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Article published In: Bridging the Methodological Divide: Linguistic and psycholinguistic approaches to formulaic language
Edited by Stefanie Wulff and Debra Titone
[The Mental Lexicon 9:3] 2014
► pp. 437472

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References (61)
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. Google Scholar logo with link to Google Scholar
Arnon, I., & Cohen Priva, U. (2013). More than words: The effect of multi-word frequency and constituency on phonetic duration. Language and Speech, 56(3), 349–371. Google Scholar logo with link to Google Scholar
Arnon, I., & Snider, N. (2010). More than words: frequency effects for multi-word phrases. Journal of Memory and Language, 62(1), 67–82. Google Scholar logo with link to Google Scholar
Baayen, R.H. (2008). Analyzing linguistic data: A practical introduction to statistics using R. Cambridge, UK: Cambridge University Press. Google Scholar logo with link to Google Scholar
Baayen, R.H., Hendrix, P., & Ramscar, M. (2013). Sidestepping the combinatorial explosion: An explanation of n-gram frequency effects based on naive discriminative learning. Language and Speech, 56(3), 329–347. Google Scholar logo with link to Google Scholar
Baayen, R.H., Milin, P., Djurdjevic, D., Hendrix, P., & Marelli, M. (2011). An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118(3), 438–481. Google Scholar logo with link to Google Scholar
Bar, M. (2007). The proactive brain: Using analogies and associations to generate predictions. Trends in Cognitive Sciences, 11(7), 280–289. Google Scholar logo with link to Google Scholar
Bates, D., Mächler, M., & Bolker, B. (2011). lme4: linear mixed-effects models using S4 classes. Retrieved from [URL].
Battig, W., & Montague, W. (1969). Category norms of verbal items in 56 categories: A replication and extension of the Connecticut category norms. Journal of Experimental Psychology, 801, 1–46. Google Scholar logo with link to Google Scholar
Beattie, G., & Butterworth, B. (1979). Contextual probability and word frequency as determinants of pauses and errors in spontaneous speech. Language and Speech, 22(3), 201. Google Scholar logo with link to Google Scholar
Belsley, D.A., Kuh, E., & Welsch, R.E. (2004). Regression diagnostics: Identifying influential data and sources of collinearity. Hoboken, NJ, USA: Wiley-Interscience.Google Scholar logo with link to Google Scholar
Block, C., & Baldwin, C. (2010). Cloze probability and completion norms for 498 sentences: Behavioral and neural validation using event-related potentials. Behavior research methods, 42(3), 665–670. Google Scholar logo with link to Google Scholar
Bloom, P., & Fischler, I. (1980). Completion norms for 329 sentence contexts. Memory and Cognition, 8(6), 631–642. Google Scholar logo with link to Google Scholar
Bormuth, J. (1966). Readability: A new approach. Reading Research Quarterly, 11, 79–132. Google Scholar logo with link to Google Scholar
Brants, T., & Franz, A. (2006). Web 1T 5-gram version 1. Philadelphia, PA USA: Linguistic Data Consortium.Google Scholar logo with link to Google Scholar
Chambers, J.M. (1992). Linear models. In J.M. Chambers & T.J. Hastie (Eds.), Statistical models in S (Chap. 4). USA, NY: Wadsworth & Brooks.Google Scholar logo with link to Google Scholar
Chou, Y.M., Polansky, A.M., & Mason, R.L. (1998). Transforming non-normal data to normality in statistical process control. Journal of Quality Technology, 30(2), 133–141. Google Scholar logo with link to Google Scholar
Conway, C.M., Bauernschmidt, A., Huang, S., & Pisoni, D. (2010). Implicit statistical learning in language processing: word predictability is the key. Cognition, 114(3), 356–371. Google Scholar logo with link to Google Scholar
Criss, A., Aue, W., & Smith, L. (2010). The effects of word frequency and context variability in cued recall. Journal of Memory and Language, 64(2), 119–132. Google Scholar logo with link to Google Scholar
Crowe, S. (1998). Decrease in performance on the verbal fluency test as a function of time: Evaluation in a young healthy sample. Journal of Clinical and Experimental Neuropsychology, 20(3), 391–401. Google Scholar logo with link to Google Scholar
DeLong, K., Urbach, T., & Kutas, M. (2005). Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8(8), 1117. Google Scholar logo with link to Google Scholar
Dilkina, K., McClelland, J.L., & Plaut, D.C. (2010). Are there mental lexicons? The role of semantics in lexical decision. Brain Research, 13651, 66–81. Google Scholar logo with link to Google Scholar
Ellis, W. (1999). A source book of Gestalt psychology. London, UK: Psychology Press.Google Scholar logo with link to Google Scholar
Fano, R.M., & Hawkins, D. (1961). Transmission of information: A statistical theory of communications. American Journal of Physics, 291, 793. Google Scholar logo with link to Google Scholar
Fillenbaum, S., Jones, L., & Rapoport, A. (1963). The predictability of words and their grammatical classes as a function of rate of deletion from a speech transcript1. Journal of Verbal Learning and Verbal Behavior, 2(2), 186–194. Google Scholar logo with link to Google Scholar
Finn, P. (1977). Word frequency, information theory, and cloze performance: A transfer feature theory of processing in reading. Reading Research Quarterly, 13(4), 508–537. Google Scholar logo with link to Google Scholar
Francis, W., & Kucera, H. (1982). Frequency analysis of English usage. Boston, MA, USA: Houghton Mifflin Company.Google Scholar logo with link to Google Scholar
Frank, S.L., & Bod, R. (2011). Insensitivity of the human sentence-processing system to hierarchical structure. Psychological Science, 22(6), 829–834. Google Scholar logo with link to Google Scholar
