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Cognitive Linguistics

Editor-in-Chief: Divjak, Dagmar


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Volume 27, Issue 4

Issues

What corpus-based Cognitive Linguistics can and cannot expect from neurolinguistics

Alice Blumenthal-Dramé
  • Corresponding author
  • Freiburg Institute for Advanced Studies, Albert-Ludwigs-Universität Freiburg, Freiburg im Breisgau, Germany
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Published Online: 2016-09-24 | DOI: https://doi.org/10.1515/cog-2016-0062

Abstract

This paper argues that neurolinguistics has the potential to yield insights that can feed back into corpus-based Cognitive Linguistics. It starts by discussing how far the cognitive realism of probabilistic statements derived from corpus data currently goes. Against this background, it argues that the cognitive realism of usage-based models could be further enhanced through deeper engagement with neurolinguistics, but also highlights a number of common misconceptions about what neurolinguistics can and cannot do for linguistic theorizing.

Keywords: corpus-based Cognitive Linguistics; neurolinguistics; cognitive realism; levels of analysis

References

  • Amenta, S. & D. Crepaldi. 2012. Morphological processing as we know it: An analytical review of morphological effects in visual word identification. Frontiers in Psychology 3. 232. doi: CrossrefGoogle Scholar

  • Arai, M. & F. Keller. 2013. The use of verb-specific information for prediction in sentence processing. Language and Cognitive Processes 28(4). 525–560.CrossrefGoogle Scholar

  • Arnon, I. & U. Cohen Priva. 2013. More than words: The effect of multi-word frequency and constituency on phonetic duration. Language and Speech 56(3). 349–371.CrossrefGoogle Scholar

  • Arnon, I. & N. Snider. 2010. More than words: Frequency effects for multi-word phrases. Journal of Memory and Language 62(1). 67–82.CrossrefGoogle Scholar

  • Blakeslee, S. & M. Blakeslee. 2008. The body has a mind of its own: How body maps in your brain help you do (almost) everything better. New York: Random House Trade Paperbacks.Google Scholar

  • Blumenthal-Dramé, A. 2012. Entrenchment in usage-based theories: What corpus data do and do not reveal about the mind. Berlin: Walter de Gruyter.Google Scholar

  • Blumenthal-Dramé, A., V. Glauche, T. Bormann, C. Weiller, M. Musso & B. Kortmann. Under revision. Frequency and chunking in derived words: A parametric fMRI study. Journal of Cognitive Neuroscience.

  • Bresnan, J. 2007. Is syntactic knowledge probabilistic? Experiments with the English dative alternation. In Sam Featherston & Wolfgang Sternefeld (eds.), Roots: Linguistics in search of its evidential base, 75–96. Berlin: Mouton de Gruyter.Google Scholar

  • Bresnan, J. & M. Ford. 2010. Predicting syntax: Processing dative constructions in American and Australian varieties of English. Language 86(1). 168–213.CrossrefGoogle Scholar

  • Bresnan, J. & J. Hay. 2008. Gradient grammar: An effect of animacy on the syntax of give in New Zealand and American English. Lingua 118(2). 245–259.CrossrefGoogle Scholar

  • Bybee, J. 2010. Language, usage and cognition. Cambridge, UK: Cambridge University Press.Google Scholar

  • Chater, N. & M. Oaksford. 2008. The probabilistic mind: Prospects for Bayesian cognitive science. Oxford: Oxford University Press.Google Scholar

  • Chater, N., J. B. Tenenbaum & A. Yuille. 2006. Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Sciences 10(7). 287–291.CrossrefGoogle Scholar

  • Dąbrowska, Ewa. 2016. Cognitive linguistics’ seven deadly sins. Cognitive Linguistics 27(4).

  • Demberg, V. & F. Keller. 2008. Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition 109(2). 193–210.CrossrefGoogle Scholar

  • Dennett, D. C. 1992. Consciousness explained. Boston: Back Bay Books.Google Scholar

  • Dennett, D. C. 2006. Sweet dreams: Philosophical obstacles to a science of consciousness. Cambridge, MA: Mit University Press Group Ltd.

  • Divjak, D. 2016. The role of lexical frequency in the acceptability of syntactic variants: Evidence from that-clauses in Polish. Cognitive Science. doi: .Crossref

  • Divjak, D. & S. Th. Gries. 2012. Frequency effects in language representation – Vol. 2 (Trends in Linguistics. Studies and Monographs [TiLSM] 244.2). Berlin: Walter de Gruyter.

