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An Introduction To The Event Related Potential Technique

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

中國科技大學通識教育中心英語文證照奬勵金實施要點 中華民國 105 年 1 月 8 日通識教育委員會議通過 一、 中國科技大學(以下簡稱本校)為鼓勵本校學生通過具公信力機構之英語文能力測驗或 取得證照,特訂定「中國科技大學通識教育中心英語文證照獎勵金實施要點」(以下簡 稱本要點)。 二、 學生於就讀本校期間,通過歐盟共同架構(CEFR)語言能力參考指標 B1(中級)同等級英 語文能力測驗以上(含)者,得依據本要點酌予獎勵。檢測項目請參閱本中心「歐洲語言 學習、教學、評量共同參考架構與各英語檢測分級對照表」(參見附表);未列於標準 對照表之測驗項目不給予獎助。 三、 凡本校學生,除應英系外,均得申請。大學部學生通過同等級以申請一次為限,在學期 間得重複申請,但該次申請之級別不得低於前次。 本獎勵金每學期核發乙次,每次核發全校前 10 名,各名次核發金額如附表。 四、 申請人應提供在學期間,申請當(學)期參加考試之證明文件及成績證明或證照,以憑辦 理。 五、 獎勵金申請作業:請至通識教育中心網頁下載「英語文證照獎勵金申請表」(附件 1), 填妥後檢附成績單正本及影本(背面簽名並註明與正本無異)各一份、本人金融帳戶存 簿(郵局或土地銀行)封面影本送至通識教育中心。 通識教育中心得每學期遴選受獎代表,擇期公開頒奬,並辦理後續請款作業。 六、 奬勵金申請期限:通過相關證照考試半年內應提出申請,逾期視同放棄。 七、 本要點之獎勵金由學校開設通識教育中心專戶,一切收支專款專用;每年度如有剩餘 款,則移至翌年度繼續使用。 八、 本要點經通識教育中心會議審查通過,陳請校長核定後公告實施,修訂時亦同。

文化と言語の多様性の中のCommon European Framework of Reference for Languages: Learning, teaching, assessment (CEFR)--それは基準か? (第10回明海大学大学院応用言語学研究科セミナー 講演)

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal.

/pdf/an-integrated-theory-of-language-production-and-5g9jv1yavn.pdf

An integrated theory of language production and comprehension

Language experience systematically constrains perception of speech contrasts that deviate phonologically and/or phonetically from those of the listener’s native language. These effects are most dramatic in adults, but begin to emerge in infancy and undergo further development through at least early childhood. The central question addressed here is: How do nonnative speech perception findings bear on phonological and phonetic aspects of second language (L2) perceptual learning? A frequent assumption has been that nonnative speech perception can also account for the relative difficulties that late learners have with specific L2 segments and contrasts. However, evaluation of this assumption must take into account the fact that models of nonnative speech perception such as the Perceptual Assimilation Model (PAM) have focused primarily on naïve listeners, whereas models of L2 speech acquisition such as the Speech Learning Model (SLM) have focused on experienced listeners. This chapter probes the assumption that L2 perceptual learning is determined by nonnative speech perception principles, by considering the commonalities and complementarities between inexperienced listeners and those learning an L2, as viewed from PAM and SLM. Among the issues examined are how language learning may affect perception of phonetic vs. phonological information, how monolingual vs. multiple language experience may impact perception, and what these may imply for attunement of speech perception to changes in the listener’s language environment. Commonalities and complementarities 3

Nonnative and second-language speech perception : commonalities and complementarities

Lexical competition in non-native spoken-word recognition

Native American English and non-native (Dutch) listeners identified either the consonant or the vowel in all possible American English CV and VC syllables. The syllables were embedded in multispeaker babble at three signal-to-noise ratios (0, 8, and 16 dB). The phoneme identification performance of the non-native listeners was less accurate than that of the native listeners. All listeners were adversely affected by noise. With these isolated syllables, initial segments were harder to identify than final segments. Crucially, the effects of language background and noise did not interact; the performance asymmetry between the native and non-native groups was not significantly different across signal-to-noise ratios. It is concluded that the frequently reported disproportionate difficulty of non-native listening under disadvantageous conditions is not due to a disproportionate increase in phoneme misidentifications.

/pdf/patterns-of-english-phoneme-confusions-by-native-and-non-50wp1ch4co.pdf

Patterns of English phoneme confusions by native and non-native listeners

How do native listeners process grammatical errors that are frequent in non-native speech? We investigated whether the neural correlates of syntactic processing are modulated by speaker identity. ERPs to gender agreement errors in sentences spoken by a native speaker were compared with the same errors spoken by a non-native speaker. In line with previous research, gender violations in native speech resulted in a P600 effect (larger P600 for violations in comparison with correct sentences), but when the same violations were produced by the non-native speaker with a foreign accent, no P600 effect was observed. Control sentences with semantic violations elicited comparable N400 effects for both the native and the non-native speaker, confirming no general integration problem in foreign-accented speech. The results demonstrate that the P600 is modulated by speaker identity, extending our knowledge about the role of speaker's characteristics on neural correlates of speech processing.

/pdf/when-one-person-s-mistake-is-another-s-standard-usage-the-3if00bugot.pdf

When one person's mistake is another's standard usage: The effect of foreign accent on syntactic processing

Asymmetric mapping from phonetic to lexical representations in second-language listening

https://www.coli.uni-saarland.de/~crocker/documents/Weber-cognition-inpress.pdf

Andrea Weber

Papers

Lexical competition in non-native spoken-word recognition

Patterns of English phoneme confusions by native and non-native listeners

When one person's mistake is another's standard usage: The effect of foreign accent on syntactic processing

Asymmetric mapping from phonetic to lexical representations in second-language listening

The role of prosody in the interpretation of structural ambiguities: a study of anticipatory eye movements.