1887
Volume 26, Issue 2-3
  • ISSN 0929-0907
  • E-ISSN: 1569-9943
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Abstract

Abstract

The general principles of perceptuo-motor processing and memory give rise to the constraint imposed on the organization of the language processing system. In particular, the Now-or-Never bottleneck demands an appropriate structure of linguistic input and rapid incorporation of both linguistic and multisensory contextual information in a progressive, integrative manner. I argue that the emerging predictive processing framework is well suited for the task of providing a comprehensive account of language processing under the Now-or-Never constraint. Moreover, this framework presents a stronger alternative to the account proposed by Christiansen and Chater (2016), as it better accommodates the available evidence concerning the role of context (in both the narrow and wider senses) in language comprehension at various levels of linguistic representation. Furthermore, the predictive processing approach allows for treating language as a special case of domain-general processing strategies, suggesting deep parallels with other cognitive processes such as vision.

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2021-02-12
2021-05-19
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References

  1. Adams, Rick A., Klaas Enno Stephan, Harriet R. Brown, Christopher D. Frith & Karl J. Friston
    2013 The computational anatomy of psychosis. Frontiers in Psychiatry4. 47. 10.3389/fpsyt.2013.00047
    https://doi.org/10.3389/fpsyt.2013.00047 [Google Scholar]
  2. Adelson, Beth
    1984 When novices surpass experts: The difficulty of a task may increase with expertise. Journal of Experimental Psychology: Learning, Memory, and Cognition10(3). 483–495.
    [Google Scholar]
  3. Allen, Roy, Peter Mcgeorge, David Pearson & Alan B. Milne
    2004 Attention and expertise in multiple target tracking. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition18(3). 337–347. 10.1002/acp.975
    https://doi.org/10.1002/acp.975 [Google Scholar]
  4. Barrett, Lisa Feldman & Moshe Bar
    2009 See it with feeling: Affective predictions during object perception. Philosophical Transactions of the Royal Society of London: Biological Sciences364(1521). 1325–1334. 10.1098/rstb.2008.0312
    https://doi.org/10.1098/rstb.2008.0312 [Google Scholar]
  5. Barton, Stephen B. & Anthony J. Sanford
    1993 A case study of anomaly detection: Shallow semantic processing and cohesion establishment. Memory & Cognition21(4). 477–487. 10.3758/BF03197179
    https://doi.org/10.3758/BF03197179 [Google Scholar]
  6. Bever, Thomas G.
    1970 The cognitive basis for linguistic structures. Cognition and the Development of Language279(362). 1–61.
    [Google Scholar]
  7. Brown, Harriet & Karl J. Friston
    2012 Free-energy and illusions: The cornsweet effect. Frontiers in Psychology3. 43. 10.3389/fpsyg.2012.00043
    https://doi.org/10.3389/fpsyg.2012.00043 [Google Scholar]
  8. Cantor, Alison D. & Elizabeth J. Marsh
    2017 Expertise effects in the Moses illusion: Detecting contradictions with stored knowledge. Memory25(2). 220–230. 10.1080/09658211.2016.1152377
    https://doi.org/10.1080/09658211.2016.1152377 [Google Scholar]
  9. Castel, Alan D., David P. McCabe, Henry L. Roediger III & Jeffrey Heitman
    2007 The dark side of expertise: Domain-specific memory errors. Psychological Science18(1). 3–5. 10.1111/j.1467‑9280.2007.01838.x
    https://doi.org/10.1111/j.1467-9280.2007.01838.x [Google Scholar]
