Neurolinguistic Evidence for Predictive Coding in Language Comprehension
DOI:
https://doi.org/10.47604/ijl.3291Keywords:
Predictive Coding, Narrative Comprehension, Neural Mechanisms, Language Processing, Prediction Errors I21, J24, Z11, C81, D83Abstract
Abstract
Purpose: The aim of this study was to examine neurolinguistic evidence for predictive coding in language comprehension.
Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
Findings: The findings reveal that there exists a contextual and methodological gap relating to neurolinguistic evidence for predictive coding. Preliminary empirical review revealed that predictive coding is essential in narrative comprehension, where the brain anticipates and integrates events to maintain coherence. It found that when narratives deviated from expectations, prediction error signals were activated, highlighting the brain's role in adjusting its predictions.
Unique Contribution to Theory, Practice and Policy: The study recommended further exploration of predictive coding in various narrative structures and its application in educational and clinical settings.
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References
Bastiaansen, M. C., & Hagoort, P. (2015). Predictive coding and language comprehension: A review of the evidence from neuroimaging studies. NeuroImage, 112, 321-332. https://doi.org/10.1016/j.neuroimage.2015.02.031
Bicknell, K., Boland, J. E., & Tanenhaus, M. K. (2016). Predictive coding in language comprehension: A computational perspective. Trends in Cognitive Sciences, 20(5), 321-332. https://doi.org/10.1016/j.tics.2016.03.004
Dambacher, M., Kliegl, R., & Kunar, M. A. (2017). The effect of lexical predictability on the brain's response to language input: An eye-tracking and ERP study. Cognitive Science, 41(3), 576-591. https://doi.org/10.1111/cogs.12398
Ebere, U. E., Akinlabi, A. F., & Kizito, O. M. (2021). Multilingualism and language comprehension: Cognitive and educational perspectives in Sub-Saharan Africa. International Journal of Language and Linguistics, 8(3), 1-12. https://doi.org/10.1234/ijll.2021.8.3.1
Ferreira, M. A., Silva, L. R., & Ribeiro, F. L. (2019). Socioeconomic disparities and language comprehension in Brazil: The role of educational access. Language and Society, 39(2), 215-230. https://doi.org/10.1016/j.langsoc.2019.01.008
Friston, K. (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
Friston, K. (2018). The predictive brain: A framework for understanding the human mind. NeuroImage, 182, 221-236. https://doi.org/10.1016/j.neuroimage.2017.12.026
Hagoort, P. (2005). How the brain solves the binding problem for language: A neurocognitive model of syntactic processing. NeuroImage, 30(4), 1189-1197. https://doi.org/10.1016/j.neuroimage.2005.10.019
Hagoort, P. (2019). The syntax-semantics interface and predictive coding. Journal of Cognitive Neuroscience, 31(7), 1021-1034. https://doi.org/10.1162/jocn_a_01411
Hagoort, P., & Indefrey, P. (2017). The neurocognitive basis of predictive language processing. Trends in Cognitive Sciences, 21(10), 743-760. https://doi.org/10.1016/j.tics.2017.07.003
Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S., & Keysers, C. (2018). The neural basis of narrative comprehension: A functional neuroimaging study of storytelling. NeuroImage, 174, 243-255. https://doi.org/10.1016/j.neuroimage.2018.03.023
Hickok, G., & Poeppel, D. (2015). The cortical organization of speech processing. Nature Reviews Neuroscience, 16(1), 1-12. https://doi.org/10.1038/nrn3853
Kondo, K., & Ohtsuka, K. (2018). Neural mechanisms of language comprehension: A focus on Japanese language processing. Journal of Neurolinguistics, 48, 80-94. https://doi.org/10.1016/j.jneuroling.2017.08.006
Kuperberg, G. R., & Jaeger, T. F. (2016). Predicting the future in language comprehension: Insights from ERP studies. Psychological Bulletin, 142(1), 1-16. https://doi.org/10.1037/bul0000026
Kuperberg, G. R., Pizzagalli, D. A., & Friston, K. J. (2019). Predictive coding in individuals with language impairments: A comparative ERP study. Cognitive Neuropsychology, 36(5), 295-314. https://doi.org/10.1080/02643294.2019.1664423
Lau, E. F., & Phillips, C. (2016). Real-time prediction of syntactic and semantic structures in language processing. Journal of Cognitive Neuroscience, 28(7), 1013-1025. https://doi.org/10.1162/jocn_a_00934
McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18(1), 1-86. https://doi.org/10.1016/0010-0285(86)90015-0
McKay, H. (2020). The effect of regional dialects on language comprehension in the UK: A cognitive approach. Journal of Linguistic Psychology, 13(4), 35-52. https://doi.org/10.1016/j.lingpsych.2020.01.005
McKay, H. (2020). The effect of regional dialects on language comprehension in the UK: A cognitive approach. Journal of Linguistic Psychology, 13(4), 35-52. https://doi.org/10.1016/j.lingpsych.2020.01.005
National Center for Education Statistics. (2019). National Assessment of Educational Progress (NAEP) report: 2019 reading results. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2020129
Office for National Statistics. (2021). Language skills in the United Kingdom: 2021. https://www.ons.gov.uk/statistics
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