Cover not available

Article published In: The Mental Lexicon
Vol. 11:1 (2016) ► pp.115–160

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload PDF
authorization required

What the Networks Tell us about Serial and Parallel Processing

An MEG Study of Language Networks and N-gram Frequency Effects in Overt Picture

 | NovaScape Data Analysis and Consulting, Canada
 | Saint Mary's University, Halifax
 | Dalhousie University, Halifax
 | Dalhousie University, Halifax
 | Dalhousie University, Halifax
 | Dalhousie University, Halifax
Published online: 16 June 2016
DOI logo with link
https://doi.org/10.1075/ml.11.1.06tre
A large literature documenting facilitative effects for high frequency complex words and phrases has led to proposals that high frequency phrases may be stored in memory rather than constructed on-line from their component parts (similarly to high frequency complex words). To investigate this, we explored language processing during a novel picture description task. Using the magneto-encephalographam (MEG) technique and generalised additive mixed-effects modelling, we characterised the effects of the frequency of use of single words as well as two-, three-, and four-word sequences (N-grams) on brain activity during the pre-production stage of unconstrained overt picture description. We expected amplitude responses to be modulated by N-gram frequency such that if N-grams were stored we would see a corresponding reduction or flattening in amplitudes as frequency increased. We found that while amplitude responses to increasing N-gram frequencies corresponded with our expectations about facilitation, the effect appeared at low frequency ranges and for single words only in the phonological network. We additionally found that high frequency N-grams elicited activity increases in some networks, which may be signs of competition or combination depending on the network. Moreover, this effect was not reliable for single word frequencies. These amplitude responses do not clearly support storage for high frequency multi-word sequences. To probe these unexpected results, we turned our attention to network topographies and the timing. We found that, with the exception of an initial ‘sentence’ network, all the networks aggregated peaks from more than one domain (e.g. semantics and phonology). Moreover, although activity moved serially from anterior ventral networks to dorsal posterior networks during processing, as expected in combinatorial accounts, sentence processing and semantic networks ran largely in parallel. Thus, network topographies and timing may account for (some) facilitative effects associated with frequency. We review literature relevant to the network topographies and timing and briefly discuss our results in relation to current processing and theoretical models.
References (146)
Abbot-Smith, K., & Tomasello, M. (2006). Exemplar-learning and schematization in a usage-based account of syntactic acquisition. The Linguistic Review, 231, 275–290. Google Scholar logo with link to Google Scholar
Allen, K., Pereira, F., Botvinick, M., & Goldberg, A.E. (2012). Distinguishing grammatical constructions with fMRI pattern analysis. Brain and Language, 123(3), 174–182. Google Scholar logo with link to Google Scholar
Arbib, M.A., & Lee, J. (2008). Describing visual scenes: Towards a neurolinguistics based on construction grammar. Brain Research, 12251, 146–162. 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, 621, 67–82. 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
Bajada, C.J., Ralph, M.A.L., & Cloutman, L.L. (2015). Transport for language South of the Sylvian Fissure: The routes and history of the main tracts and stations in the ventral language network. Cortex, 691, 141–151. Google Scholar logo with link to Google Scholar
Bannard, C., & Matthews, D. (2008). Stored word sequences in language learning: The effect of familiarity on children’s repetition of four-word combinations. Psychological Science, 191, 241–248. Google Scholar logo with link to Google Scholar
Bardouille, T., & Bow, S. (2012). State-related changes in MEG functional connectivity reveal the task-positive sensorimotor network. PLoS One, 71, 1–8. Google Scholar logo with link to Google Scholar
