Cover not available

Article published In: Pragmatics & Cognition
Vol. 26:2/3 (2019) ► pp.357–385

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

Now, never, or coming soon?

Prediction and efficient language processing

ORCiD logo with link | Ludwig-Maximilians-Universität Munich, Germany
Published online: 12 February 2021
DOI logo with link
https://doi.org/10.1075/pc.19001.rap
Abstract
The general principles of perceptuo-motor processing and memory give rise to the Now-or-Never bottleneck 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 Chunk-and-Pass 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.
Article outline
  • 1.Introduction
  • 2.The Now-or-Never bottleneck
    • 2.1Consequences 1 & 3: Multilevel organization of language and incrementality of language processing
    • 2.2Consequences 2 & 4: Prediction, locality, and language modularity
    • 2.3Not all mistakes are corrected
    • 2.4Requirements for an account of language processing: A quick summary
  • 3.Predictive processing framework
    • 3.1What is predictive processing?
    • 3.2Applying predictive processing to language: Solutions to the Now-or-Never bottleneck and some implications
  • 4.Worries and future directions
  • 5.Conclusion
  • Acknowledgements
  • Notes
  • References
References (101)
References
Adams, Rick A., Klaas Enno Stephan, Harriet R. Brown, Christopher D. Frith & Karl J. Friston. 2013. The computational anatomy of psychosis. Frontiers in Psychiatry 41. 47. Google Scholar logo with link to Google Scholar
Adelson, Beth. 1984. When novices surpass experts: The difficulty of a task may increase with expertise. Journal of Experimental Psychology: Learning, Memory, and Cognition 10(3). 483–495.Google Scholar logo with link to Google Scholar
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 Cognition 18(3). 337–347. Google Scholar logo with link to Google Scholar
Barrett, Lisa Feldman & Moshe Bar. 2009. See it with feeling: Affective predictions during object perception. Philosophical Transactions of the Royal Society of London: Biological Sciences 364(1521). 1325–1334. Google Scholar logo with link to Google Scholar
Barton, Stephen B. & Anthony J. Sanford. 1993. A case study of anomaly detection: Shallow semantic processing and cohesion establishment. Memory & Cognition 21(4). 477–487. Google Scholar logo with link to Google Scholar
Bever, Thomas G. 1970. The cognitive basis for linguistic structures. Cognition and the Development of Language 279(362). 1–61.Google Scholar logo with link to Google Scholar
Brown, Harriet & Karl J. Friston. 2012. Free-energy and illusions: The cornsweet effect. Frontiers in Psychology 31. 43. Google Scholar logo with link to Google Scholar
Cantor, Alison D. & Elizabeth J. Marsh. 2017. Expertise effects in the Moses illusion: Detecting contradictions with stored knowledge. Memory 25(2). 220–230. Google Scholar logo with link to Google Scholar
Castel, Alan D., David P. McCabe, Henry L. Roediger III & Jeffrey Heitman. 2007. The dark side of expertise: Domain-specific memory errors. Psychological Science 18(1). 3–5. Google Scholar logo with link to Google Scholar
Che, Wanxiang & Yue Zhang. 2018. Deep learning in lexical analysis and parsing. Deep Learning in Natural Language, 79–116. Springer, Singapore.Google Scholar logo with link to Google Scholar
Chi, Michelene T., Paul J. Feltovich & Robert Glaser. 1981. Categorization and representation of physics problems by experts and novices. Cognitive Science 5(2). 121–152. Google Scholar logo with link to Google Scholar
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.) Google Scholar logo with link to Google Scholar
Christiansen, Morten & Nick Chater. 2008. Language as shaped by the brain. Behavioral and Brain Sciences 31(5). 489–509. Google Scholar logo with link to Google Scholar
. 2016a. The Now-or-Never bottleneck: A fundamental constraint on language. Behavioral and Brain Sciences 391. 1–19. Google Scholar logo with link to Google Scholar
. 2016b. Squeezing through the Now-or-Never bottleneck: Reconnecting language processing, acquisition, change, and structure. Behavioral and Brain Sciences 391. 46–58.Google Scholar logo with link to Google Scholar
