人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

K. Haberlandt, Methods in Reading Research. F. Ferreira and M. Anes, Why Study Spoken Language? K. Rayner and S.C. Sereno, Eye Movements in Reading Psycholinguistic Studies. M. Kutas and C.K. Van Petten, Psycholinguistics Electrified: Event-Related Brain Potential Investigations. R.E. Remez, A Guide to Research on the Perception of Speech. K.R. Kluender, Speech Perception as a Tractable Problem in Cognitive Science. D.W. Massaro, Psychological Aspects of Speech Perception: Implications for Research and Theory. S.E. Lively, D.B. Pisoni, and S.D. Goldinger, Spoken Word Recognition: Research and Theory. D.A. Balota, Visual Word Recognition: The Journey from Features to Meaning. G.B. Simpson, Context and the Processing of Ambiguous Words. D.C. Mitchell, Sentence Parsing. R.W. Gibbs, Jr., Figurative Thought and Figurative Language. C. Cacciari and S. Glucksberg, Understanding Figurative Language. M. Singer, Discourse Inference Processes. A.C. Graesser, C.L. McMahen, and B.K. Johnson, Question Asking and Answering. P. van den Broek, Comprehension and Memory of Narrative Texts: Inferences and Coherence. C.R. Fletcher, Levels of Representation in Memory for Discourse. A.M. Glenberg, P. Kruley, and W.E. Langston, Analogical Processes in Comprehension: Simulation of a Mental Model. B.K. Britton, Understanding Expository Text: Building Mental Structures to Induce Insights. S.C. Garrod and A.J. Sanford, Resolving Sentences in a Discourse Context: How Discourse Representation Affects Language Understanding. A.J. Sanford and S.C. Garrod, Selective Processing in Text Understanding. W. Kintsch, The Psychology of Discourse Processing. P. Bloom, Recent Controversies in the Study of Language Acquisition. L. Gerken, Child Phonology: Past Research, Present Questions, Future Directions. J. Oakhill, Individual Differences in Children's Text Comprehension. C.A. Perfetti, Psycholinguistics and Reading Ability. R.K. Olson, Language Deficits in Specific Reading Disability. K. Kilborn, Learning a Language Late: Second Language Acquisition in Adults. K. Bock and W. Levelt, Language Production: Grammatical Encoding. H.H. Clark, Discourse in Production. D. Caplan, Language and the Brain. E. Zurif and D. Swinney, The Neuropsychology of Language. P.A. Carpenter, A. Miyake, and M.A Just, Working Memory Constraints in Comprehension: Evidence from Individual Differences, Aphasia, and Aging. A. Garnham, Future Directions. Index.

Handbook of Psycholinguistics

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

The increasing number of experimental studies on second language (L2) processing, frequently with English as the L2, calls for a practical and valid measure of English vocabulary knowledge and proficiency. In a large-scale study with Dutch and Korean speakers of L2 English, we tested whether LexTALE, a 5-min vocabulary test, is a valid predictor of English vocabulary knowledge and, possibly, even of general English proficiency. Furthermore, the validity of LexTALE was compared with that of self-ratings of proficiency, a measure frequently used by L2 researchers. The results showed the following in both speaker groups: (1) LexTALE was a good predictor of English vocabulary knowledge; 2) it also correlated substantially with a measure of general English proficiency; and 3) LexTALE was generally superior to self-ratings in its predictions. LexTALE, but not self-ratings, also correlated highly with previous experimental data on two word recognition paradigms. The test can be carried out on or downloaded from www.lextale.com.

/pdf/introducing-lextale-a-quick-and-valid-lexical-test-for-22scifd9tz.pdf

Introducing LexTALE: A quick and valid Lexical Test for Advanced Learners of English

The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture's grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

https://pure.mpg.de/pubman/item/item_2476926_3/component/file_2632143/Tanner_etal_2014.pdf

The time-course of feature interference in agreement comprehension: Multiple mechanisms and asymmetrical attraction.

Lexical access was examined in English–Spanish bilinguals by monitoring eye fixations on target and lexical competitors as participants followed spoken instructions in English to click on one of the objects presented on a computer (e.g., ‘Click on the beans’). Within-language lexical competitors had a phoneme onset in English that was shared with the target (e.g., ‘beetle’). Between-language lexical competitors had a phoneme onset in Spanish that was shared with the target (‘ bi gote’, ‘mustache’ in English). Participant groups varied in their age-of-acquisition of English and Spanish, and were examined in one of three language modes (Grosjean, 1998, 2001). A strong within-language (English) lexical competition (or cohort effect) was modulated by language mode and age of second language acquisition. A weaker between-language (Spanish) cohort effect was influenced primarily by the age-of-acquisition of Spanish. These results highlight the role of age-of-acquisition and mode in language processing. They are...

/pdf/carpet-or-carcel-the-effect-of-age-of-acquisition-and-1oov1fgtlk.pdf

Carpet or Cárcel: The effect of age of acquisition and language mode on bilingual lexical access

What counts in grammatical number agreement

The production effect (better memory for words read aloud than words read silently) and the picture superiority effect (better memory for pictures than words) both improve item memory in a picture ...

https://www.tandfonline.com/doi/pdf/10.1080/09658211.2018.1510966

Laurel Brehm

Papers

The time-course of feature interference in agreement comprehension: Multiple mechanisms and asymmetrical attraction.

Carpet or Cárcel: The effect of age of acquisition and language mode on bilingual lexical access

What counts in grammatical number agreement

The production effect and the generation effect improve memory in picture naming

Contrast coding choices in a decade of mixed models