Bell, A., Brenier, J.M., Gregory, M., Girand, C., & Jurafsky, D. (2009). Predictability effects on durations of content and function words in conversational English. Journal of Memory and Language, 601, 92–111. Google Scholar logo with link to Google Scholar
Bemis, D.K., & Pylkkänen, L. (2012). Basic linguistic composition recruits the left anterior temporal lobe and left angular gyrus during both listening and reading. Cerebral Cortex, bhs170.Google Scholar logo with link to Google Scholar
. (2013). Flexible composition: MEG evidence for the deployment of basic combinatorial linguistic mechanisms in response to task demands. PloS One, 81, e73949. Google Scholar logo with link to Google Scholar
Bien, H., Baayen, H.R., & Levelt, W.J.M. (2011). Frequency effects in the production of Dutch deverbal adjectives and inflected verbs. In R. Bertram, J. Hyönä, & M. Laine (Eds.), Morphology in language comprehension, production and acquisition (pp. 683–715). London: Psychology Press.Google Scholar logo with link to Google Scholar
Binder, J., Desai, R., Graves, W., & Conant, L. (2009, DEC). Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex, 191, 2767–2796. Google Scholar logo with link to Google Scholar
Binney, R.J., Parker, G.J.M., & Lambon Ralph, M.A. (2012). Convergent Connectivity and Graded Specialization in the Rostral Human Temporal Lobe as Revealed by Diffusion-Weighted Imaging Probabilistic Tractography. Journal of Cognitive Neuroscience, 241, 1998–2014. Google Scholar logo with link to Google Scholar
Bock, J. (1986). Meaning, sound, and syntax - Lexical priming in sentence production. Journal of Experimental Psychology-Learning Memory and Cognition, 121, 575–586. Google Scholar logo with link to Google Scholar
Bonaiuto, J. & Arbib, M.A. (2010). Extending the mirror neuron system model, II: what did I just do? A new role for mirror neurons. Biological Cybernetics, 1021, 341–59. Google Scholar logo with link to Google Scholar
Bonner, M.F., Peelle, J.E., Cook, P.A., & Grossman, M. (2013). Heteromodal conceptual processing in the angular gyrus. NeuroImage, 711, 175–186. Google Scholar logo with link to Google Scholar
Bornkessel, I., Zysset, S., Friederici, A.D., von Cramon, D.Y., & Schlesewsky, M. (2005). Who did what to whom? The neural basis of argument hierarchies during language comprehension. NeuroImage, 261, 221–233. Google Scholar logo with link to Google Scholar
Brennan, J., Nir, Y., Hasson, U., Malach, R., Heeger, D., & Pylkkänen, L. (2012). Syntactic structure building in the anterior temporal lobe during natural story listening. Brain and Language, 1201, 163–173. Google Scholar logo with link to Google Scholar
Brookes, M.J., Woolrich, M., Luckhoo, H., Price, D., Hale, J.R., Stephenson, M.C., Barnes, G.R., Smith, S.M., & Morris, P.G., (2011). Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Procedings of the National Academy of Sciences of the United States of America, 1081, 16783–16788. Google Scholar logo with link to Google Scholar
Bybee, J., & McClelland, J.L. (2005). Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition. The Linguistic Review, 221, 381–410. Google Scholar logo with link to Google Scholar
Caplan, D., Chen, E., & Waters, G. (2008). Task-dependent and task-independent neurovascular responses to syntactic processing. Cortex, 441, 257–275. Google Scholar logo with link to Google Scholar
Catani, M., Jones, D., & Fytche, D. (2005). Perisylvian language networks of the human brain. Annals of Neurology, 571, 8–16. Google Scholar logo with link to Google Scholar
Catani, M., Mesulam, M.M., Jakobsen, E., Malik, F., Matersteck, A., Wieneke, C., Thompson, C.K., Thiebaut de Schotten, M., Dell’Acqua, F., Weintraub, S., & Rogalski, E. (2013). A novel frontal pathway underlies verbal fluency in primary progressive aphasia. Brain, awt163, 2619–2628. Google Scholar logo with link to Google Scholar
Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 71, 249–253. Google Scholar logo with link to Google Scholar
Columbus, G. (2010). Processing MWUs: Are MWU subtypes psycholinguistically real? In D. Wood (Ed.), Perspectives on formulaic language: Acquisition and communication (pp. 194–212). London and New York: Continuum.Google Scholar logo with link to Google Scholar