Churchland, Patricia S., Vilayanur S. Ramachandran & Terrence J. Sejnowski. 1994. A critique of pure vision In Christof Koch & Joel C. Davis (eds.), Large-scale Neuronal Theories of the Brain, 23–60. Cambridge: MIT Press.Google Scholar logo with link to Google Scholar
Clark, Andy. 2013. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences 36(3). 181–204. Google Scholar logo with link to Google Scholar
. 2015a. Embodied prediction. Open MIND. Frankfurt am Main: MIND Group.Google Scholar logo with link to Google Scholar
. 2015b. Radical predictive processing. The Southern Journal of Philosophy 53(S1). 3–27. Google Scholar logo with link to Google Scholar
. 2015c. Surfing uncertainty: Prediction, action, and the embodied mind. New York, NY: Oxford University Press.Google Scholar logo with link to Google Scholar
. 2016. Attention alters predictive processing. Behavioral and Brain Sciences 391. Google Scholar logo with link to Google Scholar
Cohen, Michael A., Daniel C. Dennett & Nancy Kanwisher. 2016. What is the bandwidth of perceptual experience? Trends in Cognitive Sciences 20(5). 324–335. Google Scholar logo with link to Google Scholar
Colombo, Matteo & Stephan Hartmann. 2015. Bayesian cognitive science, unification, and explanation. The British Journal for the Philosophy of Science 68(2). 451–484. Google Scholar logo with link to Google Scholar
Cowan, Nelson. 2001. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences 24(1). 87–114. Google Scholar logo with link to Google Scholar
. 2010. The Magical Mystery Four: How is Working Memory Capacity Limited, and Why? Current Directions in Psychological Science 19(1). 51–57. Google Scholar logo with link to Google Scholar
DeLong, Katherine A., Thomas P. Urbach & Marta Kutas. 2005. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience 8(8). 1117. Google Scholar logo with link to Google Scholar
. 2017. Concerns with Nieuwland et al. multi-lab study (2017). Kutas Cognitive Electrophysiology Lab Working Paper. [URL]. (17 May 2018.)
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 Society 36(36).Google Scholar logo with link to Google Scholar
Dronkers, Nina F. 2000. The pursuit of brain-language relationships. Brain & Language 71(1). 59–61. Google Scholar logo with link to Google Scholar
Elsabbagh, Mayada & Annette Karmiloff-Smith. 2006. Modularity of mind and language. The Encyclopaedia of Language and Linguistics, 218–224. Google Scholar logo with link to Google Scholar
Ericsson, K. Anders, William G. Chase & Steve Faloon. 1980. Acquisition of a memory skill. Science 208(4448). 1181–1182. Google Scholar logo with link to Google Scholar
Erickson, Thomas D. & Mark E. Mattson. 1981. From words to meaning: A semantic illusion. Journal of Verbal Learning and Verbal Behavior 20(5). 540–551. Google Scholar logo with link to Google Scholar
Federmeier, Kara D. & Marta 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. Google Scholar logo with link to Google Scholar
Feldman, Harriet & Karl Friston. 2010. Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience 41. 215. Google Scholar logo with link to Google Scholar
Ferreira, Fernanda, Karl G. D. Bailey & Vittoria Ferraro. 2002. Good-enough representations in language comprehension. Current Directions in Psychological Science 11(1). 11–15. Google Scholar logo with link to Google Scholar
Ferreira, Fernanda & Charles Clifton Jr. 1986. The independence of syntactic processing. Journal of Memory and Language 25(3). 348–368. Google Scholar logo with link to Google Scholar
Filik, Ruth & Anthony J. Sanford. 2008. When is cataphoric reference recognized? Cognition 107(3). 1112–1121. Google Scholar logo with link to Google Scholar
Fletcher, Paul C. & Chris. D. Frith. 2009. Perceiving is believing: A Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews Neuroscience 10(1). 48–58. Google Scholar logo with link to Google Scholar
Friston, Karl. 2002. Beyond phrenology: What can neuroimaging tell us about distributed circuitry? Annual Review of Neuroscience 25(1). 221–250. Google Scholar logo with link to Google Scholar
