These results elaborate the neural codes underpinning extant dual-route models of reading, with parallel processing via the lexical route, progressing from mFus to IFG, and the sub-lexical route, progressing from IPS to anterior IFG.

Non-word Attributes’ Efficiency in Text Mining Authorship Prediction

Literature scripts can be compared to paintings, in an artistic way as well as in the perspective of financial value, whereas the value of these scripts rise and fall depending on their author’s popularity. Authors’ scripts represent a specific style of writing that can be measured and compared using a text mining field called Stylometric. Stylometric analysis depends on some features called authorship attributes, and these attributes or features can be used in special algorithms and methods to reach that aim. Generally, each method selected in the Stylometric field uses a variety of attributes to reach higher prediction accuracy. The aim of this research is to improve the accuracy of authorship prediction in literary works based on the artistic writing style of the authors. To achieve that, a new set of attributes will be used with the Stylometric Authorship Balanced Attribution method, which was chosen in this research among several other machine language methods because of its delicateness in authorship prediction projects. The attributes that have been used by most of the researchers were word frequencies (single word, pair of words, or trio of words), which led to some prediction mistakes. In this research, a new set of attributes is used to decrease these mistakes. These proposed non-word attributes are named sentence length, special characters, and punctuation symbols. The results obtained by using these proposed attributes were excellent.

Prosody and Spoken Word Recognition in Early and Late Spanish–English Bilingual Individuals

PurposeThis study was conducted to compare the influence of word properties on gated single-word recognition in monolingual and bilingual individuals under conditions of native and nonnative accent and to determine whether word-form prosody facilitates recognition in bilingual individuals.MethodWord recognition was assessed in monolingual and bilingual participants when English words were presented with English and Spanish accents in 3 gating conditions: onset only, onset plus prosody/word length only, and onset plus prosody. Word properties were quantified to assess their influence on word recognition in the onset-only condition.ResultsWord recognition speed was proportional to language experience. In the onset-only condition, only word frequency facilitated word recognition across groups. Addition of duration information or prosodic word form did not facilitate word recognition in bilingual individuals the way it did in monolingual individuals. For the bilingual groups, Spanish accent significantly facilitated recognition in the presence of prosodic information. Word attributes were far more consequential in the English accent than in the Spanish accent condition.ConclusionsWord rhyme information, word properties, and accent affect gated word recognition differently in monolingual and bilingual individuals. Top-down strategies emanating from word properties that may facilitate single-word recognition are experience and context dependent and become less available in the presence of a nonnative accent.

An Algorithm of LDA Topic Reduction Based on Rough Set

The topic model LDA can uncover latent topics in text mining, through the probability distribution of words in the text to get the distribution of topics. This approach ignores the correlation between the topics, so in some actual domain it is not enough to reflect the real situations of the topics. We can divide the topics into two classes: key topics and unimportant topics. The key topics can reflect the word attributes well, and other topics can be look as subordinate. Considering the relations of the topics, a topic reduction algorithm is proposed to retain the key topics and delete the redundant topics based on rough set. Because the LDA topics exists uncertainty distribution and rough set can deal with uncertain data well, so the algorithm based on rough set can improve the accuracy of topics analysis.