Bayesian inference; Language comprehension; Orthographic; Prediction; Prediction error; Semantic; Cognitive Neuroscience; Linguistics and Language; Developmental and Educational Psychology; Language and Linguistics; Experimental and Cognitive Psychology; predictive coding
Abstract :
[en] The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive coding - a biologically plausible algorithm for approximating Bayesian inference - offers a promising framework for characterizing the N400. Using an implemented predictive coding computational model, we demonstrate how the N400 can be formalized as the lexico-semantic prediction error produced as the brain infers meaning from the linguistic form of incoming words. We show that the magnitude of lexico-semantic prediction error mirrors the functional sensitivity of the N400 to various lexical variables, priming, contextual effects, as well as their higher-order interactions. We further show that the dynamics of the predictive coding algorithm provides a natural explanation for the temporal dynamics of the N400, and a biologically plausible link to neural activity. Together, these findings directly situate the N400 within the broader context of predictive coding research. More generally, they raise the possibility that the brain may use the same computational mechanism for inference across linguistic and non-linguistic domains.
Disciplines :
Neurosciences & behavior
Author, co-author :
Nour Eddine, Samer; Department of Psychology and Center for Cognitive Science, Tufts University, United States of America. Electronic address: Samer.Nour_Eddine@tufts.edu
Brothers, Trevor; Department of Psychology and Center for Cognitive Science, Tufts University, United States of America, Department of Psychology, North Carolina A&T, United States of America
Wang, Lin; Department of Psychology and Center for Cognitive Science, Tufts University, United States of America, Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, United States of America
SPRATLING, Michael ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) > Cognitive Science and Assessment ; Department of Informatics, King's College London, United Kingdom
Kuperberg, Gina R; Department of Psychology and Center for Cognitive Science, Tufts University, United States of America, Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, United States of America
Aitchison, L., Lengyel, M., With or without you: Predictive coding and Bayesian inference in the brain. Current Opinion in Neurobiology 46 (2017), 219–227, 10.1016/j.conb.2017.08.010.
Amsel, B.D., Tracking real-time neural activation of conceptual knowledge using single-trial event-related potentials. Neuropsychologia 49:5 (2011), 970–983, 10.1016/j.neuropsychologia.2011.01.003.
Baggio, G., Hagoort, P., The balance between memory and unification in semantics: A dynamic account of the N400. Language & Cognitive Processes 26:9 (2011), 1338–1367, 10.1080/01690965.2010.542671.
Barr, D.J., Levy, R., Scheepers, C., Tily, H.J., Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language 68:3 (2013), 255–278, 10.1016/j.jml.2012.11.001.
Bates, D.M., Mächler, M., Bolker, B., Walker, S., Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67:1 (2015), 1–48, 10.18637/jss.v067.i01.
Bentin, S., Event-related potentials, semantic processes, and expectancy factors in word recognition. Brain and Language 31:2 (1987), 308–327.
Bentin, S., McCarthy, G., Wood, C.C., Event-related potentials, lexical decision and semantic priming. Electroencephalography and Clinical Neurophysiology 60 (1985), 343–355.
Blank, H., Davis, M.H., Prediction errors but not sharpened signals simulate multivoxel fMRI patterns during speech perception. PLoS Biology, 14(11), 2016, e1002577.
Bornkessel-Schlesewsky, I., Schlesewsky, M., Toward a neurobiologically plausible model of language-related, negative event-related potentials. Frontiers in Psychology, 10, 2019, 298, 10.3389/fpsyg.2019.00298.
Braun, M., Jacobs, A.M., Hahne, A., Ricker, B., Hofmann, M., Hutzler, F., Model-generated lexical activity predicts graded ERP amplitudes in lexical decision. Brain Research 1073-1074 (2006), 431–439, 10.1016/j.brainres.2005.12.078.
Brothers, T., Greene, S., Kuperberg, G.R., Distinct neural signatures of semantic retrieval and event updating during discourse comprehension. Paper presented at the 27th Annual Meeting of the Cognitive Neuroscience Society, Boston, MA, 2020.
Brothers, T., Kuperberg, G.R., Word predictability effects are linear, not logarithmic: Implications for probabilistic models of sentence comprehension. Journal of Memory and Language, 116, 2021, 10.1016/j.jml.2020.104174.
Brothers, T., Morgan, E., Yacovone, A., Kuperberg, G.R., Multiple predictions during language comprehension: Friends, foes, or indifferent companions?. Cognition, 241, 2023, 105602, 10.1016/j.cognition.2023.105602.