  10. Che, Wanxiang & Yue Zhang
    2018 Deep learning in lexical analysis and parsing. Deep Learning in Natural Language, 79–116. Springer, Singapore.
    [Google Scholar]
  11. Chi, Michelene T., Paul J. Feltovich & Robert Glaser
    1981 Categorization and representation of physics problems by experts and novices. Cognitive Science5(2). 121–152. 10.1207/s15516709cog0502_2
    https://doi.org/10.1207/s15516709cog0502_2 [Google Scholar]
  12. Cho, Kyunghyun, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk & Yoshua Bengio
    2014 Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. (20 June, 2018.) 10.3115/v1/D14‑1179
    https://doi.org/10.3115/v1/D14-1179 [Google Scholar]
  13. Christiansen, Morten & Nick Chater
    2008 Language as shaped by the brain. Behavioral and Brain Sciences31(5). 489–509. 10.1017/S0140525X08004998
    https://doi.org/10.1017/S0140525X08004998 [Google Scholar]
  14. 2016a The Now-or-Never bottleneck: A fundamental constraint on language. Behavioral and Brain Sciences39. 1–19. 10.1017/S0140525X1500031X
    https://doi.org/10.1017/S0140525X1500031X [Google Scholar]
  15. 2016b Squeezing through the Now-or-Never bottleneck: Reconnecting language processing, acquisition, change, and structure. Behavioral and Brain Sciences39. 46–58.
    [Google Scholar]
  16. Churchland, Patricia S., Vilayanur S. Ramachandran & Terrence J. Sejnowski
    1994 A critique of pure visionInChristof Koch & Joel C. Davis (eds.), Large-scale Neuronal Theories of the Brain, 23–60. Cambridge: MIT Press.
    [Google Scholar]
  17. Clark, Andy
    2013 Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences36(3). 181–204. 10.1017/S0140525X12000477
    https://doi.org/10.1017/S0140525X12000477 [Google Scholar]
  18. 2015aEmbodied prediction. Open MIND. Frankfurt am Main: MIND Group.
    [Google Scholar]
  19. 2015b Radical predictive processing. The Southern Journal of Philosophy53(S1). 3–27. 10.1111/sjp.12120
    https://doi.org/10.1111/sjp.12120 [Google Scholar]
  20. 2015cSurfing uncertainty: Prediction, action, and the embodied mind. New York, NY: Oxford University Press.
    [Google Scholar]
  21. 2016 Attention alters predictive processing. Behavioral and Brain Sciences39. 10.1017/S0140525X15002472
    https://doi.org/10.1017/S0140525X15002472 [Google Scholar]
  22. Cohen, Michael A., Daniel C. Dennett & Nancy Kanwisher
    2016 What is the bandwidth of perceptual experience?Trends in Cognitive Sciences20(5). 324–335. 10.1016/j.tics.2016.03.006
    https://doi.org/10.1016/j.tics.2016.03.006 [Google Scholar]
  23. Colombo, Matteo & Stephan Hartmann
    2015 Bayesian cognitive science, unification, and explanation. The British Journal for the Philosophy of Science68(2). 451–484. 10.1093/bjps/axv036
    https://doi.org/10.1093/bjps/axv036 [Google Scholar]
  24. Cowan, Nelson
    2001 The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences24(1). 87–114. 10.1017/S0140525X01003922
    https://doi.org/10.1017/S0140525X01003922 [Google Scholar]
  25. 2010 The Magical Mystery Four: How is Working Memory Capacity Limited, and Why?Current Directions in Psychological Science19(1). 51–57. 10.1177/0963721409359277
    https://doi.org/10.1177/0963721409359277 [Google Scholar]
  26. DeLong, Katherine A., Thomas P. Urbach & Marta Kutas
    2005 Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience8(8). 1117. 10.1038/nn1504
    https://doi.org/10.1038/nn1504 [Google Scholar]
  27. 2017 Concerns with Nieuwland et al. multi-lab study (2017). Kutas Cognitive Electrophysiology Lab Working Paper. kutaslab.ucsd.edu/pdfs/FinalDUK17Comment9LabStudy.pdf. (17 May 2018.)
    [Google Scholar]
  28. Drenhaus, Heiner, Vera Demberg, Judith Köhne, J. & Francesca Delogu
    2014 Incremental and predictive discourse processing based on causal and concessive discourse markers: ERP studies on German and English. Annual Meeting of the Cognitive Science Society36(36).