. (2012). An analysis of the processing of multiword units in sentence reading and unit presentation using eye movement data: Implications for theories of mwus. PhD dissertation, University of Alberta, Edmonton, Canada.Google Scholar logo with link to Google Scholar
Culicover, P.W., & Jackendoff, R. (2005). Simpler syntax. Oxford, UK: Oxford University Press. Google Scholar logo with link to Google Scholar
De Cat, C., Klepousniotou, E., & Baayen, R.H. (2015). Representational deficit or processing effect? An electrophysiological study of noun-noun compound processing by very advanced L2 speakers of English. Frontiers in Psychology, 61, 77 Google Scholar logo with link to Google Scholar
Dehaene‐Lambertz, G., Dehaene, S., Anton, J.L., Campagne, A., Ciuciu, P., Dehaene, G.P., Denghien, I., Jobert, A., Lebihan, D., Sigman, M., Pallier, C., & Poline, J.B. (2006). Functional segregation of cortical language areas by sentence repetition. Human Brain Mapping, 271, 360–371. Google Scholar logo with link to Google Scholar
Del Prato, P., & Pylkkanen, L. (2014). MEG evidence for conceptual combination but not numeral quantification in the left anterior temporal lobe during language production. Language Sciences, 51, 524.Google Scholar logo with link to Google Scholar
den Ouden, D.B., Saur, D., Mader, W., Schelter, B., Lukic, S., Wali, E., Timmer, J., & Thompson, C.K. (2012). Network modulation during complex syntactic processing. NeuroImage, 591, 815–823. Google Scholar logo with link to Google Scholar
Dikker, S., & Pylkkänen, L. (2013). Predicting language: MEG evidence for lexical preactivation. Brain and language, 1271, 55–64. Google Scholar logo with link to Google Scholar
Dronkers, N., Wilkins, D., Van Jr., V., Redfern, B., & Jaeger, J.J. (2004). Lesion analysis of the brain areas involved in language comprehension. Cognition, 921, 145–177. Google Scholar logo with link to Google Scholar
Duffau, H., Moritz-Gasser, S., & Mandonnet, E. (2014). A re-examination of the neural basis of language processing: Proposal of a dynamic hodotopical model from data provided by brain stimulation mapping during picture naming. Brain and Language, 1311, 1–10. Google Scholar logo with link to Google Scholar
Ellis, N.C., & Simpson-Vlach, R. (2009). Formulaic language in native speakers: Triangulating psycholinguistics, corpus linguistics, and education. Corpus Linguistics and Linguistic Theory, 5(1), 61–78. Google Scholar logo with link to Google Scholar
Elman, J.L. (2009). On the meaning of words and dinosaur bones: Lexical knowledge without a lexicon. Cognitive Science, 331, 547–582. Google Scholar logo with link to Google Scholar
Embick, D., Hackl, M., Schaeffer, J., Kelepir, M., & Marantz, A. (2001). A magnetoencephalographic component whose latency reflects lexical frequency. Cognitive Brain Research, 101, 345–348. Google Scholar logo with link to Google Scholar
Erickson, L.C., Zielinski, B.A., Zielinski, J.E., Liu, G., Turkeltaub, P.E., Leaver, A.M., & Rauschecker, J.P. (2014). Distinct cortical locations for integration of audiovisual speech and the McGurk effect. Language Sciences, 51, 534.Google Scholar logo with link to Google Scholar
Faraway, J.J. (2006). Extending linear models with R: Generalized linear, mixed effects and nonparametric regression models. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar logo with link to Google Scholar
Fedorenko, E., Duncan, J., & Kanwisher, N. (2012). Language-selective and domain-general regions lie side by side within Broca’s area. Current Biology, 221, 2059–2062. Google Scholar logo with link to Google Scholar
Fornito, A., Zalesky, A., & Breakspear, M. (2013). Graph analysis of the human connectome: Promise, progress, and pitfalls. NeuroImage 801, 426–444. Google Scholar logo with link to Google Scholar
Goldberg, A.E. (2003). Constructions: A new theoretical approach to language. Trends in Cognitive Science, 7(5), 219–224. Google Scholar logo with link to Google Scholar
Goldberg, A. (2006). Constructions at work: the nature of generalization in language. Oxford, UK: Oxford University Press.Google Scholar logo with link to Google Scholar
Gregory, M.L., Raymond, W.D., Bell, A., Fosler-Lussier, E., & Jurafsky, D. (1999). The effects of collocational strength and contextual predictability in lexical production. CLS–99, 351, 151–166.Google Scholar logo with link to Google Scholar