Brothers, T., Swaab, T.Y., Traxler, M.J., Effects of prediction and contextual support on lexical processing: Prediction takes precedence. Cognition 136 (2015), 135–149, 10.1016/j.cognition.2014.10.017.
Brothers, T., Wlotko, E.W., Warnke, L., Kuperberg, G.R., Going the extra mile: Effects of discourse context on two late positivities during language comprehension. Neurobiology of Language 1:1 (2020), 135–160, 10.1162/nol_a_00006.
Brouwer, H., Crocker, M.W., On the proper treatment of the N400 and P600 in language comprehension. Frontiers in Psychology, 8, 2017, 1327, 10.3389/fpsyg.2017.01327.
Brouwer, H., Crocker, M.W., Venhuizen, N.J., Hoeks, J.C.J., A neurocomputational model of the N400 and the P600 in language processing. Cognitive Science 41:Suppl. 6 (2017), 1318–1352, 10.1111/cogs.12461.
Brown, C., Hagoort, P., The processing nature of the N400: Evidence from masked priming. Journal of Cognitive Neuroscience 5:1 (1993), 34–44, 10.1162/jocn.1993.5.1.34.
Brown, M., Kuperberg, G.R., A hierarchical generative framework of language processing: Linking language perception, interpretation, and production abnormalities in schizophrenia. Frontiers in Human Neuroscience, 9, 2015, 643, 10.3389/fnhum.2015.00643.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Amodei, D., Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020, 1877–1901, 10.5555/3495724.3495883.
Brysbaert, M., New, B., 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 (2009), 977–990.
Brysbaert, M., Warriner, A.B., Kuperman, V., Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods 46:3 (2014), 904–911, 10.3758/s13428-013-0403-5.
Carrasco-Ortiz, H., Midgley, K.J., Grainger, J., Holcomb, P.J., Interactions in the neighborhood: Effects of orthographic and phonological neighbors on N400 amplitude. Journal of Neurolinguistics 41 (2017), 1–10, 10.1016/j.jneuroling.2016.06.007.
Carreiras, M., Vergara, M., Perea, M., ERP correlates of transposed-letter priming effects: The role of vowels versus consonants. Psychophysiology 46:1 (2009), 34–42, 10.1111/j.1469-8986.2008.00725.x.
Chater, N., Crocker, M.W., Pickering, M.J., The rational analysis of inquiry: The case of parsing. Oaksford, M., Chater, N., (eds.) Rational models of cognition, 1998, Oxford University Press, New York, 441–468.
Chen, Q., Mirman, D., Competition and cooperation among similar representations: toward a unified account of facilitative and inhibitory effects of lexical neighbors. Psychological review 119:2 (2012), 417–430, 10.1037/a0027175.
Cheyette, S.J., Plaut, D.C., Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension. Cognition 162 (2017), 153–166, 10.1016/j.cognition.2016.10.016.
Clark, A., Whatever next? Predictive brains, situated agents, and the future of cognitive science. The Behavioral and Brain Sciences 36:3 (2013), 181–204, 10.1017/S0140525X12000477.
Coulson, S., King, J.W., Kutas, M., Expect the unexpected: Event-related brain responses to morphosyntactic violations. Language & Cognitive Processes 13:1 (1998), 21–58, 10.1080/016909698386582.
Dambacher, M., Kliegl, R., Hofmann, M., Jacobs, A.M., Frequency and predictability effects on event-related potentials during reading. Brain Research 1084:1 (2006), 89–103, 10.1016/j.brainres.2006.02.010.
Davenport, T., Coulson, S., Predictability and novelty in literal language comprehension: An ERP study. Brain Research 1418 (2011), 70–82, 10.1016/J.Brainres.2011.07.039.
Davis, C.J., Bowers, J.S., Contrasting five different theories of letter position coding: Evidence from orthographic similarity effects. Journal of Experimental Psychology. Human Perception and Performance 32:3 (2006), 535–557, 10.1037/0096-1523.32.3.535.
Deacon, D., Dynowska, A., Ritter, W., Grose-Fifer, J., Repetition and semantic priming of nonwords: Implications for theories of N400 and word recognition. Psychophysiology 41:1 (2004), 60–74, 10.1111/1469-8986.00120.
Dehaene, S., Cohen, L., Sigman, M., Vinckier, F., The neural code for written words: A proposal. Trends in Cognitive Sciences 9:7 (2005), 335–341.