  • Docherty, G. J. & P. Foulkes. 2014. An evaluation of usage-based approaches to the modeling of sociophonetic variability. Lingua 142. 42–56.CrossrefGoogle Scholar

  • Fedorenko, E., A. Nieto-Castañon & N. Kanwisher. 2012. Lexical and syntactic representations in the brain: An fMRI investigation with multi-voxel pattern analyses. Neuropsychologia 50(4). 499–513.CrossrefGoogle Scholar

  • Feldman, L. B., P. Milin, K. W. Cho, F. Moscoso del Prado Martín & P. A. O’Connor. 2015. Must analysis of meaning follow analysis of form? A time course analysis. Frontiers in Human Neuroscience 9. 111. doi: CrossrefGoogle Scholar

  • Fine, A. B., T. F. Jaeger, T. A. Farmer & T. Qian. 2013. Rapid expectation adaptation during syntactic comprehension. PLOS ONE 8(10). e77661.CrossrefGoogle Scholar

  • Frank, S. L., L. J. Otten, G. Galli & G. Vigliocco. 2015. The ERP response to the amount of information conveyed by words in sentences. Brain and Language 140. 1–11.CrossrefGoogle Scholar

  • Frisson, S., K. Rayner & M. J. Pickering. 2005. Effects of contextual predictability and transitional probability on eye movements during reading. Journal of Experimental Psychology: Learning, Memory, and Cognition 31(5). 862–877.Google Scholar

  • Fruchter, J. & A. Marantz. 2015. Decomposition, lookup, and recombination: MEG evidence for the full decomposition model of complex visual word recognition. Brain and Language 143. 81–96.CrossrefGoogle Scholar

  • Gahl, S. & S. M. Garnsey. 2004. Knowledge of grammar, knowledge of usage: Syntactic probabilities affect pronunciation variation. Language 80(4). 748–775.CrossrefGoogle Scholar

  • Gahl, S. & S. M. Garnsey. 2006. Knowledge of grammar includes knowledge of syntactic probabilities. Language 82(2). 405–410.CrossrefGoogle Scholar

  • Gahl, S. & A. C. L. Yu. 2006. Introduction to the special issue on exemplar-based models in linguistics. The Linguistic Review 23(3). 213–216.Google Scholar

  • Goldberg, A. E. 2006. Constructions at work: The nature of generalization in language. Oxford: Oxford University Press.Google Scholar

  • Graves, W. W., R. Desai, C. Humphries, M. S. Seidenberg & J. R. Binder. 2009. Neural systems for reading aloud: A multiparametric approach. Cerebral Cortex 20(8). 1799–1815.Google Scholar

  • Gries, S. Th. 2012. Corpus linguistics, theoretical linguistics, and cognitive/psycholinguistics: Towards more and more fruitful exchanges. Language and Computers 75(1). 41–63.Google Scholar

  • Gries, S. Th. & D. Divjak. 2012. Frequency effects in language learning and processing. Berlin: Walter de Gruyter.Google Scholar

  • Gries, S. Th. & N. C. Ellis. 2015. Statistical measures for usage-based linguistics. Language Learning 65(S1). 228–255.CrossrefGoogle Scholar

  • Gries, S. Th., B. Hampe & D. Schönefeld. 2005. Converging evidence: Bringing together experimental and corpus data on the association of verbs and constructions. Cognitive Linguistics 16(4). 635–676.Google Scholar

  • Gries, S. Th., B. Hampe & D. Schönefeld. 2010. Converging evidence II: More on the association of verbs and constructions. In Sally Rice & John Newman (eds.), Empirical and experimental methods in cognitive/functional research, 59–72. Stanford, CA: CSLI Publications.Google Scholar

  • Griffiths, T. L., N. Chater, C. Kemp, A. Perfors & J. B. Tenenbaum. 2010. Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences 14(8). 357–364.CrossrefGoogle Scholar

  • Griffiths, T. L., M. Steyvers & J. B. Tenenbaum. 2007. Topics in semantic representation. Psychological Review 114(2). 211–244.CrossrefGoogle Scholar

  • Griffiths, T. & J. Tenenbaum. 2006. Statistics and the Bayesian mind. Significance 3(3). 130–133.CrossrefGoogle Scholar

  • Griffiths, T. L., E. Vul & A. N. Sanborn. 2012. Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science 21(4). 263–268.CrossrefGoogle Scholar

  • Halle, M. & A. Marantz. 1994. Some key features of distributed morphology. MIT Working Papers in Linguistics 21. 275–288.Google Scholar