Griffin, Z., & Bock, K. (1998). Constraint, word frequency, and the relationship between lexical processing levels in spoken word production. Journal of Memory and Language, 38(3), 313–338. Google Scholar logo with link to Google Scholar
Hahn, L.W., & Sivley, R.M. (2011). Entropy, semantic relatedness and proximity. Behavior Research Methods, 43(3), 746–760. Google Scholar logo with link to Google Scholar
Hay, J., Pelucchi, B., Estes, K., & Saffran, J. (2011). Linking sounds to meanings: Infant statistical learning in a natural language. Cognitive Psychology, 63(2), 93–106. Google Scholar logo with link to Google Scholar
Kamide, Y. (2008). Anticipatory processes in sentence processing. Language and Linguistics Compass, 2(4), 647. Google Scholar logo with link to Google Scholar
Kučera, H., & Francis, W. (1967). Computational analysis of present-day American English. Dartmouth, NH, USA: Dartmouth Publishing Group.Google Scholar logo with link to Google Scholar
Kutas, M., & Hillyard, S. (1984). Brain potentials during reading reflect word expectancy and semantic association. Nature, 307(5947), 161–163. Google Scholar logo with link to Google Scholar
McEvoy, C.L., Nelson, D.L., & Komatsu, T. (1999). What is the connection between true and false memories? The differential roles of inter item associations in recall and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(5), 1177. Google Scholar logo with link to Google Scholar
McKenna, M.C. (1986). Cloze procedure as a memory-search process. Journal of Educational Psychology, 781, 433–440. Google Scholar logo with link to Google Scholar
Mirman, D., Graf Estes, K., & Magnuson, J. (2010). Computational modeling of statistical learning: Effects of transitional probability versus frequency and links to word learning. Infancy, 15(5), 471–486. Google Scholar logo with link to Google Scholar
Nelson, D.L., McEvoy, C.L., & Dennis, S. (2000). What is free association and what does it measure? Memory & Cognition, 28(6), 887–899. Google Scholar logo with link to Google Scholar
Nelson, D.L., McEvoy, C.L., & Schreiber, T.A. (1998). The University of South Florida word association, rhyme, and word fragment norms. [URL].
Nelson, D.L., McKinney, V., Gee, N., & Janczura, G. (1998). Interpreting the influence of implicitly activated memories on recall and recognition. Psychological Review, 105(2), 299. Google Scholar logo with link to Google Scholar
Norris, D., & Kinoshita, S. (2008). Perception as evidence accumulation and Bayesian inference: Insights from masked priming. Journal of Experimental Psychology: General, 137(3), 434–455. Google Scholar logo with link to Google Scholar
Owens, M., O’Boyle, P., McMahon, J., Ming, J., & Smith, F. (1997). A comparison of human and statistical language model performance using missing-word tests. Language and Speech, 40(4), 377. Google Scholar logo with link to Google Scholar
Pickering, M., & Garrod, S. (2007). Do people use language production to make predictions during comprehension? Trends in Cognitive Sciences, 11(3), 105–110. Google Scholar logo with link to Google Scholar
R Development Core Team. (2009). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar logo with link to Google Scholar
Ramscar, M., & Gitcho, N. (2007). Developmental change and the nature of learning in childhood. Trends in Cognitive Science, 11(7), 274–279. Google Scholar logo with link to Google Scholar
Ruff, R., Light, R., Parker, S., & Levin, H. (1997). The psychological construct of word fluency. Brain and Language, 57(3), 394–405. Google Scholar logo with link to Google Scholar
Saffran, J.R., Aslin, R.N., & Newport, E.L. (1996). Statistical learning by 8-month-old infants. Science, 2741, 1926–1928. Google Scholar logo with link to Google Scholar
Schwanenflugel, P., & LaCount, K. (1988). Semantic relatedness and the scope of facilitation for upcoming words in sentences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(2), 344. Google Scholar logo with link to Google Scholar
Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 271, 379–423. Google Scholar logo with link to Google Scholar
. (1951). Prediction and entropy of printed English. Bell System Technical Journal, 30(1), 50–64. Google Scholar logo with link to Google Scholar
Shaoul, C., & Westbury, C.F. (2011). Formulaic sequences: Do they exist and do they matter? The Mental Lexicon, 6(1), 171–196. Google Scholar logo with link to Google Scholar
Shaoul, C., Westbury, C.F., & Baayen, R.H. (2013). The subjective frequency of word n-grams. Psihologija, 46(4), 497–537. Google Scholar logo with link to Google Scholar
Smith, N.J. (2011). Scaling up psycholinguistics. Unpublished Doctoral Dissertation Downloaded in December, 2013 from [URL]. San Diego, CA, USA: University of California, San Diego.Google Scholar logo with link to Google Scholar
Smith, N.J., & Levy, R. (2011). Cloze but no cigar: The complex relationship between cloze, corpus, and subjective probabilities in language processing. In Proceedings of the 33rd annual meeting of the cognitive science conference (pp. 1637–1642).
Taylor, W. (1953). “Cloze procedure”: A new tool for measuring readability. Journalism Quarterly, 30(4), 415–433. Google Scholar logo with link to Google Scholar
Willems, R., & Hagoort, P. (2007). Neural evidence for the interplay between language, gesture, and action: A review. Brain and Language, 101(3), 278–289. Google Scholar logo with link to Google Scholar
Wood, S. (2006). Generalized additive models: An introduction with R. USA, NY: CRC Press. Google Scholar logo with link to Google Scholar
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