Grewe, T., Bornkessel-Schlesewsky, I., Zysset, S., Wiese, R., von Cramon, D.Y., & Schlesewsky, M. (2007). The role of the posterior superior temporal sulcus in the processing of unmarked transitivity. NeuroImage, 351, 343–352. Google Scholar logo with link to Google Scholar
Griffiths, J.D., Marslen-Wilson, W.D., Stamatakis, E.A., & Tyler, L.K. (2013). Functional organization of the neural language system: Dorsal and ventral pathways are critical for syntax. Cerebral Cortex, 231, 139–147. Google Scholar logo with link to Google Scholar
Grill-Spector, K., Henson, R., & Martin, A. (2006). Repetition and the brain: Neural models of stimulus-specific effects. Trends in Cognitive Sciences, 101, 14–23. Google Scholar logo with link to Google Scholar
Hagoort, P. (2005). On Broca, brain, and binding: A new framework. Trends in Cognitive Science, 91, 416–423. Google Scholar logo with link to Google Scholar
. (2013). MUC (Memory, Unification, Control) and beyond. Frontiers in Psychology, 41. Google Scholar logo with link to Google Scholar
Haller, S., Klarhoefer, M., Schwarzbach, J., Radue, E.W., & Indefrey, P. (2007). Spatial and temporal analysis of fMRI data on word and sentence reading. European Journal of Neuroscience, 261, 2074–2084. Google Scholar logo with link to Google Scholar
Hansen, P.C., Kringelbach, M.L., & Salmelin, R. (2010). MEG: An introduction to methods. Oxford, UK: Oxford University Press. Google Scholar logo with link to Google Scholar
Hastie, T., & Tibshirani, R. (1990). Generalized additive model regression. New York, NY: Chapman & Hall.Google Scholar logo with link to Google Scholar
Hebb, D.O. (1949). The organization of behavior. New York, NY: Wiley & Sons.Google Scholar logo with link to Google Scholar
Hein, G., & Knight, R.T.R.T. (2008). Superior temporal sulcus – it’s my area: Or is it? Journal of Cognitive Neuroscience, 201, 2125–2136. Google Scholar logo with link to Google Scholar
Hendrix, P. (2009). Electrophysiological effects in language production: A picture naming study using generalized additive modeling. MA dissertation, Radboud University, Nijmegen, the Netherlands.Google Scholar logo with link to Google Scholar
Herrmann, B., Maess, B., Hahne, A., Schröger, E., & Friederici, A.D. (2011). Syntactic and auditory spatial processing in the human temporal cortex: An MEG study. NeuroImage, 571, 624–633. Google Scholar logo with link to Google Scholar
Hickok, G., & Poeppel, D. (2004). Dorsal and ventral streams: A framework for understanding aspects of the functional anatomy of language. Cognition, 921, 67–99. Google Scholar logo with link to Google Scholar
. (2007). The cortical organization of speech processing. Nature Reviews Neuroscience, 81, 393–402. Google Scholar logo with link to Google Scholar
Hocking, J., & Price, C.J. (2008). The role of the posterior superior temporal sulcus in audiovisual processing. Cerebral Cortex, 181, 2439–2449. Google Scholar logo with link to Google Scholar
Hurley, R.S., Paller, K.A., Rogalski, E.J., & Mesulam, M.M. (2012). Neural mechanisms of object naming and word comprehension in primary progressive aphasia. The Journal of Neuroscience, 321, 4848–4855. Google Scholar logo with link to Google Scholar
Indefrey, P., Brown, C.M., Hellwig, F., Amunts, K., Herzog, H., & Seitz, R.J. (2001). A neural correlate of syntactic encoding during speech production. Proceedings of the National Academy of Sciences of the United States of America , 981, 5933–5936.
Indefrey, P., & Levelt, W.J. (2004). The spatial and temporal signatures of word production components. Cognition, 921, 101–144. Google Scholar logo with link to Google Scholar
Jackendoff, R. (2002a). Foundations of language. Oxford, UK: Oxford University Press. Google Scholar logo with link to Google Scholar
. (2002b). What’s in the lexicon? In S. Nooteboom, F. Weerman, & F. Wijnen (Eds.), Storage and computation in the language faculty (pp. 23–58). Dordrecht, The Netherlands: Kluwer Academic Press. Google Scholar logo with link to Google Scholar
. (2007). A parallel architecture perspective on language processing. Brain Research, 11461, 2–22. Google Scholar logo with link to Google Scholar
