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Regular Article

Better together: integrating multivariate with univariate methods, and MEG with EEG to study language comprehension

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Received 10 Nov 2022, Accepted 05 Jun 2023, Published online: 12 Jun 2023

References

  • Ahlfors, S. P., Han, J., Belliveau, J. W., & Hämäläinen, M. S. (2010). Sensitivity of MEG and EEG to source orientation. Brain Topography, 23(3), 227–232. https://doi.org/10.1007/s10548-010-0154-x
  • Ahlfors, S. P., Han, J., Lin, F. H., Witzel, T., Belliveau, J. W., Hämäläinen, M. S., & Halgren, E. (2010). Cancellation of EEG and MEG signals generated by extended and distributed sources. Human Brain Mapping, 31(1), 140–149.
  • Aly, M., & Turk-Browne, N. B. (2015). Attention stabilizes representations in the human hippocampus. Cerebral Cortex, 26(2), 783–796. https://doi.org/10.1093/cercor/bhv041
  • Amsel, B. D. (2011). Tracking real-time neural activation of conceptual knowledge using single-trial event-related potentials. Neuropsychologia, 49(5), 970–983. https://doi.org/10.1016/j.neuropsychologia.2011.01.003
  • Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412. https://doi.org/10.1016/j.jml.2007.12.005
  • Bell, A. J., & Sejnowski, T. J. (1997). The “independent components” of natural scenes are edge filters. Vision Research, 37(23), 3327–3338. https://doi.org/10.1016/S0042-6989(97)00121-1
  • Boldt, A., & Yeung, N. (2015). Shared neural markers of decision confidence and error detection. The Journal of Neuroscience, 35(8), 3478–3484. https://doi.org/10.1523/JNEUROSCI.0797-14.2015
  • Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2019). Toward a neurobiologically plausible model of language-related, negative event-related potentials. Frontiers in Psychology, 10, 298. https://doi.org/10.3389/fpsyg.2019.00298
  • Brosnan, M. B., Sabaroedin, K., Silk, T., Genc, S., Newman, D. P., Loughnane, G. M., Fornito, A., O’Connell, R. G., & Bellgrove, M. A. (2020). Evidence accumulation during perceptual decisions in humans varies as a function of dorsal frontoparietal organization. Nature Human Behaviour, 4(8), 844–855. https://doi.org/10.1038/s41562-020-0863-4
  • Brothers, T., Wlotko, E. W., Warnke, L., & Kuperberg, G. R. (2020). Going the extra mile: Effects of discourse context on two late positivities during language comprehension. Neurobiology of Language, 1(1), 135–160. https://doi.org/10.1162/nol_a_00006
  • Brothers, T., Zeitlin, M., Choi Perrachione, A., Choi, C., & Kuperberg, G. (2022). Domain-general conflict monitoring predicts neural and behavioral indices of linguistic error processing during reading comprehension. Journal of Experimental Psychology: General, 151(7), 1502–1519. https://doi.org/10.1037/xge0001130
  • Brouwer, H., & Crocker, M. W. (2017). On the proper treatment of the N400 and P600 in language comprehension. Frontiers in Psychology, 8, 1327. https://doi.org/10.3389/fpsyg.2017.01327
  • Brouwer, H., Crocker, M. W., Venhuizen, N. J., & Hoeks, J. C. J. (2017). A neurocomputational model of the N400 and the P600 in language processing. Cognitive Science, 41, 1318–1352. https://doi.org/10.1111/cogs.12461
  • Brysbaert, M., & New, B. (2009). Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41(4), 977–990. https://doi.org/10.3758/BRM.41.4.977
  • Buzsáki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13(6), 407–420. https://doi.org/10.1038/nrn3241
  • Carlson, T., Tovar, D. A., Alink, A., & Kriegeskorte, N. (2013). Representational dynamics of object vision: The first 1000 ms. Journal of Vision, 13, https://doi.org/10.1167/13.10.1
  • Chao, L. L., Haxby, J. V., & Martin, A. (1999). Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects. Nature Neuroscience, 2(10), 913–919. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10491613 http://www.nature.com/neuro/journal/v2/n10/pdf/nn1099_913.pdf. https://doi.org/10.1038/13217
  • Cheyette, S. J., & Plaut, D. C. (2017). Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension. Cognition, 162, 153–166. https://doi.org/10.1016/j.cognition.2016.10.016
  • Cichy, R. M., & Pantazis, D. (2017). Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space. Neuroimage, 158, 441–454. https://doi.org/10.1016/j.neuroimage.2017.07.023
  • Cichy, R. M., Pantazis, D., & Oliva, A. (2014). Resolving human object recognition in space and time. Nature Neuroscience, 17(3), 455–462. https://doi.org/10.1038/nn.3635
  • Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12(4), 335–359. https://doi.org/10.1016/S0022-5371(73)80014-3
  • Coulson, S., King, J. W., & Kutas, M. (1998). Expect the unexpected: Event-related brain response to morphosyntactic violations. Language and Cognitive Processes, 13(1), 21–58. https://doi.org/10.1080/016909698386582
  • Cuffin, B. N., & Cohen, D. (1979). Comparison of the magnetoencephalogram and electroencephalogram. Electroencephalography and Clinical Neurophysiology, 47(2), 132–146. https://doi.org/10.1016/0013-4694(79)90215-3
  • Dambacher, M., Kliegl, R., Hofmann, M., & Jacobs, A. M. (2006). Frequency and predictability effects on event-related potentials during reading. Brain Research, 1084(1), 89–103. https://doi.org/10.1016/j.brainres.2006.02.010
  • DeLong, K. A., Urbach, T. P., & Kutas, M. (2005). Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8(8), 1117–1121. https://doi.org/10.1038/nn1504
  • Desender, K., Murphy, P., Boldt, A., Verguts, T., & Yeung, N. (2019). A post-decisional neural marker of confidence predicts information-seeking in decision-making. The Journal of Neuroscience, 39(17), 3309–3319. https://doi.org/10.1523/JNEUROSCI.2620-18.2019
  • Desender, K., Ridderinkhof, K. R., & Murphy, P. (2021). Understanding neural signals of post-decisional performance monitoring: An integrative review. Elife, 10, e67556. https://doi.org/10.7554/eLife.67556
  • Devereux, B. J., Clarke, A., Marouchos, A., & Tyler, L. K. (2013). Representational similarity analysis reveals commonalities and differences in the semantic processing of words and objects. The Journal of Neuroscience, 33(48), 18906–18916. https://doi.org/10.1523/JNEUROSCI.3809-13.2013
  • Devlin, J. T., Gonnerman, L. M., Andersen, E. S., & Seidenberg, M. S. (1998). Category-specific semantic deficits in focal and widespread brain damage: A computational account. Journal of Cognitive Neuroscience, 10(1), 77–94. https://www.ncbi.nlm.nih.gov/pubmed/9526084. https://doi.org/10.1162/089892998563798
  • Diedrichsen, J., Ridgway, G. R., Friston, K. J., & Wiestler, T. (2011). Comparing the similarity and spatial structure of neural representations: A pattern-component model. Neuroimage, 55(4), 1665–1678. https://doi.org/10.1016/j.neuroimage.2011.01.044
  • Dikker, S., Assaneo, M. F., Gwilliams, L., Wang, L., & Kösem, A. (2020). Magnetoencephalography and language. Neuroimaging Clinics of North America, 30(2), 229–238. https://doi.org/10.1016/j.nic.2020.01.004
  • Fairhall, S. L., & Caramazza, A. (2013). Brain regions that represent amodal conceptual knowledge. Journal of Neuroscience, 33(25), 10552–10558. https://doi.org/10.1523/JNEUROSCI.0051-13.2013
  • Federmeier, K. D., & Kutas, M. (1999). A rose by any other name: Long-term memory structure and sentence processing. Journal of Memory and Language, 41(4), 469–495. https://doi.org/10.1006/jmla.1999.2660
  • Federmeier, K. D., & Laszlo, S. (2009). Psychology of learning and motivation. Psychology of Learning and Motivation, 51, 1–44. https://doi.org/10.1016/S0079-7421(09)51001-8
  • Federmeier, K. D., Wlotko, E. W., De Ochoa-Dewald, E., & Kutas, M. (2007). Multiple effects of sentential constraint on word processing. Brain Research, 1146, 75–84. https://doi.org/10.1016/j.brainres.2006.06.101
  • Fitz, H., & Chang, F. (2019). Language ERPs reflect learning through prediction error propagation. Cognitive Psychology, 111, 15–52. https://doi.org/10.1016/j.cogpsych.2019.03.002
  • Friston, K. J. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 815–836. https://doi.org/10.1098/rstb.2005.1622
  • Garrard, P., Lambon-Ralph, M. A., Hodges, J. R., & Patterson, K. (2001). Prototypicality, distinctiveness, and intercorrelation: Analyses of the semantic attributes of living and nonliving concepts. Cognitive Neuropsychology, 18(2), 125–174. https://doi.org/10.1080/02643290125857
  • Garrido, L., Vaziri-Pashkam, M., Nakayama, K., & Wilmer, J. (2013). The consequences of subtracting the mean pattern in fMRI multivariate correlation analyses. Frontiers in Neuroscience, 7, 174. https://doi.org/10.3389/fnins.2013.00174
  • Geisler, C. D., & Gerstein, G. L. (1961). The surface EEG in relation to its sources. Electroencephalography and Clinical Neurophysiology, 13(6), 927–934. https://doi.org/10.1016/0013-4694(61)90199-7
  • Gonnerman, L. M., Andersen, E. S., Devlin, J. T., Kempler, D., & Seidenberg, M. S. (1997). Double dissociation of semantic categories in Alzheimer’s disease. Brain and Language, 57(2), 254–279. https://doi.org/10.1006/brln.1997.1752
  • Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Parkkonen, L., & Hämäläinen, M. S. (2014). MNE software for processing MEG and EEG data. Neuroimage, 86, 446–460. https://doi.org/10.1016/j.neuroimage.2013.10.027
  • Grynszpan, F., & Geselowitz, D. B. (1973). Model studies of the magnetocardiogram. Biophysical Journal, 13(9), 911–925. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1484372/pdf/biophysj00707-0056.pdf. https://doi.org/10.1016/S0006-3495(73)86034-5
  • Guggenmos, M., Sterzer, P., & Cichy, R. M. (2018). Multivariate pattern analysis for MEG: A comparison of dissimilarity measures. Neuroimage, 173, 434–447. https://doi.org/10.1016/j.neuroimage.2018.02.044
  • Hagoort, P. (2013). Muc (memory, unification, control) and beyond. Frontiers in Psychology, 4, 416. https://doi.org/10.3389/fpsyg.2013.00416
  • Halgren, E., Dhond, R. P., Christensen, N., Van Petten, C., Marinkovic, K., Lewine, J. D., & Dale, A. M. (2002). N400-like magnetoencephalography responses modulated by semantic context, word frequency, and lexical class in sentences. Neuroimage, 17(3), 1101–1116. https://doi.org/10.1006/nimg.2002.1268
  • Hämäläinen, M. S., Hari, R., Ilmoniemi, R. J., Knuutila, J. E. T., & Lounasmaa, O. V. (1993). Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65(2), 413–497. https://doi.org/10.1103/RevModPhys.65.413
  • Hauk, O., & Pulvermuller, F. (2004). Effects of word length and frequency on the human event-related potential. Clinical Neurophysiology, 115(5), 1090–1103. https://doi.org/10.1016/j.clinph.2003.12.020
  • Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425–2430. https://doi.org/10.1126/science.1063736
  • Hebart, M. N., & Baker, C. I. (2018). Deconstructing multivariate decoding for the study of brain function. Neuroimage, 180, 4–18. https://doi.org/10.1016/j.neuroimage.2017.08.005
  • Helenius, P., Salmelin, R., Service, E., & Connolly, J. (1998). Distinct time courses of word and context comprehension in the left temporal cortex. Brain, 121(6), 1133–1142. https://doi.org/10.1093/brain/121.6.1133
  • Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. (1986). Distributed representations. In D. E. Rumelhart, J. L. McClelland, & PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1: Foundations (pp. 77–109). MIT Press.
  • Hirshorn, E. A., Li, Y., Ward, M. J., Richardson, R. M., Fiez, J. A., & Ghuman, A. S. (2016). Decoding and disrupting left midfusiform gyrus activity during word reading. Proceedings of the National Academy of Sciences, 113(29), 8162–8167. https://doi.org/10.1073/pnas.1604126113
  • Holcomb, P. J., Kounios, J., Anderson, J. E., & West, W. C. (1999). Dual-coding, context-availability, and concreteness effects in sentence comprehension: An electrophysiological investigation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(3), 721–742. https://doi.org/10.1037/0278-7393.25.3.721
  • Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016). Natural speech reveals the semantic maps that tile human cerebral cortex. Nature, 532(7600), 453–458. https://doi.org/10.1038/nature17637
  • Ihara, A., Hayakawa, T., Wei, Q., Munetsuna, S., & Fujimaki, N. (2007). Lexical access and selection of contextually appropriate meaning for ambiguous words. Neuroimage, 38(3), 576–588. https://doi.org/10.1016/j.neuroimage.2007.07.047
  • Jimura, K., & Poldrack, R. A. (2012). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia, 50(4), 544–552. https://doi.org/10.1016/j.neuropsychologia.2011.11.007
  • Jung, T. P., Makeig, S., Humphries, C., Lee, T. W., McKeown, M. J., Iragui, V., & Sejnowski, T. J. (2000). Removing electroencephalographic artifacts by blind source separation. Psychophysiology, 37(2), 163–178. https://doi.org/10.1111/1469-8986.3720163
  • Kappenman, E. S., & Luck, S. J. (2012). ERP components: The ups and downs of brainwave recordings. In S. J. Luck, & E. S. Kappenman (Eds.), The Oxford handbook of event-related potential components (pp. 3–30). Oxford University Press.
  • Karimi-Rouzbahani, H., Ramezani, F., Woolgar, A., Rich, A., & Ghodrati, M. (2021). Perceptual difficulty modulates the direction of information flow in familiar face recognition. Neuroimage, 233, 117896. https://doi.org/10.1016/j.neuroimage.2021.117896
  • Kemp, C., & Tenenbaum, J. B. (2008). Structured models of semantic cognition. Behavioral and Brain Sciences, 31(6), 717–718. https://doi.org/10.1017/S0140525X08005931
  • Khaligh-Razavi, S. M., Cichy, R. M., Pantazis, D., & Oliva, A. (2018). Tracking the spatiotemporal neural dynamics of real-world object size and Animacy in the human brain. Journal of Cognitive Neuroscience, 30(11), 1559–1576. https://doi.org/10.1162/jocn_a_01290
  • Kounios, J., & Holcomb, P. J. (1994). Concreteness effects in semantic processing: ERP evidence supporting dual-coding theory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(4), 804–823. https://doi.org/10.1037/0278-7393.20.4.804
  • Kriegeskorte, N. (2011). Pattern-information analysis: From stimulus decoding to computational-model testing. Neuroimage, 56(2), 411–421. https://doi.org/10.1016/j.neuroimage.2011.01.061
  • Kriegeskorte, N., & Bandettini, P. (2007). Analyzing for information, not activation, to exploit high-resolution fMRI. Neuroimage, 38(4), 649–662. https://doi.org/10.1016/j.neuroimage.2007.02.022
  • Kriegeskorte, N., Mur, M., & Bandettini, P. (2008). How does nature program neuron types? Frontiers in Neuroscience, 2(1), 4. https://doi.org/10.3389/neuro.01.016.2008
  • Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., Tanaka, K., & Bandettini, P. A. (2008). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60(6), 1126–1141. https://doi.org/10.1016/j.neuron.2008.10.043
  • Kuperberg, G. R. (2007). Neural mechanisms of language comprehension: Challenges to syntax. Brain Research, 1146, 23–49. https://doi.org/10.1016/j.brainres.2006.12.063
  • Kuperberg, G. R. (2016). Separate streams or probabilistic inference? What the N400 can tell US about the comprehension of events. Language, Cognition and Neuroscience, 31(5), 602–616. https://doi.org/10.1080/23273798.2015.1130233
  • Kuperberg, G. R., Alexander, E., & Brothers, T. (under review). The posterior P600 does not reflect error correction: An information seeking account of linguistic error processing.
  • Kuperberg, G. R., Brothers, T., & Wlotko, E. (2020). A tale of two positivities and the N400: Distinct neural signatures are evoked by confirmed and violated predictions at different levels of representation. Journal of Cognitive Neuroscience, 32(1), 12–35. https://doi.org/10.1162/jocn_a_01465
  • Kuperberg, G. R., Kreher, D. A., Sitnikova, T., Caplan, D. N., & Holcomb, P. J. (2007). The role of Animacy and thematic relationships in processing active English sentences: Evidence from event-related potentials. Brain and Language, 100(3), 223–237. https://doi.org/10.1016/j.bandl.2005.12.006
  • Kuperberg, G. R., Sitnikova, T., Caplan, D., & Holcomb, P. J. (2003). Electrophysiological distinctions in processing conceptual relationships within simple sentences. Cognitive Brain Research, 17(1), 117–129. https://doi.org/10.1016/S0926-6410(03)00086-7
  • Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62(1), 621–647. https://doi.org/10.1146/annurev.psych.093008.131123
  • Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207(4427), 203–205. https://doi.org/10.1126/science.7350657
  • Kutas, M., & Hillyard, S. A. (1984). Brain potentials during reading reflect word expectancy and semantic association. Nature, 307(5947), 161–163. https://doi.org/10.1038/307161a0
  • Lambon-Ralph, M. A., Jefferies, E., Patterson, K., & Rogers, T. T. (2017). The neural and computational bases of semantic cognition. Nature Reviews Neuroscience, 18(1), 42–55. https://doi.org/10.1038/nrn.2016.150
  • LaRocque, K. F., Smith, M. E., Carr, V. A., Witthoft, N., Grill-Spector, K., & Wagner, A. D. (2013). Global similarity and pattern separation in the human medial temporal lobe predict subsequent memory. The Journal of Neuroscience, 33(13), 5466–5474. https://doi.org/10.1523/JNEUROSCI.4293-12.2013
  • Laszlo, S., & Plaut, D. C. (2012). A neurally plausible parallel distributed processing model of event-related potential word reading data. Brain and Language, 120(3), 271–281. https://doi.org/10.1016/j.bandl.2011.09.001
  • 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, 33(43), 17174–17181. https://doi.org/10.1523/JNEUROSCI.1018-13.2013
  • Lau, E. F., Phillips, C., & Poeppel, D. (2008). A cortical network for semantics: (De)constructing the N400. Nature Reviews Neuroscience, 9(12), 920–933. https://doi.org/10.1038/nrn2532
  • Liuzzi, A. G., Aglinskas, A., & Fairhall, S. L. (2020). General and feature-based semantic representations in the semantic network. Scientific Reports, 10(1), 8931. https://doi.org/10.1038/s41598-020-65906-0
  • Luck, S. J. (2014a). Chapter 8: Baseline correction, averaging, and time-frequency analysis. In S.J. Luck (Ed.), An introduction to the event-related potential technique (2 ed., pp. 249–282). MIT Press.
  • Luck, S. J. (2014b). Chapter 6: Artifact rejection and correction. In S.J. Luck (Ed.), An introduction to the event-related potential technique (2nd ed, pp. 185–218). MIT Press.
  • Maess, B., Herrmann, C. S., Hahne, A., Nakamura, A., & Friederici, A. D. (2006). Localizing the distributed language network responsible for the N400 measured by MEG during auditory sentence processing. Brain Research, 1096(1), 163–172. https://doi.org/10.1016/j.brainres.2006.04.037
  • Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164(1), 177–190. https://doi.org/10.1016/j.jneumeth.2007.03.024
  • Martin, A., & Chao, L. L. (2001). Semantic memory and the brain: Structure and processes. Current Opinion in Neurobiology, 11(2), 194–201. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11301239. https://doi.org/10.1016/S0959-4388(00)00196-3
  • McRae, K., de Sa, V. R., & Seidenberg, M. S. (1997). On the nature and scope of featural representations of word meaning. Journal of Experimental Psychology: General, 126(2), 99–130. https://doi.org/10.1037/0096-3445.126.2.99
  • Mikolov, T., Chen, K., Corrado, G. S., & Dean, J. (2013). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations (ICLR), workshop track proceedings, Scottsdale, Arizona.
  • Moss, H. E., Tyler, L. K., Durrant-Peatfield, M., & Bunn, E. M. (1998). ‘Two eyes of a see-through’: Impaired and intact semantic knowledge in a case of selective deficit for living things. Neurocase, 4(4-5), 291–310. https://doi.org/10.1080/13554799808410629
  • Mumford, D. (1992). On the computational architecture of the neocortex. Biological Cybernetics, 66(3), 241–251. https://doi.org/10.1007/BF00198477
  • Murphy, P. R., Robertson, I. H., Harty, S., & O’Connell, R. G. (2015). Neural evidence accumulation persists after choice to inform metacognitive judgments. Elife, 4, e11946. https://doi.org/10.7554/eLife.11946
  • Nieuwland, M. S., Barr, D. J., Bartolozzi, F., Busch-Moreno, S., Darley, E., Donaldson, D. I., Ferguson, H. J., Fu, X., Heyselaar, E., Huettig, F., & Matthew Husband, E. (2020). Dissociable effects of prediction and integration during language comprehension: Evidence from a large-scale study using brain potentials. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1791), 20180522. https://doi.org/10.1098/rstb.2018.0522
  • Nili, H., Wingfield, C., Walther, A., Su, L., Marslen-Wilson, W., & Kriegeskorte, N. (2014). A toolbox for representational similarity analysis. PLoS Computational Biology, 10(4), e1003553. https://doi.org/10.1371/journal.pcbi.1003553
  • Nour Eddine, S. (2021). Divide and Concur: A predictive coding account of the N400 ERP component [Doctoral dissertation]. Tufts University.
  • Nour Eddine, S., Brothers, T., & Kuperberg, G. R. (2022). The N400 in silico: A review of computational models. In K. Federmeier (Ed.), Psychology of learning and motivation (Vol. 76, pp. 123–206). Academic Press.
  • Nour Eddine, S., Brothers, T., Wang, L., Spratling, M., & Kuperberg, G. R. (2023). A predictive coding model of the N400. bioRxiv, 04.
  • Nunez, P. L. (1990). Localization of brain activity with electroencephalography. In S. Sato (Ed.), Magnetoencephalography (advances in neurology, Vol. 54) (Vol. 54, pp. 39–65). Raven Press.
  • Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011). FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 201, 1. https://doi.org/10.1155/2011/156869
  • Op de Beeck, H. P., Baker, C. I., DiCarlo, J. J., & Kanwisher, N. G. (2006). Discrimination training alters object representations in human extrastriate cortex. The Journal of Neuroscience, 26(50), 13025–13036. https://doi.org/10.1523/JNEUROSCI.2481-06.2006
  • Osterhout, L., Bersick, M., & McKinnon, R. (1997). Brain potentials elicited by words: Word length and frequency predict the latency of an early negativity. Biological Psychology, 46(2), 143–168. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9288411. https://doi.org/10.1016/S0301-0511(97)05250-2
  • Osterhout, L., Kim, A., & Kuperberg, G. R. (2012). The neurobiology of sentence comprehension. In M. Spivey, M. Joannisse, & K. McRae (Eds.), The Cambridge handbook of psycholinguistics (pp. 365–389). Cambridge University Press.
  • Paczynski, M., & Kuperberg, G. R. (2011). Electrophysiological evidence for use of the animacy hierarchy, but not thematic role assignment, during verb-argument processing. Language and Cognitive Processes, 26(9), 1402–1456. https://doi.org/10.1080/01690965.2011.580143
  • Paczynski, M., & Kuperberg, G. R. (2012). Multiple influences of semantic memory on sentence processing: Distinct effects of semantic relatedness on violations of real-world event/state knowledge and animacy selection restrictions. Journal of Memory and Language, 67(4), 426–448. https://doi.org/10.1016/j.jml.2012.07.003
  • Payne, B. R., Lee, C. L., & Federmeier, K. D. (2015). Revisiting the incremental effects of context on word processing: Evidence from single-word event-related brain potentials. Psychophysiology, 52(11), 1456–1469. https://doi.org/10.1111/psyp.12515
  • Perrin, F., Pernier, J., Bertrand, O., & Echallier, J. F. (1989). Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2), 184–187. https://doi.org/10.1016/0013-4694(89)90180-6
  • Proklova, D., Kaiser, D., & Peelen, M. V. (2016). Disentangling representations of object shape and object category in human visual cortex: The animate-inanimate distinction. Journal of Cognitive Neuroscience, 28(5), 680–692. https://doi.org/10.1162/jocn_a_00924
  • Rabovsky, M., Hansen, S. S., & McClelland, J. L. (2018). Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behaviour, 2(9), 693–705. https://doi.org/10.1038/s41562-018-0406-4
  • Rabovsky, M., & McRae, K. (2014). Simulating the N400 ERP component as semantic network error: Insights from a feature-based connectionist attractor model of word meaning. Cognition, 132(1), 68–89. https://doi.org/10.1016/j.cognition.2014.03.010
  • Rabovsky, M., Sommer, W., & Abdel Rahman, R. (2012). The time course of semantic richness effects in visual word recognition. Frontiers in Human Neuroscience, 6, 11. https://doi.org/10.3389/fnhum.2012.00011
  • Randall, B., Moss, H. E., Rodd, J. M., Greer, M., & Tyler, L. K. (2004). Distinctiveness and correlation in conceptual structure: Behavioral and computational studies. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(2), 393–406. https://doi.org/10.1037/0278-7393.30.2.393
  • Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87. https://doi.org/10.1038/4580
  • Rogers, T. T., & McClelland, J. L. (2008). Précis of semantic cognition: A parallel distributed processing approach. Behavioral and Brain Sciences, 31(6), 689–714. https://doi.org/10.1017/S0140525X0800589X
  • Rugg, M. D. (1990). Event-related brain potentials dissociate repetition effects of high-and low-frequency words. Memory & Cognition, 18(4), 367–379. https://doi.org/10.3758/BF03197126
  • Sassenhagen, J., & Fiebach, C. J. (2019). Finding the P3 in the P600: Decoding shared neural mechanisms of responses to syntactic violations and oddball targets. Neuroimage, 200, 425–436. https://doi.org/10.1016/j.neuroimage.2019.06.048
  • Sassenhagen, J., Schlesewsky, M., & Bornkessel-Schlesewsky, I. (2014). The P600-as-P3 hypothesis revisited: Single-trial analyses reveal that the late EEG positivity following linguistically deviant material is reaction time aligned. Brain and Language, 137, 29–39. https://doi.org/10.1016/j.bandl.2014.07.010
  • Sha, L., Haxby, J. V., Abdi, H., Guntupalli, J. S., Oosterhof, N. N., Halchenko, Y. O., & Connolly, A. C. (2015). The animacy continuum in the human ventral vision pathway. Journal of Cognitive Neuroscience, 27(4), 665–678. https://doi.org/10.1162/jocn_a_00733
  • Siedenberg, R., Goodin, D. S., Aminoff, M. J., Rowley, H. A., & Roberts, T. P. L. (1996). Comparison of late components in simultaneously recorded event-related electrical potentials and event-related magnetic fields. Electroencephalography and Clinical Neurophysiology, 99(2), 191–197. https://doi.org/10.1016/0013-4694(96)95215-3
  • Silverstein, M. (1976). Hierarchy of features and ergativity. In R. M. W. Dixon (Ed.), Grammatical categories in Australian languages (pp. 112–171). Australian Institute of Aboriginal Studies.
  • Smith, N. J., & Kutas, M. (2015). Regression-based estimation of ERP waveforms: I. The rERP framework. Psychophysiology, 52(2), 157–168. https://doi.org/10.1111/psyp.12317
  • Soltani, M., & Knight, R. T. (2000). Neural origins of the P300. Critical Reviews™ in Neurobiology, 14(3-4), 26. https://doi.org/10.1615/CritRevNeurobiol.v14.i3-4.20
  • Spratling, M. W. (2016). Predictive coding as a model of cognition. Cognitive Processing, 17(3), 279–305. https://doi.org/10.1007/s10339-016-0765-6
  • Steinhauser, M., & Yeung, N. (2010). Decision processes in human performance monitoring. The Journal of Neuroscience, 30(46), 15643–15653. https://doi.org/10.1523/JNEUROSCI.1899-10.2010
  • Stokes, M. G., Wolff, M. J., & Spaak, E. (2015). Decoding rich spatial information with high temporal resolution. Trends in Cognitive Sciences, 19(11), 636–638. https://doi.org/10.1016/j.tics.2015.08.016
  • Swaab, T. Y., Ledoux, K., Camblin, C. C., & Boudewyn, M. A. (2012). Language-related ERP components. In S. J. Luck, & E. S. Kappenman (Eds.), The Oxford handbook of event-related potential components (pp. 397–439). Oxford University Press.
  • Szewczyk, J. M., & Schriefers, H. (2013). Prediction in language comprehension beyond specific words: An ERP study on sentence comprehension in Polish. Journal of Memory and Language, 68(4), 297–314. https://doi.org/10.1016/j.jml.2012.12.002
  • Tarkiainen, A., Helenius, P., Hansen, P. C., Cornelissen, P. L., & Salmelin, R. (1999). Dynamics of letter string perception in the human occipitotemporal cortex. Brain, 122(11), 2119–2132. https://doi.org/10.1093/brain/122.11.2119
  • Taylor, K. I., Devereux, B. J., & Tyler, L. K. (2011). Conceptual structure: Towards an integrated neuro-cognitive account. Language and Cognitive Processes, 26(9), 1368–1401. https://doi.org/10.1080/01690965.2011.568227
  • Twomey, D. M., Murphy, P. R., Kelly, S. P., & O’Connell, R. G. (2015). The classic P300 encodes a build-to-threshold decision variable. European Journal of Neuroscience, 42(1), 1636–1643. https://doi.org/10.1111/ejn.12936
  • Tyler, L. K., & Moss, H. E. (2001). Towards a distributed account of conceptual knowledge. Trends in Cognitive Sciences, 5(6), 244–252. http://www.ncbi.nlm.nih.gov/pubmed/11390295. https://doi.org/10.1016/S1364-6613(00)01651-X
  • Uusitalo, M. A., & Ilmoniemi, R. J. (1997). Signal-space projection method for separating MEG or EEG into components. Medical & Biological Engineering & Computing, 35(2), 135–140. https://doi.org/10.1007/BF02534144
  • Van Berkum, J. J. A. (2009). The neuropragmatics of ‘simple’ utterance comprehension: An ERP review. In U. Sauerland, & K. Yatsushiro (Eds.), Semantics and pragmatics: From experiment to theory (pp. 276–316). Palgrave Macmillan.
  • van de Meerendonk, N., Kolk, H. H. J., Chwilla, D. J., & Vissers, C. T. W. M. (2009). Monitoring in language perception. Language and Linguistics Compass, 3(5), 1211–1224. https://doi.org/10.1111/j.1749-818X.2009.00163.x
  • Van Petten, C., & Kutas, M. (1990). Interactions between sentence context and word frequencyinevent-related brainpotentials. Memory & Cognition, 18(4), 380–393. https://doi.org/10.3758/BF03197127
  • Van Petten, C., & Luka, B. J. (2012). Prediction during language comprehension: Benefits, costs, and ERP components. International Journal of Psychophysiology, 83(2), 176–190. https://doi.org/10.1016/j.ijpsycho.2011.09.015
  • Van Petten, C., Weckerly, J., McIsaac, H. K., & Kutas, M. (1997). Working memory capacity dissociates lexical and sentential context effects. Psychological Science, 8(3), 238–242. https://doi.org/10.1111/j.1467-9280.1997.tb00418.x
  • Walther, A., Nili, H., Ejaz, N., Alink, A., Kriegeskorte, N., & Diedrichsen, J. (2016). Reliability of dissimilarity measures for multi-voxel pattern analysis. Neuroimage, 137, 188–200. https://doi.org/10.1016/j.neuroimage.2015.12.012
  • Wang, L., Hagoort, P., & Jensen, O. (2018). Language prediction is reflected by coupling between frontal gamma and posterior alpha oscillations. Journal of Cognitive Neuroscience, 30(3), 432–447. https://doi.org/10.1162/jocn_a_01190
  • Wang, L., Kuperberg, G., & Jensen, O. (2018). Specific lexico-semantic predictions are associated with unique spatial and temporal patterns of neural activity. Elife, 7, e39061. https://doi.org/10.7554/eLife.39061
  • Wang, L., Schoot, L., Brothers, T., Alexander, E., Warnke, L., Kim, M., Khan, S., Hämäläinen, M., & Kuperberg, G. R. (2023). Predictive coding across the left fronto-temporal hierarchy during language comprehension. Cerebral Cortex, 4478–4497. https://doi.org/10.1093/cercor/bhac356
  • Wang, L., Wlotko, E., Alexander, E. J., Schoot, L., Kim, M., Warnke, L., & Kuperberg, G. R. (2020). Neural evidence for the prediction of Animacy features during language comprehension: Evidence from MEG and EEG Representational Similarity Analysis. The Journal of Neuroscience, 40(16), 3278–3291. https://doi.org/10.1523/JNEUROSCI.1733-19.2020
  • Warrington, E. K., & McCarthy, R. (1987). Categories of knowledge. Brain, 110(5), 1273–1296. https://doi.org/10.1093/brain/110.5.1273
  • Warrington, E. K., & Shallice, T. (1984). Category specific semantic impairments. Brain, 107(3), 829–853. https://doi.org/10.1093/brain/107.3.829
  • Woolnough, O., Donos, C., Rollo, P. S., Forseth, K. J., Lakretz, Y., Crone, N. E., Fischer-Baum, S., Dehaene, S., & Tandon, N. (2021). Spatiotemporal dynamics of orthographic and lexical processing in the ventral visual pathway. Nature Human Behaviour, 5(3), 389–398. https://doi.org/10.1038/s41562-020-00982-w
  • Wydell, T. N., Vuorinen, T., Helenius, P., & Salmelin, R. (2003). Neural correlates of letter-string length and lexicality during reading in a regular orthography. Journal of Cognitive Neuroscience, 15(7), 1052–1062. https://doi.org/10.1162/089892903770007434
  • Xiang, M., & Kuperberg, G. (2015). Reversing expectations during discourse comprehension. Language, Cognition and Neuroscience, 30(6), 648–672. https://doi.org/10.1080/23273798.2014.995679
  • Zannino, G. D., Perri, R., Pasqualetti, P., Caltagirone, C., & Carlesimo, G. A. (2006). Analysis of the semantic representations of living and nonliving concepts: A normative study. Cognitive Neuropsychology, 23(4), 515–540. https://doi.org/10.1080/02643290542000067

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