    [Google Scholar]
  29. Dronkers, Nina F.
    2000 The pursuit of brain-language relationships. Brain & Language71(1). 59–61. 10.1006/brln.1999.2212
    https://doi.org/10.1006/brln.1999.2212 [Google Scholar]
  30. Elsabbagh, Mayada & Annette Karmiloff-Smith
    2006 Modularity of mind and language. The Encyclopaedia of Language and Linguistics, 218–224. 10.1016/B0‑08‑044854‑2/00859‑2
    https://doi.org/10.1016/B0-08-044854-2/00859-2 [Google Scholar]
  31. Ericsson, K. Anders, William G. Chase & Steve Faloon
    1980 Acquisition of a memory skill. Science208(4448). 1181–1182. 10.1126/science.7375930
    https://doi.org/10.1126/science.7375930 [Google Scholar]
  32. Erickson, Thomas D. & Mark E. Mattson
    1981 From words to meaning: A semantic illusion. Journal of Verbal Learning and Verbal Behavior20(5). 540–551. 10.1016/S0022‑5371(81)90165‑1
    https://doi.org/10.1016/S0022-5371(81)90165-1 [Google Scholar]
  33. Federmeier, Kara D. & Marta Kutas, M.
    1999 A rose by any other name: Long-term memory structure and sentence processing. Journal of Memory and Language41(4). 469–495. 10.1006/jmla.1999.2660
    https://doi.org/10.1006/jmla.1999.2660 [Google Scholar]
  34. Feldman, Harriet & Karl Friston
    2010 Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience4. 215. 10.3389/fnhum.2010.00215
    https://doi.org/10.3389/fnhum.2010.00215 [Google Scholar]
  35. Ferreira, Fernanda, Karl G. D. Bailey & Vittoria Ferraro
    2002 Good-enough representations in language comprehension. Current Directions in Psychological Science11(1). 11–15. 10.1111/1467‑8721.00158
    https://doi.org/10.1111/1467-8721.00158 [Google Scholar]
  36. Ferreira, Fernanda & Charles Clifton Jr
    1986 The independence of syntactic processing. Journal of Memory and Language25(3). 348–368. 10.1016/0749‑596X(86)90006‑9
    https://doi.org/10.1016/0749-596X(86)90006-9 [Google Scholar]
  37. Filik, Ruth & Anthony J. Sanford
    2008 When is cataphoric reference recognized?Cognition107(3). 1112–1121. 10.1016/j.cognition.2007.11.001
    https://doi.org/10.1016/j.cognition.2007.11.001 [Google Scholar]
  38. Fletcher, Paul C. & Chris. D. Frith
    2009 Perceiving is believing: A Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews Neuroscience10(1). 48–58. 10.1038/nrn2536
    https://doi.org/10.1038/nrn2536 [Google Scholar]
  39. Friston, Karl
    2002 Beyond phrenology: What can neuroimaging tell us about distributed circuitry?Annual Review of Neuroscience25(1). 221–250. 10.1146/annurev.neuro.25.112701.142846
    https://doi.org/10.1146/annurev.neuro.25.112701.142846 [Google Scholar]
  40. 2005 A theory of cortical responses. Philosophical Transactions of the Royal Society of London: Biological Sciences360(1456). 815–836. 10.1098/rstb.2005.1622
    https://doi.org/10.1098/rstb.2005.1622 [Google Scholar]
  41. Friston, Karl, Marco Lin, Chris D. Frith, Giovanni Pezzulo, J. Allan Hobson & Sasha Ondobaka
    2017 Active inference, curiosity and insight. Neural Computation29(10). 2633–2683. 10.1162/neco_a_00999
    https://doi.org/10.1162/neco_a_00999 [Google Scholar]
  42. Friston, Karl, Francesco Rigoli, Dmitri Ognibene, Christoph Mathys, Thomas Fitzgerald & Giovanni Pezzulo
    2015 Active inference and epistemic value. Cognitive Neuroscience6(4). 187–214. 10.1080/17588928.2015.1020053
    https://doi.org/10.1080/17588928.2015.1020053 [Google Scholar]
  43. Giard, Marie H. & F. Péronnet
    1999 Auditory-visual integration during multimodal object recognition in humans: A behavioral and electrophysiological study. Journal of Cognitive Neuroscience11(5). 473–490. 10.1162/089892999563544
    https://doi.org/10.1162/089892999563544 [Google Scholar]
  44. Glorot, Xavier, Antoine Bordes & Yoshua Bengio
    2011 Domain adaptation for large-scale sentiment classification: A deep learning approach. 28th International Conference on Machine Learning (ICML-11), 513–520.
    [Google Scholar]
  45. Goodglass, Harold
    1993Understanding aphasia. San Diego: Academic Press.
    [Google Scholar]
  46. Gregory, Richard L.
    1997 Knowledge in perception and illusion. Philosophical Transactions of the Royal Society: Biological Sciences352(1358). 1121–1127. 10.1098/rstb.1997.0095
    https://doi.org/10.1098/rstb.1997.0095 [Google Scholar]
  47. Hashimoto, Kazuma, Caiming Xiong, Yoshimasa Tsuruoka & Richard Socher
    2016 A joint many-task model: Growing a neural network for multiple NLP tasks. arXiv preprint arXiv:1611.01587. (15 June, 2018.)
    [Google Scholar]
  48. Heeger, David J.
    2017 Theory of cortical function. National Academy of Sciences (NAS)114(8). 1773–1782. 10.1073/pnas.1619788114
    https://doi.org/10.1073/pnas.1619788114 [Google Scholar]
  49. Heiser, Marc, Marco Iacoboni, Fumiko Maeda, Jake Marcus & John C. Mazziotta
    2003 The essential role of Broca’s area in imitation. European Journal of Neuroscience17(5). 1123–1128. 10.1046/j.1460‑9568.2003.02530.x
    https://doi.org/10.1046/j.1460-9568.2003.02530.x [Google Scholar]
  50. Helmholtz, Hermann
    1860Treatise on physiological optics (J. P. C. Southall, Trans. 1962 ed., Vol.3). New York: Dover.
    [Google Scholar]
  51. Hohwy, Jacob, Andreas Roepstorff & Karl Friston
    2008 Predictive coding explains binocular rivalry: An epistemological review. Cognition108(3). 687–701. 10.1016/j.cognition.2008.05.010
    https://doi.org/10.1016/j.cognition.2008.05.010 [Google Scholar]
  52. Horga, Guillermo, Kelly C. Schatz, Anissa Abi-Dargham & Bradley S. Peterson
    2014 Deficits in Predictive Coding Underlie Hallucinations in Schizophrenia. The Journal of Neuroscience34(24). 8072–8082. 10.1523/JNEUROSCI.0200‑14.2014
    https://doi.org/10.1523/JNEUROSCI.0200-14.2014 [Google Scholar]
  53. Johnson, Kathy E. & Carolyn B. Mervis
    1997 Effects of varying levels of expertise on the basic level of categorization. Journal of Experimental Psychology: General126(3). 248–277. 10.1037/0096‑3445.126.3.248
    https://doi.org/10.1037/0096-3445.126.3.248 [Google Scholar]
  54. Karimi, Hossein & Fernanda Ferreira
    2016 Good-enough linguistic representations and online cognitive equilibrium in language processing. The Quarterly Journal of Experimental Psychology69(5). 1013–1040. 10.1080/17470218.2015.1053951
    https://doi.org/10.1080/17470218.2015.1053951 [Google Scholar]
  55. Kempson, Ruth, Eleni Gregoromichelaki & Christine Howes
    2018 Language as an adaptive tool for interaction: A niche effect or a radical departure?Dynamic Syntax Workshop. 1. Edinburgh, UK.
    [Google Scholar]
  56. Kirby, Simon, Kenny Smith & Hannah Cornish
    2008 Language, Learning and Cultural Evolution: How linguistic transmission leads to cumulative adaptationInRobin Cooper & Ruth Kempson (eds.), Language in Flux: Dialogue Coordination, Language Variation, Change and Evolution. London: College Publications.
    [Google Scholar]
  57. Kiros, Ryan, Ruslan Salakhutdinov & Richard S. Zemel
    2014 Multimodal neural language models. Proceedings of the 31st International Conference on Machine Learning, PMLR32(2). 595–603.
    [Google Scholar]
  58. Köhne, Judith & Vera Demberg
    2013 The time-course of processing discourse connectives. Proceedings of the Annual Meeting of the Cognitive Science Society35. Retrieved fromhttps://escholarship.org/uc/item/3ng7w640. (20 June, 2018.)
    [Google Scholar]
  59. Kowsari, Kamran, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi, Matthew S. Gerber & Laura E. Barnes
    2017 Hdltex: Hierarchical deep learning for text classification. Machine Learning and Applications (ICMLA), 364–371.
    [Google Scholar]
  60. Kuperberg, Gina R. & T. Florian Jaeger
    2016 What do we mean by prediction in language comprehension?Language, Cognition and Neuroscience31(1). 32–59. 10.1080/23273798.2015.1102299
    https://doi.org/10.1080/23273798.2015.1102299 [Google Scholar]
  61. Lee, Tai Sing. & David Mumford
    2003 Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A, Optics, Image Science, and Vision20(7). 1434–1448. 10.1364/JOSAA.20.001434
    https://doi.org/10.1364/JOSAA.20.001434 [Google Scholar]
  62. Liu, Jingzhou, Wei-Cheng Chang, Yuexin Wu & Yiming Yang
    2017 Deep learning for extreme multi-label text classification. 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM), 115–124.
    [Google Scholar]
  63. Long, Mingsheng & Jianmin Wang
    2015 Learning multiple tasks with deep relationship networks. CoRR. 3.
    [Google Scholar]
  64. Lotem, Arnon, Oleg Kolodny, Joseph Y. Halpern, Luca Onnis & Shimon Edelman
    2016 The bottleneck may be the solution, not the problem. Behavioral and Brain Sciences39. 39–40. 10.1017/S0140525X15000886
    https://doi.org/10.1017/S0140525X15000886 [Google Scholar]
  65. MacDonald, John & Harry McGurk
    1978 Visual influences on speech perception processes. Perception & Psychophysics24(3). 253–257. 10.3758/BF03206096
    https://doi.org/10.3758/BF03206096 [Google Scholar]
  66. MacDonald, Maryellen C., Neal J. Pearlmutter & Mark S. Seidenberg
    1994 The lexical nature of syntactic ambiguity resolution. Psychological Review101(4). 676–703. 10.1037/0033‑295X.101.4.676
    https://doi.org/10.1037/0033-295X.101.4.676 [Google Scholar]
  67. Marr, David
    1976 Early processing of visual information. Philosophical Transactions of the Royal Society of London: Biological Sciences275(942). 483–519.
    [Google Scholar]
  68. McDonald, Scott A. & Richard C. Shillcock
    2003 Eye movements reveal the on-line computation of lexical probabilities during reading. Psychological Science14(6). 648–652. 10.1046/j.0956‑7976.2003.psci_1480.x
    https://doi.org/10.1046/j.0956-7976.2003.psci_1480.x [Google Scholar]
  69. McGurk, Harry & John MacDonald
    1976 Hearing lips and seeing voices. Nature264(5588). 746–748. 10.1038/264746a0
    https://doi.org/10.1038/264746a0 [Google Scholar]
  70. Misra, Ishan, Abhinav Shrivastava, Abhinav Gupta & Martial Hebert
    2016 Cross-stitch networks for multi-task learning. IEEE Conference on Computer Vision and Pattern Recognition, 3994–4003.
    [Google Scholar]
  71. Molholm, Sophie, Walter Ritter, Micah M. Murray, Daniel C. Javitt, Charles E. Schroeder & John J. Foxe
    2002 Multisensory auditory – visual interactions during early sensory processing in humans: A high-density electrical mapping study. Cognitive Brain Research14(1). 115–128. 10.1016/S0926‑6410(02)00066‑6
    https://doi.org/10.1016/S0926-6410(02)00066-6 [Google Scholar]
  72. Ngiam, Jiquan, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Y. Ng
    2011 Multimodal deep learning. 28th international conference on machine learning (ICML-11), 689–696.
    [Google Scholar]
  73. Nieuwland, Mante S., Stephen Politzer-Ahles, Evelien Heyselaar, Katrien Segaert, Emily Darley, Nina Kazanina, Sarah von Grebmer zu Wolfsthurn
    2017 Limits on prediction in language comprehension: A multi-lab failure to replicate evidence for probabilistic pre-activation of phonology. BioRxiv. (05 July 2018.)
    [Google Scholar]
  74. Orlandi, Nico & Lee Geoff
    2019 How Radical is Predictive Processing?InMatteo Colombo, Elizabeth Irvine & Mog Stapleton (eds.), Andy Clark and his Critics. 206–219. New York, NY: Oxford University Press. 10.1093/oso/9780190662813.003.0016
    https://doi.org/10.1093/oso/9780190662813.003.0016 [Google Scholar]
  75. Papathomas, Thomas V.
    2017 The Hollow-Mask Illusion and Variations. InArthur G. Shapiro & Dejan Todorović (eds.), The Oxford Compendium of Visual Illusions. 614–619. New York, NY: Oxford University Press.
    [Google Scholar]
  76. Pashler, Harold
    1988 Familiarity and visual change detection. Perception & Psychophysics44(4). 369–378. 10.3758/BF03210419
    https://doi.org/10.3758/BF03210419 [Google Scholar]
  77. Pellicano, Elizabeth & David Burr
    2012 When the world becomes ‘too real’: A Bayesian explanation of autistic perception. Trends in Cognitive Sciences16(10). 504–510. 10.1016/j.tics.2012.08.009
    https://doi.org/10.1016/j.tics.2012.08.009 [Google Scholar]
  78. Pezzulo, Giovanni
    2014 Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference. Cognitive, Affective, & Behavioral Neuroscience14(3). 902–911. 10.3758/s13415‑013‑0227‑x
    https://doi.org/10.3758/s13415-013-0227-x [Google Scholar]
  79. Rao, Rajesh P. & Dana H. Ballard
    1999 Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience2(1). 79. 10.1038/4580
    https://doi.org/10.1038/4580 [Google Scholar]
  80. Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele & Honglak Lee
    2016 Generative adversarial text to image synthesis. arXiv preprint arXiv:1605.05396.
    [Google Scholar]
  81. Remez, Robert E., Daria F. Ferro, Kathryn R. Dubowski, Judith Meer, Robin S. Broder & Morgana L. Davids
    2010 Is desynchrony tolerance adaptable in the perceptual organization of speech?Attention, Perception, & Psychophysics72(8). 2054–2058. 10.3758/BF03196682
    https://doi.org/10.3758/BF03196682 [Google Scholar]
  82. Rohde, Hannah & William S. Horton
    2014 Anticipatory looks reveal expectations about discourse relations. Cognition133(3). 667–691. 10.1016/j.cognition.2014.08.012
    https://doi.org/10.1016/j.cognition.2014.08.012 [Google Scholar]
  83. Roy, Deb & Niloy Mukherjee
    2005 Towards situated speech understanding: Visual context priming of language models. Computer Speech & Language19(2). 227–248. 10.1016/j.csl.2004.08.003
    https://doi.org/10.1016/j.csl.2004.08.003 [Google Scholar]
  84. Shallice, Tim
    1988From neuropsychology to mental structure. New York, NY: Cambridge University Press. 10.1017/CBO9780511526817
    https://doi.org/10.1017/CBO9780511526817 [Google Scholar]
  85. Simons, Daniel J. & Christopher F. Chabris
    1999 Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception28(9). 1059–1074. 10.1068/p281059
    https://doi.org/10.1068/p281059 [Google Scholar]
  86. Slattery, Timothy J., Patrick Sturt, Kiel Christianson, Masaya Yoshida & Fernanda Ferreira
    2013 Lingering misinterpretations of garden path sentences arise from competing syntactic representations. Journal of Memory and Language69(2). 104–120. 10.1016/j.jml.2013.04.001
    https://doi.org/10.1016/j.jml.2013.04.001 [Google Scholar]
  87. Sperber, Dan
    2002 In defence of massive modularity. InEmmanuel Dupoux (ed.), Language, Brain and Cognitive Development: Essays in Honor of Jacques Mehler, 47–57. Cambridge, Mass.: MIT Press.
    [Google Scholar]
  88. Spratling, Michael W.
    2008 Reconciling predictive coding and biased competition models of cortical function. Frontiers in Computational Neuroscience2. 4.
    [Google Scholar]
  89. 2017 A review of predictive coding algorithms. Brain and Cognition112. 92–97. 10.1016/j.bandc.2015.11.003
    https://doi.org/10.1016/j.bandc.2015.11.003 [Google Scholar]
  90. Stephan, Klaas E., Zina M. Manjaly, Christoph D. Mathys, Lilian A. Weber, Saee Paliwal, Tim Gard, Marc Tittgemeyer
    2016 Allostatic self-efficacy: A metacognitive theory of dyshomeostasis-induced fatigue and depression. Frontiers in Human Neuroscience10. 550. 10.3389/fnhum.2016.00550
    https://doi.org/10.3389/fnhum.2016.00550 [Google Scholar]
  91. Sutskever, Ilya, Oriol Vinyals & Quoc V. Le
    2014 Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems27. 3104–3112.
    [Google Scholar]
  92. Talsma, Durk
    2015 Predictive coding and multisensory integration: An attentional account of the multisensory mind. Frontiers in Integrative Neuroscience9. 19. 10.3389/fnint.2015.00019
    https://doi.org/10.3389/fnint.2015.00019 [Google Scholar]
  93. Taylor, John R.
    2012The mental corpus: How language is represented in the mind. Oxford University Press. 10.1093/acprof:oso/9780199290802.001.0001
    https://doi.org/10.1093/acprof:oso/9780199290802.001.0001 [Google Scholar]
  94. Traxler, Matthew J.
    2012Introduction to psycholinguistics understanding language science. Chichester, UK, Malden, Mass.: Wiley-Blackwell.
    [Google Scholar]
  95. Van Oostendorp, Herre & Sjaak De Mul, S.
    1990 Moses beats Adam: A semantic relatedness effect on a semantic illusion. Acta Psychologica74(1). 35–46. 10.1016/0001‑6918(90)90033‑C
    https://doi.org/10.1016/0001-6918(90)90033-C [Google Scholar]
  96. Vervaeke, John, Timothy P. Lillicrap & Blake A. Richards
    2012 Relevance realization and the emerging framework in cognitive science. Journal of Logic and Computation22(1). 79–99. 10.1093/logcom/exp067
    https://doi.org/10.1093/logcom/exp067 [Google Scholar]
  97. Wiese, Wanja
    2018Experienced Wholeness: Integrating Insights from Gestalt Theory, Cognitive Neuroscience, and Predictive Processing. Cambridge, Mass.: MIT Press. 10.7551/mitpress/9780262036993.001.0001
    https://doi.org/10.7551/mitpress/9780262036993.001.0001 [Google Scholar]
  98. Williams, Daniel
    2018 Predictive coding and thought. Synthese197. 1–27.
    [Google Scholar]
  99. Wu, Yonghui, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun
    2016 Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
    [Google Scholar]
  100. Yu, Mo & Mark Dredze
    2014 Improving lexical embeddings with semantic knowledge. 52nd Annual Meeting of the Association for Computational Linguistics2(2). 545–550.
    [Google Scholar]
  101. Zarcone, Alessandra, Marten Van Schijndel, Jorrig Vogels, & Vera Demberg
    2016 Salience and attention in surprisal-based accounts of language processing. Frontiers in Psychology7. 844. 10.3389/fpsyg.2016.00844
    https://doi.org/10.3389/fpsyg.2016.00844 [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1075/pc.19001.rap
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  • Article Type: Research Article
Keyword(s): context; language processing; predictive processing; processing bottleneck; vision
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