  • Hanna, J. & F. Pulvermüller. 2014. Neurophysiological evidence for whole form retrieval of complex derived words: A mismatch negativity study. Frontiers in Human Neuroscience 8. 886.Google Scholar

  • Hare, M., M. K. Tanenhaus & K. McRae 2007. Understanding and producing the reduced relative construction: Evidence from ratings, editing and corpora. Journal of Memory and Language 56(3). 410–435.CrossrefGoogle Scholar

  • Hauk, O., M. H. Davis & F. Pulvermüller. 2008. Modulation of brain activity by multiple lexical and word form variables in visual word recognition: A parametric fMRI study. NeuroImage 42(3). 1185–1195.CrossrefGoogle Scholar

  • Hay, J. 2001. Lexical frequency in morphology: Is everything relative? Linguistics 39(6). 1041–1070.Google Scholar

  • Hay, J. & R. H. Baayen. 2005. Shifting paradigms: Gradient structure in morphology. Trends in Cognitive Sciences 9(7). 342–348.CrossrefGoogle Scholar

  • Huettig, F. & N. Mani. 2016. Is prediction necessary to understand language? Probably not. Language, Cognition and Neuroscience 31(1). 19–31.CrossrefGoogle Scholar

  • Jaeger, T. F. & N. E. Snider. 2013. Alignment as a consequence of expectation adaptation: Syntactic priming is affected by the prime’s prediction error given both prior and recent experience. Cognition 127(1). 57–83.CrossrefGoogle Scholar

  • Jones, M. N. & D. J. K. Mewhort. 2007. Representing word meaning and order information in a composite holographic lexicon. Psychological Review 114(1). 1–37.CrossrefGoogle Scholar

  • Kamide, Y. 2012. Learning individual talkers’ structural preferences. Cognition 124(1). 66–71.CrossrefGoogle Scholar

  • Kapatsinski, V. & J. Radicke. 2009. Frequency and the emergence of prefabs: Evidence from monitoring. In R. Corrigan, E. A. Moravcsik, H. Ouali & K. Wheatley (eds.), Formulaic language. Vol. 2: Acquisition, loss, psychological reality, and functional explanations, 499–520. Amsterdam: Benjamins.Google Scholar

  • Kleinschmidt, D. F. & T. F. Jaeger. 2015. Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel. Psychological Review 122(2). 148–203.CrossrefGoogle Scholar

  • Küchenhoff, H. & H.-J. Schmid. 2015. Reply to “More (old and new) misunderstandings of collostructional analysis: On Schmid & Küchenhoff” by Stefan Th. Gries. Cognitive Linguistics 26(3). 537–547.Google Scholar

  • Kuperberg, G. R. & T. F. Jaeger. 2016. What do we mean by prediction in language comprehension? Language, Cognition and Neuroscience 31(1). 32–59.CrossrefGoogle Scholar

  • Levy, R. 2008. Expectation-based syntactic comprehension. Cognition 106(3). 1126–1177.CrossrefGoogle Scholar

  • Lewis, A. G. & M. Bastiaansen. 2015. A predictive coding framework for rapid neural dynamics during sentence-level language comprehension. Cortex 68. 155–168.CrossrefGoogle Scholar

  • Lewis, G., O. Solomyak & A. Marantz. 2011. The neural basis of obligatory decomposition of suffixed words. Brain and Language 118(3). 118–127.CrossrefGoogle Scholar

  • Linzen, T. & T. F. Jaeger. 2016. Uncertainty and expectation in sentence processing: Evidence from subcategorization distributions. Cognitive Science 40(6). 1287–1585.Google Scholar

  • Linzen, T., A. Marantz & L. Pylkkänen. 2013. Syntactic context effects in visual word recognition: An MEG study. The Mental Lexicon 8(2). 117–139.CrossrefGoogle Scholar

  • Marr, D., S. Ullman, & T. A. Poggio. 2010. Vision: A computational investigation into the human representation and processing of visual information. Cambridge, MA: MIT Press.Google Scholar

  • McDonald, S. A. & R. C. Shillcock. 2003. Low-level predictive inference in reading: The influence of transitional probabilities on eye movements. Vision Research 43(16). 1735–1751.CrossrefGoogle Scholar

  • Milin, P., D. Divjak, S. Dimitrijević & R. H. Baayen. 2016. Towards cognitively plausible data science in language research. Cognitive Linguistics 27(4).