Delaney-Busch, N., Morgan, E., Lau, E., Kuperberg, G.R., Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming. Cognition 187:June 2019 (2019), 10–20, 10.1016/j.cognition.2019.01.001.
Dell, G.S., Chang, F., The P-chain: Relating sentence production and its disorders to comprehension and acquisition. Philosophical Transactions of the Royal Society, B: Biological Sciences, 369(1634), 2014, 20120394, 10.1098/rstb.2012.0394.
DeLong, K.A., Chan, W.H., Kutas, M., Similar time courses for word form and meaning preactivation during sentence comprehension. Psychophysiology, 56(4), 2019, e13312, 10.1111/psyp.13312.
DeLong, K.A., Chan, W.H., Kutas, M., Testing limits: ERP evidence for word form preactivation during speeded sentence reading. Psychophysiology, 58(2), 2021, e13720, 10.1111/psyp.13720.
DeLong, K.A., Kutas, M., Comprehending surprising sentences: Sensitivity of post-N400 positivities to contextual congruity and semantic relatedness. Language, Cognition and Neuroscience 35:8 (2020), 1044–1063, 10.1080/23273798.2019.1708960.
DeLong, K.A., Urbach, T.P., Kutas, M., Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience 8:8 (2005), 1117–1121, 10.1038/nn1504.
van Dijk, T.A., Kintsch, W., Strategies of discourse comprehension. 1983, Academic Press, New York.
Douglas, R.J., Martin, K.A.C., Whitteridge, D., A canonical microcircuit for Neocortex. Neural Computation 1:4 (1989), 480–488, 10.1162/neco.1989.1.4.480.
Elman, J.L., McClelland, J.L., Speech perception as a cognitive process: The interactive activation model. Lass, N., (eds.) Speech and language, Vol. 10, 1984, Academic Press, New York, 337–374.
Federmeier, K.D., Thinking ahead: The role and roots of prediction in language comprehension. Psychophysiology 44:4 (2007), 491–505, 10.1111/j.1469-8986.2007.00531.x.
Federmeier, K.D., Connecting and considering: Electrophysiology provides insights into comprehension. Psychophysiology, 59(1), 2022, e13940, 10.1111/psyp.13940.
Federmeier, K.D., Kutas, M., A rose by any other name: Long-term memory structure and sentence processing. Journal of Memory and Language 41:4 (1999), 469–495, 10.1006/Jmla.1999.2660.
Federmeier, K.D., Laszlo, S., Time for meaning: Electrophysiology provides insights into the dynamics of representation and processing in semantic memory. Psychology of Learning and Motivation 51 (2009), 1–44, 10.1016/S0079-7421(09)51001-8.
Federmeier, K.D., Wlotko, E.W., De Ochoa-Dewald, E., Kutas, M., Multiple effects of sentential constraint on word processing. Brain Research 1146 (2007), 75–84, 10.1016/j.brainres.2006.06.101.
Fitz, H., Chang, F., Language ERPs reflect learning through prediction error propagation. Cognitive Psychology 111 (2019), 15–52, 10.1016/j.cogpsych.2019.03.002.
Fletcher, P.C., Frith, C.D., Perceiving is believing: A Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews. Neuroscience 10:1 (2009), 48–58.
Franklin, N.T., Norman, K.A., Ranganath, C., Zacks, J.M., Gershman, S.J., Structured event memory: A neuro-symbolic model of event cognition. Psychological Review 127:3 (2020), 327–361, 10.1037/rev0000177.
Friston, K.J., A theory of cortical responses. Philosophical Transactions of the Royal Society of London B: Biological Sciences 360:1456 (2005), 815–836, 10.1098/Rstb.2005.1622.
Grainger, J., Cracking the orthographic code: An introduction. Language & Cognitive Processes 23:1 (2008), 1–35, 10.1080/01690960701578013.
Grainger, J., Holcomb, P.J., Watching the word go by: On the time-course of component processes in visual word recognition. Lang & Ling Compass 3 (2009), 128–156.
Grainger, J., Jacobs, A.M., Orthographic processing in visual word recognition: A multiple read-out model. Psychological Review 103:3 (1996), 518–565, 10.1037/0033-295x.103.3.518.
Grainger, J., Kiyonaga, K., Holcomb, P.J., The time course of orthographic and phonological code activation. Psychological Science 17:12 (2006), 1021–1026.
Hagoort, P., Brown, C., Groothusen, J., The syntactic positive shift (SPS) as an ERP measure of syntactic processing. Language & Cognitive Processes 8:4 (1993), 439–483, 10.1080/01690969308407585.
Hanulikova, A., van Alphen, P.M., van Goch, M.M., Weber, A., When one person's mistake is another's standard usage: The effect of foreign accent on syntactic processing. Journal of Cognitive Neuroscience 24:4 (2012), 878–887, 10.1162/jocn_a_00103.
Harm, M.W., Seidenberg, M., Phonology, reading acquisition, and dyslexia: Insights from connectionist models. Psychological Review 106:3 (1999), 491–528.
Harm, M.W., Seidenberg, M.S., Computing the meanings of words in reading: Cooperative division of labor between visual and phonological processes. Psychological Review 111:3 (2004), 662–720, 10.1037/0033-295X.111.3.662.
Hauk, O., Davis, M.H., Ford, M., Pulvermuller, F., Marslen-Wilson, W.D., The time course of visual word recognition as revealed by linear regression analysis of ERP data. Neuroimage 30:4 (2006), 1383–1400, 10.1016/j.neuroimage.2005.11.048.
Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P., de Lange, F.P., A hierarchy of linguistic predictions during natural language comprehension. Proceedings of the National Academy of Sciences of the United States of America, 119(32), 2022, e2201968119, 10.1073/pnas.2201968119.
Holcomb, P.J., Automatic and attentional processing: An event-related brain potential analysis of semantic priming. Brain and Language 35:1 (1988), 66–85.
Holcomb, P.J., Grainger, J., On the time course of visual word recognition: An event-related potential investigation using masked repetition priming. Journal of Cognitive Neuroscience 18:10 (2006), 1631–1643.
Holcomb, P.J., Grainger, J., O'Rourke, T., An electrophysiological study of the effects of orthographic neighborhood size on printed word perception. Journal of Cognitive Neuroscience 14:6 (2002), 938–950, 10.1162/089892902760191153.
Holcomb, P.J., Kounios, J., Anderson, J.E., West, W.C., Dual-coding, context-availability, and concreteness effects in sentence comprehension: An electrophysiological investigation. Journal of Experimental Psychology. Learning, Memory, and Cognition 25:3 (1999), 721–742.
Holcomb, P.J., Neville, H.J., Auditory and visual semantic priming in lexical decision: A comparison using event-related brain potentials. Language & Cognitive Processes 5:4 (1990), 281–312, 10.1080/01690969008407065.
Ito, A., Corley, M., Pickering, M., Martin, A.E., Nieuwland, M.S., Predicting form and meaning: Evidence from brain potentials. Journal of Memory and Language 86 (2016), 157–171, 10.1016/j.jml.2015.10.007.
Kiyonaga, K., Grainger, J., Midgley, K., Holcomb, P.J., Masked cross-modal repetition priming: An event-related potential investigation. Language & Cognitive Processes 22:3 (2007), 337–376.
Kleinschmidt, D.F., Jaeger, F.T., Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel. Psychological Review 122:2 (2015), 148–203, 10.1037/a0038695.
Kounios, J., Green, D.L., Payne, L., Fleck, J.I., Grondin, R., McRae, K., Semantic richness and the activation of concepts in semantic memory: Evidence from event-related potentials. Brain Research 1282 (2009), 95–102, 10.1016/j.brainres.2009.05.092.
Kounios, J., Holcomb, P.J., Concreteness effects in semantic processing: ERP evidence supporting dual-coding theory. Journal of Experimental Psychology. Learning, Memory, and Cognition 20:4 (1994), 804–823, 10.1037/0278-7393.20.4.804.
Kuperberg, G.R., Neural mechanisms of language comprehension: Challenges to syntax. Brain Research 1146 (2007), 23–49, 10.1016/j.brainres.2006.12.063.
Kuperberg, G.R., Separate streams or probabilistic inference? What the N400 can tell us about the comprehension of events. Language, Cognition and Neuroscience 31:5 (2016), 602–616, 10.1080/23273798.2015.1130233.
Kuperberg, G.R., Alexander, E., Brothers, T., The posterior P600 does not reflect error correction: An information seeking account of linguistic error processing. 2024.
Kuperberg, G.R., Brothers, T., Wlotko, E., 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 (2020), 12–35, 10.1162/jocn_a_01465.
Kuperberg, G.R., Jaeger, T.F., What do we mean by prediction in language comprehension?. Language, Cognition and Neuroscience 31:1 (2016), 32–59, 10.1080/23273798.2015.1102299.
Kuperberg, G.R., Kreher, D.A., Sitnikova, T., Caplan, D.N., Holcomb, P.J., The role of animacy and thematic relationships in processing active English sentences: Evidence from event-related potentials. Brain and Language 100:3 (2007), 223–237, 10.1016/j.bandl.2005.12.006.
Kuperberg, G.R., Paczynski, M., Ditman, T., Establishing causal coherence across sentences: An ERP study. Journal of Cognitive Neuroscience 23:5 (2011), 1230–1246, 10.1162/jocn.2010.21452.
Kuperberg, G.R., Sitnikova, T., Caplan, D., Holcomb, P.J., Electrophysiological distinctions in processing conceptual relationships within simple sentences. Cognitive Brain Research 17:1 (2003), 117–129, 10.1016/S0926-6410(03)00086-7.
Kutas, M., In the company of other words: Electrophysiological evidence for single-word and sentence context effects. Language & Cognitive Processes 8 (1993), 533–572.
Kutas, M., Federmeier, K.D., Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology 62 (2011), 621–647, 10.1146/annurev.psych.093008.131123.
Kutas, M., Hillyard, S.A., Brain potentials during reading reflect word expectancy and semantic association. Nature 307:5947 (1984), 161–163, 10.1038/307161a0.
Kuznetsova, A., Brockhoff, P.B., Christensen, R.H.B., lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 2017.
Laszlo, S., Armstrong, B.C., PSPs and ERPs: Applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended event-related potential reading data. Brain and Language 132 (2014), 22–27, 10.1016/j.bandl.2014.03.002.
Laszlo, S., Federmeier, K.D., A beautiful day in the neighborhood: An event-related potential study of lexical relationships and prediction in context. Journal of Memory and Language 61:3 (2009), 326–338, 10.1016/j.jml.2009.06.004.
Laszlo, S., Federmeier, K.D., The N400 as a snapshot of interactive processing: Evidence from regression analyses of orthographic neighbor and lexical associate effects. Psychophysiology 48:2 (2011), 176–186, 10.1111/j.1469-8986.2010.01058.x.
Laszlo, S., Federmeier, K.D., Never seem to find the time: Evaluating the physiological time course of visual word recognition with regression analysis of single-item event-related potentials. Language, Cognition and Neuroscience 29:5 (2014), 642–661.
Laszlo, S., Plaut, D.C., A neurally plausible parallel distributed processing model of event-related potential word reading data. Brain and Language 120:3 (2012), 271–281, 10.1016/j.bandl.2011.09.001.
Lau, E.F., Gramfort, A., Hämäläinen, M.S., Kuperberg, G.R., Automatic semantic facilitation in anterior temporal cortex revealed through multimodal neuroimaging. The Journal of Neuroscience 33:43 (2013), 17174–17181, 10.1523/Jneurosci.1018-13.2013.
Lau, E.F., Phillips, C., Poeppel, D., A cortical network for semantics: (De)constructing the N400. Nature Reviews. Neuroscience 9:12 (2008), 920–933, 10.1038/nrn2532.
Lau, E.F., Weber, K., Gramfort, A., Hämäläinen, M.S., Kuperberg, G.R., Spatiotemporal signatures of lexico-semantic prediction. Cerebral Cortex 26:4 (2016), 1377–1387, 10.1093/cercor/bhu219.
Lee, C.-L., Federmeier, K.D., To watch, to see, and to differ: An event-related potential study of concreteness effects as a function of word class and lexical ambiguity. Brain and Language 104:2 (2008), 145–158, 10.1016/j.bandl.2007.06.002.
Levy, R., Expectation-based syntactic comprehension. Cognition 106:3 (2008), 1126–1177, 10.1016/j.cognition.2007.05.006.
McClelland, J.L., Elman, J.L., The TRACE model of speech perception. Cognitive Psychology 18:1 (1986), 1–86, 10.1016/0010-0285(86)90015-0.
McClelland, J.L., Rumelhart, D.E., An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review 88:5 (1981), 375–407, 10.1037//0033-295x.88.5.375.
Meade, G., Mahnich, C., Holcomb, P.J., Grainger, J., Orthographic neighborhood density modulates the size of transposed-letter priming effects. Cognitive, Affective, & Behavioral Neuroscience 21:5 (2021), 948–959, 10.3758/s13415-021-00905-w.
Meade, G., Midgley, K.J., Dijkstra, T., Holcomb, P.J., Cross-language neighborhood effects in learners indicative of an integrated lexicon. Journal of Cognitive Neuroscience 30:1 (2018), 70–85, 10.1162/jocn_a_01184.
van de Meerendonk, N., Kolk, H.H.J., Chwilla, D.J., Vissers, C.T.W.M., Monitoring in language perception. Lang & Ling Compass 3:5 (2009), 1211–1224, 10.1111/j.1749-818X.2009.00163.x.
Michaelov, J., Coulson, S., Bergen, B.K., So Cloze Yet So Far: N400 Amplitude Is Better Predicted by Distributional Information Than Human Predictability Judgements," in. IEEE Transactions on Cognitive and Developmental Systems 15:3 (Sept. 2023), 1033–1042, 10.1109/TCDS.2022.3176783.
Millidge, B., Tschantz, A., Buckley, C.J., Predictive coding approximates backprop along arbitrary computation graphs. 2020, Cornell University (arXiv).
Misra, M., Holcomb, P.J., Event-related potential indices of masked repetition priming. Psychophysiology 40:1 (2003), 115–130, 10.1111/1469-8986.00012.
Mumford, D., On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biological Cybernetics 66:3 (1992), 241–251, 10.1007/BF00198477.
Münte, T.F., Heinze, H.J., Matzke, M., Wieringa, B.M., Johannes, S., Brain potentials and syntactic violations revisited: No evidence for specificity of the syntactic positive shift. Neuropsychologia 36:3 (1998), 217–226.
Narayanan, S., Jurafsky, D., Combining structure and probabilities in a Bayesian model of human sentence processing. Paper presented at the CUNY Conference on Human Sentence Processing, New York, 2002.
Nieuwland, M.S., Do “early” brain responses reveal word form prediction during language comprehension? A critical review. Neuroscience and Biobehavioral Reviews 96 (2019), 367–400, 10.1016/j.neubiorev.2018.11.019.
Nieuwland, M.S., Barr, D.J., Bartolozzi, F., Busch-Moreno, S., Darley, E., Donaldson, D.I., Wolfsthurn, S., Dissociable effects of prediction and integration during language comprehension: Evidence from a large-scale study using brain potentials. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 375(1791), 2020, 20180522, 10.1098/rstb.2018.0522.
Nieuwland, M.S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., Huettig, F., Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. Elife, 7, 2018, e33468, 10.7554/eLife.33468.
Nobre, A.C., McCarthy, G., Language-related field potentials in the anterior-medial temporal lobe: II. Effects of word type and semantic priming. The Journal of Neuroscience 15:2 (1995), 1090–1098.
Norris, D., The Bayesian reader: Explaining word recognition as an optimal Bayesian decision process. Psychological Review 113:2 (2006), 327–357, 10.1037/0033-295X.113.2.327.
Nour Eddine, S., Brothers, T., Kuperberg, G.R., The N400 in silico: A review of computational models. Federmeier, K., (eds.) Psychology of learning and motivation, Vol. 76, 2022, Academic Press, 123–206.
Nour Eddine, S., Brothers, T., Wang, L., Kuperberg, G.R., Contextual facilitation in language comprehension: Insights from a unified predictive coding framework. Paper presented at the 15th Annual Meeting of the Society for the Neurobiology of Language, Marseille, France, 2023.
Payne, B.R., Federmeier, K.D., Contextual constraints on lexico-semantic processing in aging: Evidence from single-word event-related brain potentials. Brain Research 1687 (2018), 117–128, 10.1016/j.brainres.2018.02.021.
Payne, B.R., Lee, C.L., Federmeier, K.D., Revisiting the incremental effects of context on word processing: Evidence from single-word event-related brain potentials. Psychophysiology 52:11 (2015), 1456–1469, 10.1111/psyp.12515.
Pearl, J., Probabilistic reasoning in intelligent systems: Networks of plausible inference. 1988, Morgan Kaufmann, San Mateo, California.
Price, C.J., Devlin, J.T., The interactive account of ventral occipitotemporal contributions to reading. Trends in Cognitive Sciences 15:6 (2011), 246–253, 10.1016/J.Tics.2011.04.001.
R Core Team, R: A language and environment for statistical computing (version 4.2.2). 2022, R Foundation for Statistical Computing, Vienna, Austria Retrieved from https://www.R-project.org.
Rabovsky, M., Change in a probabilistic representation of meaning can account for N400 effects on articles: A neural network model. Neuropsychologia, 143, 2020, 107466, 10.1016/j.neuropsychologia.2020.107466.
Rabovsky, M., Hansen, S.S., McClelland, J.L., Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behaviour 2:9 (2018), 693–705, 10.1038/s41562-018-0406-4.
Rabovsky, M., McRae, K., Simulating the N400 ERP component as semantic network error: Insights from a feature-based connectionist attractor model of word meaning. Cognition 132:1 (2014), 68–89, 10.1016/j.cognition.2014.03.010.
Rabovsky, M., Sommer, W., Abdel Rahman, R., Implicit word learning benefits from semantic richness: Electrophysiological and behavioral evidence. Journal of Experimental Psychology. Learning, Memory, and Cognition 38:4 (2012), 1076–1083, 10.1037/a0025646.
Rabovsky, M., Sommer, W., Abdel Rahman, R., The time course of semantic richness effects in visual word recognition. Frontiers in Human Neuroscience, 6, 2012, 11, 10.3389/fnhum.2012.00011.
Rao, R.P.N., Ballard, D.H., Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Computation 9:4 (1997), 721–763, 10.1162/neco.1997.9.4.721.
Rao, R.P.N., Ballard, D.H., Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2:1 (1999), 79–87, 10.1038/4580.
Rescorla, R.A., Wagner, A.R., A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. Prokasy, W.E., Black, A.H., (eds.) Classical conditioning II: Current research and theory, 1972, Appleton- Century-Crofts, New York, 64–99.
Rugg, M.D., The effects of semantic priming and word repetition on event-related potentials. Psychophysiology 22 (1985), 642–647.
Rugg, M.D., Event-related potentials dissociate repetition effects of high and low frequency words. Memory and Cognition 18 (1990), 367–379.
Rugg, M.D., Doyle, M.C., Melan, C., An event-related potential study of the effects of within- and across-modality word repetition. Language & Cognitive Processes 8:4 (1993), 357–377, 10.1080/01690969308407582.
Rugg, M.D., Nieto-Vegas, M., Modality-specific effects of immediate word repetition: Electrophysiological evidence. Neuroreport 10:12 (1999), 2661–2664, 10.1097/00001756-199908200-00041.
Satterthwaite, F.E., An approximate distribution of estimates of variance components. Biometric Bulletin 2:6 (1946), 110–114.
Seidenberg, M.S., McClelland, J.L., A distributed developmental model of word recognition and naming. Psychological Review 96:4 (1989), 523–568.
Smith, N.J., Levy, R., The effect of word predictability on reading time is logarithmic. Cognition 128:3 (2013), 302–319, 10.1016/j.cognition.2013.02.013.
Sohoglu, E., Davis, M.H., Rapid computations of spectrotemporal prediction error support perception of degraded speech. Elife, 9, 2020, 10.7554/eLife.58077.
Song, Y., Lukasiewicz, T., Xu, Z., Bogacz, R., Can the brain do backpropagation? Exact implementation of backpropagation in predictive coding networks. Paper presented at the Advances in Neural Information Processing Systems, 33, 2020, NeurIPS 2020.
Spratling, M.W., Predictive coding as a model of biased competition in visual attention. Vision Research 48:12 (2008), 1391–1408, 10.1016/j.visres.2008.03.009.
Spratling, M.W., Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function. Neural Computation 24:1 (2012), 60–103, 10.1162/NECO_a_00222.
Spratling, M.W., Image segmentation using a sparse coding model of cortical area V1. IEEE Transactions on Image Processing 22:4 (2013), 1631–1643, 10.1109/tip.2012.2235850.
Spratling, M.W., A single functional model of drivers and modulators in cortex. Journal of Computational Neuroscience 36:1 (2014), 97–118, 10.1007/s10827-013-0471-7.
Spratling, M.W., A neural implementation of Bayesian inference based on predictive coding. Connection Science 28:4 (2016), 346–383, 10.1080/09540091.2016.1243655.
Spratling, M.W., Predictive coding as a model of cognition. Cognitive Processing 17:3 (2016), 279–305, 10.1007/s10339-016-0765-6.
Spratling, M.W., A review of predictive coding algorithms. Brain and Cognition 112 (2017), 92–97, 10.1016/j.bandc.2015.11.003.
Spratling, M.W., De Meyer, K., Kompass, R., Unsupervised learning of overlapping image components using divisive input modulation. Computational Intelligence and Neuroscience, 2009, 2009, 381457, 10.1155/2009/381457.
Szewczyk, J.M., Federmeier, K.D., Context-based facilitation of semantic access follows both logarithmic and linear functions of stimulus probability. Journal of Memory and Language, 123, 2022, 10.1016/j.jml.2021.104311.
Taylor, W., 'Cloze' procedure: A new tool for measuring readability. Journalism Quarterly 30 (1953), 415–433.
Thornhill, D.E., Van Petten, C., Lexical versus conceptual anticipation during sentence processing: Frontal positivity and N400 ERP components. International Journal of Psychophysiology 83:3 (2012), 382–392, 10.1016/j.ijpsycho.2011.12.007.
Van Berkum, J.J.A., Hagoort, P., Brown, C.M., Semantic integration in sentences and discourse: Evidence from the N400. Journal of Cognitive Neuroscience 11:6 (1999), 657–671, 10.1162/089892999563724.
Van Berkum, J.J.A., Van den Brink, D., Tesink, C.M.J.Y., Kos, M., Hagoort, P., The neural integration of speaker and message. Journal of Cognitive Neuroscience 20:4 (2008), 580–591.
Van Berkum, J.J.A., Zwitserlood, P., Hagoort, P., Brown, C.M., When and how do listeners relate a sentence to the wider discourse? Evidence from the N400 effect. Cognitive Brain Research 17:3 (2003), 701–718.
Van Petten, C., Kutas, M., Interactions between sentence context and word frequency in event-related brain potentials. Memory and Cognition 18 (1990), 380–393.
Van Petten, C., Luka, B.J., Prediction during language comprehension: Benefits, costs, and ERP components. International Journal of Psychophysiology 83:2 (2012), 176–190, 10.1016/j.ijpsycho.2011.09.015.
Vinckier, F., Dehaene, S., Jobert, A., Dubus, J.P., Sigman, M., Cohen, L., Hierarchical coding of letter strings in the ventral stream: Dissecting the inner organization of the visual word-form system. Neuron 55:1 (2007), 143–156, 10.1016/j.neuron.2007.05.031.
Vissers, C.T., Chwilla, D.J., Kolk, H.H., Monitoring in language perception: The effect of misspellings of words in highly constrained sentences. Brain Research 1106:1 (2006), 150–163.
Wang, L., Brothers, T., Jensen, O., Kuperberg, G.R., Dissociating the pre-activation of word meaning and form during sentence comprehension: Evidence from EEG representational similarity analysis. Psychonomic Bulletin & Review, 2023, 10.3758/s13423-023-02385-0.
Wang, L., Kuperberg, G., Jensen, O., Specific lexico-semantic predictions are associated with unique spatial and temporal patterns of neural activity. Elife, 7, 2018, e39061, 10.7554/eLife.39061.
Wang, L., Kuperberg, G.R., Better together: Integrating multivariate with univariate methods, and MEG with EEG to study language comprehension. Language, Cognition and Neuroscience., 2023, 10.1080/23273798.2023.2223783.
Wang, L., Nour Eddine, S., Brothers, T.A., Jensen, O., Kuperberg, G.R., Predictive Coding explains the dynamics of neural activity within the left ventromedial temporal lobe during reading comprehension. 2024.
Wang, L., Schoot, L., Brothers, T., Alexander, E., Warnke, L., Kim, M., Kuperberg, G.R., Predictive coding across the left fronto-temporal hierarchy during language comprehension. Cerebral Cortex 33:8 (2023), 4478–4497, 10.1093/cercor/bhac356.
Whittington, J.C.R., Bogacz, R., Theories of error back-propagation in the brain. Trends in Cognitive Sciences 23:3 (2019), 235–250, 10.1016/j.tics.2018.12.005.
Wlotko, E.W., Federmeier, K.D., So that's what you meant! Event-related potentials reveal multiple aspects of context use during construction of message-level meaning. Neuroimage 62:1 (2012), 356–366, 10.1016/j.neuroimage.2012.04.054.
Xiang, M., Kuperberg, G., Reversing expectations during discourse comprehension. Language, Cognition and Neuroscience 30:6 (2015), 648–672, 10.1080/23273798.2014.995679.
Zorzi, M., Houghton, G., Butterworth, B., Two routes or one in reading aloud? A connectionist dual-process model. Journal of Experimental Psychology. Human Perception and Performance 24:4 (1998), 1131–1161.
Zwaan, R.A., Radvansky, G.A., Situation models in language comprehension and memory. Psychological Bulletin 123:2 (1998), 162–185, 10.1037/0033-2909.123.2.162.