. (2011). What is the human language faculty: Two views. Language, 871, 586–624. Google Scholar logo with link to Google Scholar
Janssen, N., & Barber, H.A. (2012). Phrase frequency effects in language production. PloS One, 71, e33202. Google Scholar logo with link to Google Scholar
Jiang, N., & Nekrasova, T. (2007). The processing of formulaic sequences by second language speakers. The Modern Language Journal, 911, 433–445. Google Scholar logo with link to Google Scholar
Jurafsky, D., Bell, M., & Raymond, W. (2001). Probabilistic relations between words: Evidence from reduction in lexical production. In J. Bybee & P. Hopper (Eds.), Frequency and the emergence of linguistic structure (pp. 229–254). Amsterdam: John Benjamins. Google Scholar logo with link to Google Scholar
Kapatsinski, V., & Radicke, J. (2009). Frequency and the emergence of prefabs: Evidence from monitoring. In R. Corrigan, E. Moravcsik, H. Ouali, & K. Wheatley (Eds.), Formulaic language, Vol. ii: Acquisition, loss, psychological reality, functional explanations (pp. 499–522). Amsterdam: John Benjamins. Google Scholar logo with link to Google Scholar
Keele, L. (2008). Semiparametric regression for the social sciences. New York, NY: Chapman & Hall/CRC.Google Scholar logo with link to Google Scholar
Keller, F., & Lapata, M. (2003). Using the web to obtain frequencies for unseen bigrams. Journal Computational Linguistics, 291, 459–484. Google Scholar logo with link to Google Scholar
Kryuchkova, T., Tucker, B.V., Wurm, L.H., & Baayen, R.H. (2012). Danger and usefulness are detected early in auditory lexical processing: Evidence from electroencephalography. Brain and Language, 1221, 81–91. Google Scholar logo with link to Google Scholar
Lahnakoski, J.M., Glerean, E., Salmi, J., Jääskeläinen, I.P., Sams, M., Hari, R., & Nummenmaa, L. (2012). Naturalistic fMRI mapping reveals superior temporal sulcus as the hub for the distributed brain network for social perception. Frontiers in Human Neuroscience, 61, 233. Google Scholar logo with link to Google Scholar
Lamb, S. (1966). Outline of stratificational grammar. Washington, DC: Georgetown University Press.Google Scholar logo with link to Google Scholar
Lau, E.F., Gramfort, A., Hämäläinen, M.S., & Kuperberg, G.R. (2013). Automatic semantic facilitation in anterior temporal cortex revealed through multimodal neuroimaging. The Journal of Neuroscience, 331, 17174–17181. Google Scholar logo with link to Google Scholar
Levelt, W.J. (1983). Monitoring and self-repair in speech. Cognition, 141, 41–104. Google Scholar logo with link to Google Scholar
Lewis, G., & Poeppel, D. (2014). The role of visual representations during the lexical access of spoken words. Brain and Language, 1341, 1–10. Google Scholar logo with link to Google Scholar
Lewis, G., Solomyak, O., & Marantz, A. (2011). The neural basis of obligatory decomposition of suffixed words. Brain and Language, 1181, 118–127. Google Scholar logo with link to Google Scholar
Li, L., Miller, E.K., & Desimone, R. (2004). The representation of stimulus familiarity in anterior inferior temporal cortex. Journal of Neuropsychology, 691, 1918–1929.Google Scholar logo with link to Google Scholar
Llorens, A., Trébuchon, A., Riès, S., Liégeois-Chauvel, C., & Alario, F. (2014). How familiarization and repetition modulate the picture naming network. Brain and Language, 1331, 47–58. Google Scholar logo with link to Google Scholar
Lu, Z., & Kaufman, L. (2003). Magnetic source imaging of the human brain. New York, NY: Routledge. Google Scholar logo with link to Google Scholar
MacCallum, R., Zhang, S., Preacher, K., & Rucker, D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 71, 19–40. Google Scholar logo with link to Google Scholar
Magnusdottir, S., Fillmore, P., den Ouden, D.B., Hjaltason, H., Rorden, C., Kjartansson, O., Bonilha, L., & Fridriksson, J. (2013). Damage to left anterior temporal cortex predicts impairment of complex syntactic processing: A lesion‐symptom mapping study. Human Brain Mapping, 341, 2715–2723. Google Scholar logo with link to Google Scholar
Makuuchi, M., Bahlmann, J., Anwander, A., & Friederici, A.D. (2009). Segregating the core computational faculty of human language from working memory. Proceedings of the National Academy of Sciences , 1061, 8362–8367.