  • Newmeyer, F. J. 2003. Grammar is grammar and usage is usage. Language 79(4). 682–707.CrossrefGoogle Scholar

  • Newmeyer, F. J. 2006. On Gahl and Garnsey on grammar and usage. Language 82(2). 399–404.CrossrefGoogle Scholar

  • Perfors, A., J. B. Tenenbaum, T. L. Griffiths & F. Xu. 2011. A tutorial introduction to Bayesian models of cognitive development. Cognition 120(3). 302–321.CrossrefGoogle Scholar

  • Schmidtke, D., V. Kuperman, C. L. Gagné & T. L. Spalding. 2015. Competition between conceptual relations affects compound recognition: The role of entropy. Psychonomic Bulletin and Review 23(2). 556–570.Google Scholar

  • Siyanova-Chanturia, A., K. Conklin & W. J. B. Van Heuven. 2011. Seeing a phrase “time and again” matters: The role of phrasal frequency in the processing of multiword sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition 37(3). 776–784.Google Scholar

  • Smith, N. J. & R. Levy. 2013. The effect of word predictability on reading time is logarithmic. Cognition 128(3). 302–319.CrossrefGoogle Scholar

  • Snider, N. & I. Arnon. 2012. A unified lexicon and grammar? Compositional and noncompositional phrases in the lexicon. In S. Gries & D. Divjak (eds.), Frequency effects in language, 127–163. Berlin: Mouton de Gruyter.Google Scholar

  • Solomyak, O. & A. Marantz. 2009. Evidence for early morphological decomposition in visual word recognition. Journal of Cognitive Neuroscience 22(9). 2042–2057.Google Scholar

  • Tenenbaum, J. B., C. Kemp, T. L. Griffiths & N. D. Goodman. 2011. How to grow a mind: Statistics, structure, and abstraction. Science 331(6022). 1279–1285.CrossrefGoogle Scholar

  • Tily, H., S. Gahl, I. Arnon, N. Snider, A. Kothari & J. Bresnan. 2009. Syntactic probabilities affect pronunciation variation in spontaneous speech. Language and Cognition 1(2). 147–165.CrossrefGoogle Scholar

  • Tremblay, A. & R. H. Baayen. 2010. Holistic processing of regular four-word sequences: A behavioral and ERP study of the effects of structure, frequency, and probability on immediate free recall. In D. Wood (ed.), Perspectives on formulaic language: Acquisition and communication, 151–173. London: Continuum.Google Scholar

  • Tremblay, A. & B. V. Tucker. 2011. The effects of N-gram probabilistic measures on the recognition and production of four-word sequences. The Mental Lexicon 6(2). 302–324.CrossrefGoogle Scholar

  • Tremblay, A., B. Derwing, G. Libben & C. Westbury. 2011. Processing advantages of lexical bundles: Evidence from self-paced reading and sentence recall tasks: Lexical bundle processing. Language Learning 61(2). 569–613.CrossrefGoogle Scholar

  • Tressoldi, P. E., F. Sella, M. Coltheart & C. Umiltà. 2012. Using functional neuroimaging to test theories of cognition: A selective survey of studies from 2007 to 2011 as a contribution to the Decade of the Mind Initiative. Cortex 48(9). 1247–1250.CrossrefGoogle Scholar

  • Van Petten, C. & B. J. Luka. 2012. Prediction during language comprehension: Benefits, costs, and ERP components. International Journal of Psychophysiology 83(2). 176–190.CrossrefGoogle Scholar

  • Willems, R. M., S. L. Frank, A. D. Nijhof, P. Hagoort & A. van den Bosch. 2016. Prediction during natural language comprehension. Cerebral Cortex 26(6). 2506–2516.CrossrefGoogle Scholar

  • Wilson, M. P. & S. M. Garnsey. 2009. Making simple sentences hard: Verb bias effects in simple direct object sentences. Journal of Memory and Language 60(3). 368–392.CrossrefGoogle Scholar

  • Wilson, S. M., A. L. Isenberg, & G. Hickok 2009. Neural correlates of word production stages delineated by parametric modulation of psycholinguistic variables. Human Brain Mapping 30(11). 3596–3608.CrossrefGoogle Scholar

About the article

Received: 2016-05-28

Revised: 2016-08-25

Accepted: 2016-08-25

Published Online: 2016-09-24

Published in Print: 2016-11-01


Citation Information: Cognitive Linguistics, Volume 27, Issue 4, Pages 493–505, ISSN (Online) 1613-3641, ISSN (Print) 0936-5907, DOI: https://doi.org/10.1515/cog-2016-0062.

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