. 2005. A theory of cortical responses. Philosophical Transactions of the Royal Society of London: Biological Sciences 360(1456). 815–836. Google Scholar logo with link to Google Scholar
Friston, Karl, Marco Lin, Chris D. Frith, Giovanni Pezzulo, J. Allan Hobson & Sasha Ondobaka 2017. Active inference, curiosity and insight. Neural Computation 29(10). 2633–2683. Google Scholar logo with link to Google Scholar
Friston, Karl, Francesco Rigoli, Dmitri Ognibene, Christoph Mathys, Thomas Fitzgerald & Giovanni Pezzulo. 2015. Active inference and epistemic value. Cognitive Neuroscience 6(4). 187–214. Google Scholar logo with link to Google Scholar
Giard, Marie H. & F. Péronnet 1999. Auditory-visual integration during multimodal object recognition in humans: A behavioral and electrophysiological study. Journal of Cognitive Neuroscience 11(5). 473–490. Google Scholar logo with link to Google Scholar
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 logo with link to Google Scholar
Goodglass, Harold. 1993. Understanding aphasia. San Diego: Academic Press.Google Scholar logo with link to Google Scholar
Gregory, Richard L. 1997. Knowledge in perception and illusion. Philosophical Transactions of the Royal Society: Biological Sciences 352(1358). 1121–1127. Google Scholar logo with link to Google Scholar
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 logo with link to Google Scholar
Heeger, David J. 2017. Theory of cortical function. National Academy of Sciences (NAS) 114(8). 1773–1782. Google Scholar logo with link to Google Scholar
Heiser, Marc, Marco Iacoboni, Fumiko Maeda, Jake Marcus & John C. Mazziotta. 2003. The essential role of Broca’s area in imitation. European Journal of Neuroscience 17(5). 1123–1128. Google Scholar logo with link to Google Scholar
Helmholtz, Hermann. 1860. Treatise on physiological optics (J. P. C. Southall, Trans. 1962 ed., Vol. 31). New York: Dover.Google Scholar logo with link to Google Scholar
Hohwy, Jacob, Andreas Roepstorff & Karl Friston. 2008. Predictive coding explains binocular rivalry: An epistemological review. Cognition 108(3). 687–701. Google Scholar logo with link to Google Scholar
Horga, Guillermo, Kelly C. Schatz, Anissa Abi-Dargham & Bradley S. Peterson. 2014. Deficits in Predictive Coding Underlie Hallucinations in Schizophrenia. The Journal of Neuroscience 34(24). 8072–8082. Google Scholar logo with link to Google Scholar
Johnson, Kathy E. & Carolyn B. Mervis. 1997. Effects of varying levels of expertise on the basic level of categorization. Journal of Experimental Psychology: General 126(3). 248–277. Google Scholar logo with link to Google Scholar
Karimi, Hossein & Fernanda Ferreira. 2016. Good-enough linguistic representations and online cognitive equilibrium in language processing. The Quarterly Journal of Experimental Psychology 69(5). 1013–1040. Google Scholar logo with link to Google Scholar
Kempson, Ruth, Eleni Gregoromichelaki & Christine Howes. 2018. Language as an adaptive tool for interaction: A niche effect or a radical departure? Dynamic Syntax Workshop. 11. Edinburgh, UK.Google Scholar logo with link to Google Scholar
Kirby, Simon, Kenny Smith & Hannah Cornish. 2008. Language, Learning and Cultural Evolution: How linguistic transmission leads to cumulative adaptation In Robin Cooper & Ruth Kempson (eds.), Language in Flux: Dialogue Coordination, Language Variation, Change and Evolution. London: College Publications.Google Scholar logo with link to Google Scholar
Kiros, Ryan, Ruslan Salakhutdinov & Richard S. Zemel. 2014. Multimodal neural language models. Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2). 595–603.Google Scholar logo with link to Google Scholar
Köhne, Judith & Vera Demberg. 2013. The time-course of processing discourse connectives. Proceedings of the Annual Meeting of the Cognitive Science Society 351. Retrieved from [URL]. (20 June, 2018.)
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 logo with link to Google Scholar