Makuuchi, M., & Friederici, A.D. (2013). Hierarchical functional connectivity between the core language system and the working memory system. Cortex, 491, 2416–2423. Google Scholar logo with link to Google Scholar
Marantz, A. (2005). Generative linguistics within the cognitive neuroscience of language. The Linguistic Review, 221, 429–445. Google Scholar logo with link to Google Scholar
Marchini, J., Heaton, C., & Ripley, B. (2012). fastICA: FastICA Algorithms to perform ICA and Projection Pursuit (R package version 1.1-16).Google Scholar logo with link to Google Scholar
Marek, A., Habets, B., Jansma, B.M., Nager, W., & Muente, T.F. (2007). Neural correlates of conceptualisation difficulty during the preparation of complex utterances. Aphasiology, 211, 1147–1156. Google Scholar logo with link to Google Scholar
Martin, N., Weisberg, R., & Saffran, E. (1989). Variables influencing the occurrence of naming errors – implications for models of lexical retrieval. Journal of Memory and Language, 281, 462–485. Google Scholar logo with link to Google Scholar
Mei, L., Xue, G., Lu, Z-L., He, Q., Zhang, M., et al. (2014). Artificial language training reveals the neural substrates underlying addressed and assembled phonologies. PLoS ONE, 91, e93548. Google Scholar logo with link to Google Scholar
Mesulam, M.M. (1998). From sensation to cognition. Brain, 1211, 1013–1052. Google Scholar logo with link to Google Scholar
Mesulam, M., Rogalski, E., Wieneke, C., Cobia, D., Rademaker, A., Thompson, C., & Weintraub, S. (2009). Neurology of anomia in the semantic variant of primary progressive aphasia. Brain, awp138, 132(9), 2553–2565. Google Scholar logo with link to Google Scholar
Mesulam, M.M., Wieneke, C., Hurley, R., Rademaker, A., Thompson, C.K., Weintraub, S., & Rogalski, E.J. (2013). Words and objects at the tip of the left temporal lobe in primary progressive aphasia. Brain, 1361, 601–618. Google Scholar logo with link to Google Scholar
Meyer, C.F., Grabowski, R., Han, H.-Y., Mantzouranis, K., & Moses, S. (2003). The world wide web as linguistic corpus. In P. Leistyna & C.F. Meyer (Eds.), Language and computers, corpus analysis: Language structure and language use (pp. 241–254). Amsterdam: Rodopi. Google Scholar logo with link to Google Scholar
Mion, M., Patterson, K., Acosta-Cabronero, J., Pengas, G., Izquierdo-Garcia, D., Hong, Y.T., Fryer, T.D., Williams, G.B., Hodges, J.R., & Nestor, P.J. (2010). What the left and right anterior fusiform gyri tell us about semantic memory. Brain, 1331, 3256–3268. Google Scholar logo with link to Google Scholar
Motulsky, H., & Christopoulos, A. (2004). Fitting models to biological data using linear and non-linear regression. A practical guide to curve fitting. Oxford, UK: Oxford University Press.Google Scholar logo with link to Google Scholar
Newman, A.J., Supalla, T., Hauser, P., Newport, E.L., & Bavelier, D. (2010). Dissociating neural subsystems for grammar by contrasting word order and inflection. Proceedings of the National Academy of Sciences , 1071, 7539–7544.
Noonan, K.A., Jefferies, E., Visser, M., & Lambon Ralph, M.A. (2013). Going beyond inferior prefrontal involvement in semantic control: evidence for the additional contribution of dorsal angular gyrus and posterior middle temporal cortex. Journal of Cognitive Neuroscience, 251:1824–1850. Google Scholar logo with link to Google Scholar
Noppeney, U., & Price, C.J. (2004). An fMRI study of syntactic adaptation. Journal of Cognitive Neuroscience, 161, 702–713. Google Scholar logo with link to Google Scholar
Okada, K., & Hickok, G. (2006). Left posterior auditory-related cortices participate both in speech perception and speech production: Neural overlap revealed by fMRI. Brain and Language, 961, 112–117. Google Scholar logo with link to Google Scholar
Oomen, C., & Postma, A. (2001). Effects of time pressure on mechanisms of speech production and self-monitoring. Journal of Psycholinguistic Research, 301, 163–184. Google Scholar logo with link to Google Scholar
Patterson, K., Nestor, P.J., & Rogers, T.T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 81, 976–987. Google Scholar logo with link to Google Scholar
Price, C. (2010). The anatomy of language: A review of 100 fMRI studies published in 2009. Annals of the New York Academy of Sciences: The Year in Cognitive Neuroscience, 11911, 62–88. Google Scholar logo with link to Google Scholar
. (2012). A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading. NeuroImage, 151, 816–847. Google Scholar logo with link to Google Scholar
Pylkkänen, L., Stringfellow, A., Flagg, E., & Marantz, A. (2000). A neural response sensitive to repetition and phonotactic probability: MEG investigations of lexical access. In Proceedings of Biomag (pp. 363–367).
Pylkkänen, L., Stringfellow, A., & Marantz, A. (2002). Neuromagnetic evidence for the timing of lexical activation: An MEG component sensitive to phonotactic probability but not to neighborhood density. Brain and Language, 811, 666–678. Google Scholar logo with link to Google Scholar
Pylkkanen, L., & Marantz, A. (2003). Tracking the time course of word recognition with meg. Trends in Cognitive Sciences, 71, 187–189. Google Scholar logo with link to Google Scholar
Pylkkänen, L., Feintuch, S., Hopkins, E., & Marantz, A. (2004). Neural correlates of the effects of morphological family frequency and family size: An MEG study. Cognition, 911, B35–B45. Google Scholar logo with link to Google Scholar
Rauschecker, J.P., & Scott, S.K. (2009). Maps and streams in the auditory cortex: Nonhuman primates illuminate human speech processing. Nature Neuroscience, 121, 718–724. Google Scholar logo with link to Google Scholar
R Development Core Team. (2012). R: A language and environment for statistical computing. Vienna, Austria (ISBN 3-900051-07-0).Google Scholar logo with link to Google Scholar
Roelofs, A., Meyer, A.S., & Levelt, W.J. (1998). A case for the lemma/lexeme distinction in models of speaking: Comment on Caramazza and Miozzo (1997). Cognition, 69(2), 219–230. Google Scholar logo with link to Google Scholar
Saur, D., Kreher, B.W., Schnell, S., Kümmerer, D., Kellmeyer, P., Vry, M.S., Umarova, R., Musso, M., Glauche, V., Abel, S., Huber, W., Rijntjes, M., Hennig, J., & Weiller, C. (2008). Ventral and dorsal pathways for language. Proceedings of the National Academy of Sciences , 1051, 18035–18040.
Segaert, K., Kempen, G., Petersson, K.M., & Hagoort, P. (2013). Syntactic priming and the lexical boost effect during sentence production and sentence comprehension: An fMRI study. Brain and Language, 124(2), 174–183. Google Scholar logo with link to Google Scholar
Schwartz, M.F., Kimberg, D.Y., Walker, G.M., Faseyitan, O., Brecher, A., Dell, G.S., & Coslett, H.B. (2009). Anterior temporal involvement in semantic word retrieval: VLSM evidence from aphasia. Brain, 1321, 3411–3427. Google Scholar logo with link to Google Scholar
Sekiguchi, T., Koyama, S., & Kakigi, R. (2000). The effect of word repetition on evoked magnetic responses in the human brain. Japanese Psychological Research, 42(1), 3–14. Google Scholar logo with link to Google Scholar
Simon, D.A., Lewis, G., & Marantz, A. (2012). Disambiguating form and lexical frequency effects in MEG responses using homonyms. Language and Cognitive Processes, 271, 275–287. Google Scholar logo with link to Google Scholar
Siyanova-Chanturia, A., Conklin, K., & Heuven, W.J.van. (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, 371, 776–784. Google Scholar logo with link to Google Scholar
Solomyak, O., & Marantz, A. (2009). Lexical access in early stages of visual word processing: A single-trial correlational MEG study of heteronym recognition. Brain and Language, 1081, 191–196. Google Scholar logo with link to Google Scholar
Sosa, A.V., & MacFarlane, J. (2002). Evidence for frequency-based constituents in the mental lexicon: Collocations involving the word of. Brain and Language, 821, 227–236. Google Scholar logo with link to Google Scholar
Taulu, S., & Simola, J. (2006). Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology, 511, 1759–1768. Google Scholar logo with link to Google Scholar
Thelwall, M., & Sud, P. (2012). Webometrics research with the Bing Search API 2.0. Journal of Informetrics, 61, 44–52. Google Scholar logo with link to Google Scholar
Tremblay, A. (2009). Processing advantages of lexical bundles: Evidence from self-paced reading, word and sentence recall, and free recall with event-related brain potential recordings. PhD dissertation. University of Alberta.Google Scholar logo with link to Google Scholar
. (2013). icaOcularCorrection: Independent components analysis (ICA) based eye-movement correction (R package version 2.4.3.5.1 alpha – pre-release).Google Scholar logo with link to Google Scholar
Tremblay, A., & Baayen, R.H. (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 (pp. 151–173). London and New York: Continuum.Google Scholar logo with link to Google Scholar
Tremblay, A., Derwing, B., Libben, G., & Westbury, C. (2011). Processing advantages of lexical bundles: Evidence from self-paced reading and sentence recall tasks. Language Learning, 611, 569–613. Google Scholar logo with link to Google Scholar
Tremblay, A. & Newman, A.J. (2015). Modeling non-linear relationships in ERP data using mixed-effects regression with R examples. Psychophysiology, 1–16. Google Scholar logo with link to Google Scholar