Kuperberg, Gina R. & T. Florian Jaeger. 2016. What do we mean by prediction in language comprehension? Language, Cognition and Neuroscience 31(1). 32–59. Google Scholar logo with link to Google Scholar
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 Vision 20(7). 1434–1448. Google Scholar logo with link to Google Scholar
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 logo with link to Google Scholar
Long, Mingsheng & Jianmin Wang. 2015. Learning multiple tasks with deep relationship networks. CoRR. 31.Google Scholar logo with link to Google Scholar
Lotem, Arnon, Oleg Kolodny, Joseph Y. Halpern, Luca Onnis & Shimon Edelman. 2016. The bottleneck may be the solution, not the problem. Behavioral and Brain Sciences 391. 39–40. Google Scholar logo with link to Google Scholar
MacDonald, John & Harry McGurk. 1978. Visual influences on speech perception processes. Perception & Psychophysics 24(3). 253–257. Google Scholar logo with link to Google Scholar
MacDonald, Maryellen C., Neal J. Pearlmutter & Mark S. Seidenberg. 1994. The lexical nature of syntactic ambiguity resolution. Psychological Review 101(4). 676–703. Google Scholar logo with link to Google Scholar
Marr, David. 1976. Early processing of visual information. Philosophical Transactions of the Royal Society of London: Biological Sciences 275(942). 483–519.Google Scholar logo with link to Google Scholar
McDonald, Scott A. & Richard C. Shillcock. 2003. Eye movements reveal the on-line computation of lexical probabilities during reading. Psychological Science 14(6). 648–652. Google Scholar logo with link to Google Scholar
McGurk, Harry & John MacDonald. 1976. Hearing lips and seeing voices. Nature 264(5588). 746–748. Google Scholar logo with link to Google Scholar
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 logo with link to Google Scholar
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 Research 14(1). 115–128. Google Scholar logo with link to Google Scholar
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 logo with link to Google Scholar
Nieuwland, Mante S., Stephen Politzer-Ahles, Evelien Heyselaar, Katrien Segaert, Emily Darley, Nina Kazanina, Sarah von Grebmer zu Wolfsthurn et al. 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 logo with link to Google Scholar
Orlandi, Nico & Lee Geoff. 2019. How Radical is Predictive Processing? In Matteo Colombo, Elizabeth Irvine & Mog Stapleton (eds.), Andy Clark and his Critics. 206–219. New York, NY: Oxford University Press. Google Scholar logo with link to Google Scholar
Papathomas, Thomas V. 2017. The Hollow-Mask Illusion and Variations. In Arthur G. Shapiro & Dejan Todorović (eds.), The Oxford Compendium of Visual Illusions. 614–619. New York, NY: Oxford University Press.Google Scholar logo with link to Google Scholar
Pashler, Harold. 1988. Familiarity and visual change detection. Perception & Psychophysics 44(4). 369–378. Google Scholar logo with link to Google Scholar
Pellicano, Elizabeth & David Burr. 2012. When the world becomes ‘too real’: A Bayesian explanation of autistic perception. Trends in Cognitive Sciences 16(10). 504–510. Google Scholar logo with link to Google Scholar
Pezzulo, Giovanni. 2014. Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference. Cognitive, Affective, & Behavioral Neuroscience 14(3). 902–911. Google Scholar logo with link to Google Scholar
Rao, Rajesh P. & Dana H. Ballard. 1999. Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2(1). 79. Google Scholar logo with link to Google Scholar
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 logo with link to Google Scholar
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, & Psychophysics 72(8). 2054–2058. Google Scholar logo with link to Google Scholar
Rohde, Hannah & William S. Horton. 2014. Anticipatory looks reveal expectations about discourse relations. Cognition 133(3). 667–691. Google Scholar logo with link to Google Scholar
Roy, Deb & Niloy Mukherjee. 2005. Towards situated speech understanding: Visual context priming of language models. Computer Speech & Language 19(2). 227–248. Google Scholar logo with link to Google Scholar
Shallice, Tim. 1988. From neuropsychology to mental structure. New York, NY: Cambridge University Press. Google Scholar logo with link to Google Scholar
Simons, Daniel J. & Christopher F. Chabris. 1999. Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception 28(9). 1059–1074. Google Scholar logo with link to Google Scholar
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 Language 69(2). 104–120. Google Scholar logo with link to Google Scholar
Sperber, Dan. 2002. In defence of massive modularity. In Emmanuel Dupoux (ed.), Language, Brain and Cognitive Development: Essays in Honor of Jacques Mehler, 47–57. Cambridge, Mass.: MIT Press.Google Scholar logo with link to Google Scholar
Spratling, Michael W. 2008. Reconciling predictive coding and biased competition models of cortical function. Frontiers in Computational Neuroscience 2. 4.Google Scholar logo with link to Google Scholar
2017. A review of predictive coding algorithms. Brain and Cognition 1121. 92–97. Google Scholar logo with link to Google Scholar
Stephan, Klaas E., Zina M. Manjaly, Christoph D. Mathys, Lilian A. Weber, Saee Paliwal, Tim Gard, Marc Tittgemeyer et al. 2016. Allostatic self-efficacy: A metacognitive theory of dyshomeostasis-induced fatigue and depression. Frontiers in Human Neuroscience 101. 550. Google Scholar logo with link to Google Scholar
Sutskever, Ilya, Oriol Vinyals & Quoc V. Le. 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 271. 3104–3112.Google Scholar logo with link to Google Scholar
Talsma, Durk. 2015. Predictive coding and multisensory integration: An attentional account of the multisensory mind. Frontiers in Integrative Neuroscience 91. 19. Google Scholar logo with link to Google Scholar
Taylor, John R. 2012. The mental corpus: How language is represented in the mind. Oxford University Press. Google Scholar logo with link to Google Scholar
Traxler, Matthew J. 2012. Introduction to psycholinguistics understanding language science. Chichester, UK, Malden, Mass.: Wiley-Blackwell.Google Scholar logo with link to Google Scholar
Van Oostendorp, Herre & Sjaak De Mul, S. 1990. Moses beats Adam: A semantic relatedness effect on a semantic illusion. Acta Psychologica 74(1). 35–46. Google Scholar logo with link to Google Scholar
Vervaeke, John, Timothy P. Lillicrap & Blake A. Richards 2012. Relevance realization and the emerging framework in cognitive science. Journal of Logic and Computation 22(1). 79–99. Google Scholar logo with link to Google Scholar
Wiese, Wanja. 2018. Experienced Wholeness: Integrating Insights from Gestalt Theory, Cognitive Neuroscience, and Predictive Processing. Cambridge, Mass.: MIT Press. Google Scholar logo with link to Google Scholar
Williams, Daniel. 2018. Predictive coding and thought. Synthese 1971. 1–27.Google Scholar logo with link to Google Scholar
Wu, Yonghui, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.Google Scholar logo with link to Google Scholar
Yu, Mo & Mark Dredze. 2014. Improving lexical embeddings with semantic knowledge. 52nd Annual Meeting of the Association for Computational Linguistics 2(2). 545–550.Google Scholar logo with link to Google Scholar
Zarcone, Alessandra, Marten Van Schijndel, Jorrig Vogels, & Vera Demberg. 2016. Salience and attention in surprisal-based accounts of language processing. Frontiers in Psychology 71. 844. Google Scholar logo with link to Google Scholar
Cited by (4)

Cited by four other publications

Vicente, Agustín, Christian Michel & Valentina Petrolini
2024. Literalism in Autistic People: a Predictive Processing Proposal. Review of Philosophy and Psychology 15:4 ► pp. 1133 ff. DOI logo
Mackenzie, J. Lachlan
2023. The Grammar of Incremental Language Production in Context. In The Cambridge Handbook of Language in Context, ► pp. 140 ff. DOI logo
Löhr, Guido & Christian Michel
2022. Copredication in Context: A Predictive Processing Approach. Cognitive Science 46:5 DOI logo
Rappe, Sofiia
2022. Predictive minds can think: addressing generality and surface compositionality of thought. Synthese 200:1 DOI logo

This list is based on CrossRef data as of 29 november 2025. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.

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