Troyer, M., O’Donnell, T.J., Fedorenko, E., & Gibson, E. (2011). In N.A. Taatgen & H. van Rijn (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 336–341). Austin, TX: Cognitive Science Society.Google Scholar logo with link to Google Scholar
Tyler, L.K., Marslen-Wilson, W.D., Randall, B., Wright, P., Devereux, B.J., Zhuang, J., Papoutsi, M., & Stamatakis, E.A. (2011). Left inferior frontal cortex and syntax: function, structure and behaviour in patients with left hemisphere damage. Brain, 1341, 415–431. Google Scholar logo with link to Google Scholar
Ullman, M. (2007). The biocognition of the mental lexicon. In M.G. Gaskell (Ed.), The Oxford handbook of psycholinguistics (pp. 267–286). Oxford: Oxford University Press. Google Scholar logo with link to Google Scholar
Underwood, G., Schmitt, N., & Galpin, A. (2004). The eyes have it. An eye-movement study into the processing of formulaic sequences. In N. Schmitt (Ed.), Formulaic sequences. Acquisition, processing and use (pp. 153–172). Amsterdam: John Benjamins. Google Scholar logo with link to Google Scholar
Vigneau, M., Beaucousin, V., Herve, P., Duffau, H., Crivello, F., & Houde, O. (2006). Meta-analyzing left hemisphere language areas: Phonology, semantics, and sentence processing. NeuroImage, 301, 1414–1432. Google Scholar logo with link to Google Scholar
Visser, M., & Ralph, M.L. (2011). Differential contributions of bilateral ventral anterior temporal lobe and left anterior superior temporal gyrus to semantic processes. Journal of Cognitive Neuroscience, 231, 3121–3131. Google Scholar logo with link to Google Scholar
Vrba, J., Taulu, S., Nenonen, J., & Ahonen, A. (2010). Signal space separation beamformer. Brain Topography, 231, 128–133. Google Scholar logo with link to Google Scholar
Walker, G.M., Schwartz, M.F., Kimberg, D.Y., Faseyitan, O., Brecher, A., Dell, G.S., & Coslett, H.B. (2011). Support for anterior temporal involvement in semantic error production in aphasia: New evidence from VLSM. Brain and Language, 1171, 110–122. Google Scholar logo with link to Google Scholar
Westerlund, M., & Pylkkänen, L. (2014). The role of the left anterior temporal lobe in semantic composition vs. semantic memory. Neuropsychologia, 571, 59–70. Google Scholar logo with link to Google Scholar
Willems, R.M., Özyürek, A., & Hagoort, P. (2009). Differential roles for left inferior frontal and superior temporal cortex in multimodal integration of action and language. NeuroImage, 471, 1992–2004. Google Scholar logo with link to Google Scholar
Wilson, S.M., Demarco, A.T., Henry, M.L., Gesierich, B., Babiak, M., Mandelli, M.L., Miller, B.L., & Gorno-Tempini, M.L. (2014). What role does the anterior temporal lobe play in sentence-level processing? Neural correlates of syntactic processing in semantic variant primary progressive Aphasia. Journal of Cognitive Neuroscience, 26, 970. Google Scholar logo with link to Google Scholar
Wood, S.N. (2006). Generalized additive models. New York, NY: Chapman & Hall/CRC. Google Scholar logo with link to Google Scholar
Wood, S.N. (2012). mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness Estimation (R package version 1.7-22).Google Scholar logo with link to Google Scholar
Zhang, L., Xi, J., Xu, G., Shu, H., Wang, X., & Li, P. (2011). Cortical dynamics of acoustic and phonological processing in speech perception. PloS One, 61, e20963. Google Scholar logo with link to Google Scholar
Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., & Smith, G.M. (2009). Mixed effects models and extensions in ecology with R. New York, NY: Springer. Google Scholar logo with link to Google Scholar
Cited by (3)

Cited by three other publications

Lago, Sara, Rachele Pezzetta, Simone Gastaldon, Francesca Peressotti & Giorgio Arcara
2023. Trial‐by‐trial fluctuations of pre‐stimulus alpha power predict language ERPs. Psychophysiology 60:12 DOI logo
Kirjavainen, Minna, Ludivine Crible & Kate Beeching
2022. Can filled pauses be represented as linguistic items? Investigating the effect of exposure on the perception and production of um. Language and Speech 65:2 ► pp. 263 ff. DOI logo
Wray, Alison
2017. Formulaic Sequences as a Regulatory Mechanism for Cognitive Perturbations During the Achievement of Social Goals. Topics in Cognitive Science 9:3 ► pp. 569 ff. DOI logo

This list is based on CrossRef data as of 27 november 2025. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.

Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue