Neurolinguistic Communication Intervention

Review

Retiring Linguistics for a Unified Language Science

Downloads

https://doi.org/10.54963/nci.v1i1.2379
Albu, I., & Voß, T. E. (2026). Retiring Linguistics for a Unified Language Science. Neurolinguistic Communication Intervention, 1(1), 16–31. https://doi.org/10.54963/nci.v1i1.2379
Volume 1 Issue 1 (2026)
Copyright (c) 2026 Irina Albu, Torben Edward Voß
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors

  • Irina Albu

    Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands
    Faculty of Arts, Humanities and Social Sciences, School of Psychology, Trinity College Dublin, D02 PN40 Dublin, Ireland
  • Torben Edward Voß

    Faculty of Arts, Humanities and Social Sciences, School of Psychology, Trinity College Dublin, D02 PN40 Dublin, Ireland

Received: 3 December 2025; Revised: 7 January 2026; Accepted: 22 February 2026; Published: 11 March 2026

Language research has never been richer; spanning formal theory, documentation, neuroscience, psychology, education and AI. Yet it remains partitioned by disciplinary silos, methodological habits, and Western, Educated, Industrialized, Rich, and Democratic (WEIRD) sampling biases. We argue that sustained progress on core problems, i.e., how language is learned, processed, varies, breaks down and can be engineered, requires “retiring linguistics” as an isolated discipline and consolidating expertise within an integrated Language Science. This shift does not compromise rigor; rather, it situates formal modelling alongside quantitative evidence, field-based research, clinical and technological applications. Recent advances that bridge traditional boundaries such as neurosemantic mapping, speech neuroprosthetics, and computational approaches to sociolinguistic variation illustrate the potential of such integration. At the same time structural barriers including departmental incentives, fragmented training pathways, and inconsistent terminology continue to limit coordination across fields. This article combines a critical review of contemporary language research with a concrete proposal for institutional and epistemic reforms: transdisciplinary institutes and appointments, evaluation criteria that reward collaboration, curricula that braid theory, computation and field methods, funding and venues for cross-field work, ethical frameworks centred on partnership, and benefit-sharing with language communities. Unifying around problems rather than on departments can deliver more universally applicable research and greater societal benefit, from equitable language technologies and education, to improved clinical outcomes, by aligning explanations across levels from neurons to social networks.

Keywords:

Language Science Interdisciplinary Linguistics Psycholinguistics Methodological Integration Linguistic Diversity

References

  1. Chomsky, N. Persistent topics in linguistic theory. Diogenes 1965, 13, 13–20. DOI: https://doi.org/10.1177/039219216501305102
  2. Pinker, S. The Language Instinct: The New Science of Language and Mind; Penguin Books: London, UK, 1994.
  3. Evans, N.; Levinson, S.C. The myth of language universals: Language diversity and its importance for cognitive science. Behav. Brain Sci. 2009, 32, 429–448. DOI: https://doi.org/10.1017/S0140525X0999094X
  4. Bybee, J.L. Language Change; Cambridge University Press: Cambridge, UK, 2015.
  5. Lieven, E. Usage-based approaches to language development: Where do we go from here? Lang. Cogn. 2016, 8, 346–368. DOI: https://doi.org/10.1017/langcog.2016.16
  6. Goldberg, A.E. Explain Me This: Creativity, Competition, and the Partial Productivity of Constructions; Princeton University Press: Princeton, NJ, USA, 2019.
  7. Tomasello, M. The ontogeny of cultural learning. Curr. Opin. Psychol. 2016, 8, 1–4. DOI: https://doi.org/10.1016/j.copsyc.2015.09.008
  8. Levy, R.P. Communicative efficiency, uniform information density, and the rational speech act theory. In Proceedings of the Annual Meeting of the Cognitive Science Society, Madison, WI, USA, 25–28 July 2018.
  9. ILST. Available online: https://web.sas.upenn.edu/langscience/ (accessed on 27 May 2025).
  10. Willems, R.M.; Frank, S.L.; Nijhof, A.D.; et al. Prediction during natural language comprehension. Cereb. Cortex 2015, 26, 2506–2516. DOI: https://doi.org/10.1093/cercor/bhv075
  11. Goldstein, A.; Zada, Z.; Buchnik, E.; et al. Shared computational principles for language processing in humans and deep language models. Nat. Neurosci. 2022, 25, 369–380. DOI: https://doi.org/10.1038/s41593-022-01026-4
  12. Fedorenko, E.; Ivanova, A.A.; Regev, T.I. The language network as a natural kind within the broader landscape of the human brain. Nat. Rev. Neurosci. 2024, 25, 289–312. DOI: https://doi.org/10.1038/s41583-024-00802-4
  13. Diachek, E.; Blank, I.; Siegelman, M.; et al. The domain-general multiple demand (MD) network does not support core aspects of language comprehension: A large-scale fMRI investigation. J. Neurosci. 2020, 40, 4536–4550. DOI: https://doi.org/10.1523/JNEUROSCI.2036-19.2020
  14. Alday, P.M. M/EEG analysis of naturalistic stories: A review from speech to language processing. Lang. Cogn. Neurosci. 2018, 34, 457–473. DOI: https://doi.org/10.1080/23273798.2018.1546882
  15. Zhao, J.; Gao, R.; Brennan, J.R. Decoding the neural dynamics of headed syntactic structure building. J. Neurosci. 2025, 45, e2126242025. DOI: https://doi.org/10.1523/JNEUROSCI.2126-24.2025
  16. Christiansen, M.H.; Chater, N. The Now-or-Never bottleneck: A fundamental constraint on language. Behav. Brain Sci. 2015, 39, e62. DOI: https://doi.org/10.1017/S0140525X1500031X
  17. Hasson, U.; Egidi, G.; Marelli, M.; et al. Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension. Cognition 2018, 180, 135–157. DOI: https://doi.org/10.1016/j.cognition.2018.06.018
  18. Kirby, M.S.; Spencer, T.D.; Spiker, S.T. Humble behaviorism redux. Behav. Soc. Issues 2022, 31, 133–158. DOI: https://doi.org/10.1007/s42822-022-00092-4
  19. Arnon, I.; Kirby, S. Cultural evolution creates the statistical structure of language. Sci. Rep. 2024, 14, 5255. DOI: https://doi.org/10.1038/s41598-024-56152-9
  20. Futrell, R. Information-theoretic principles in incremental language production. Proc. Natl. Acad. Sci. U.S.A. 2023, 120, e2220593120. DOI: https://doi.org/10.1073/pnas.2220593120
  21. Koplenig, A. Language structure is influenced by the number of speakers but seemingly not by the proportion of non-native speakers. R. Soc. Open Sci. 2019, 6, 181274. DOI: https://doi.org/10.1098/rsos.181274
  22. Henrich, J.; Heine, S.J.; Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 2010, 33, 61–83. DOI: https://doi.org/10.1017/S0140525X0999152X
  23. Kidd, E.; Garcia, R. How diverse is child language acquisition research? First Lang. 2022, 42, 014272372110664. DOI: https://doi.org/10.1177/01427237211066405
  24. Chomsky, N. Derivation by phase. In A Life in Language; MIT Press: Cambridge, MA, USA, 2001; pp. 1–52. DOI: https://doi.org/10.7551/mitpress/4056.003.0004
  25. Keine, S.; Zeijlstra, H. Clause-internal successive cyclicity: Phasality or DP intervention? Nat. Lang. Linguist. Theory 2025, 43, 1119–1182. DOI: https://doi.org/10.1007/s11049-024-09627-3
  26. Blümel, A.; Collins, C. Smuggling and labeling theory. Biolinguistics 2025, 19. DOI: https://doi.org/10.5964/bioling.17229
  27. Wang, N.; Li, J. Estimating the strength and timing of syntactic structure building in naturalistic reading. arXiv preprint 2025, arXiv:2509.23195. DOI: https://doi.org/10.48550/arXiv.2509.23195
  28. Arehalli, S.; Dillon, B.; Linzen, T. Syntactic surprisal from neural models predicts, but underestimates, human processing difficulty from syntactic ambiguities. arXiv preprint 2022, arXiv:2210.12187. DOI: https://doi.org/10.48550/arXiv.2210.12187
  29. Smith, N.J.; Levy, R. The effect of word predictability on reading time is logarithmic. Cognition 2013, 128, 302–319. DOI: https://doi.org/10.1016/j.cognition.2013.02.013
  30. Friederici, A.D. Towards a neural basis of auditory sentence processing. Trends Cogn. Sci. 2002, 6, 78–84. DOI: https://doi.org/10.1016/S1364-6613(00)01839-8
  31. Harris, D.W.; Rubio-Fernandez, P. Common ground: Between formal pragmatics and psycholinguistics. Available online: https://philarchive.org/rec/HARCGF (accessed on 27 May 2025).
  32. Scontras, G.; Degen, J.; Goodman, N.D. On the grammatical source of adjective ordering preferences. Semant. Pragmat. 2019, 12, 1–21. DOI: https://doi.org/10.3765/sp.12.7
  33. Port, R.F.; O’Dell, M. Neutralization of syllable-final voicing in German. J. Phonetics 1985, 13, 455–471. DOI: https://doi.org/10.1016/S0095-4470(19)30797-1
  34. Du, N.; Durvasula, K. Phonetically incomplete neutralisation can be phonologically complete: Evidence from Huai’an Mandarin. Phonology 2022, 39, 559–595. DOI: https://doi.org/10.1017/S0952675723000192
  35. Merrill, W.; Goldberg, Y.; Schwartz, R.; et al. Provable limitations of acquiring meaning from ungrounded form: What will future language models understand? Trans. Assoc. Comput. Linguist. 2021, 9, 1047–1060. DOI: https://doi.org/10.1162/tacl_a_00412
  36. Schwarz-Friesel, M. On the status of external evidence in the theories of cognitive linguistics: Compatibility problems or signs of stagnation in the field? Or: Why do some linguists behave like Fodor’s input systems? Lang. Sci. 2012, 34, 656–664. DOI: https://doi.org/10.1016/j.langsci.2012.04.007
  37. Priem, J.; Piwowar, H.; Orr, R. OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. arXiv preprint 2022, arXiv:2205.01833. DOI: https://doi.org/10.48550/arXiv.2205.01833
  38. Oxley, K. Partial and particularistic: Grant peer review panels evaluating interdisciplinarity. Res. Eval. 2024, 34, rvaf037. DOI: https://doi.org/10.1093/reseval/rvaf037
  39. Xiang, S.; Romero, D.M.; Teplitskiy, M. Evaluating interdisciplinary research: Disparate outcomes for topic and knowledge base. Proc. Natl. Acad. Sci. U.S.A. 2025, 122. DOI: https://doi.org/10.1073/pnas.2409752122
  40. Rushforth, A.; De Rijcke, S. Practicing responsible research assessment: Qualitative study of faculty hiring, promotion, and tenure assessments in the United States. Res. Eval. 2024, 33, rvae007. DOI: https://doi.org/10.1093/reseval/rvae007
  41. Mäkinen, E.I.; Evans, E.D.; McFarland, D.A. Interdisciplinary research, tenure review, and guardians of the disciplinary order. J. High. Educ. 2024, 96, 54–81. DOI: https://doi.org/10.1080/00221546.2024.2301912
  42. Eddington, D. Linguistics and the scientific method. Available online: https://www.researchgate.net/publication/228848641_Linguistics_and_the_scientific_method (accessed on 27 May 2025).
  43. Gibbs, R.W. Introspection and cognitive linguistics: Should we trust our own intuitions? Annu. Rev. Cogn. Linguist. 2006, 4, 135–151. DOI: https://doi.org/10.1075/arcl.4.06gib
  44. Linzen, T.; Oseki, Y. The reliability of acceptability judgments across languages. Glossa 2018, 3. DOI: https://doi.org/10.5334/gjgl.528
  45. Sönning, L.; Werner, V. The replication crisis, scientific revolutions, and linguistics. Linguistics 2021, 59, 1179–1206. DOI: https://doi.org/10.1515/ling-2019-0045
  46. Grieve, J. Observation, experimentation, and replication in linguistics. Linguistics 2021, 59, 1343–1356. DOI: https://doi.org/10.1515/ling-2021-0094
  47. Bishop, D.V.M. Why is it so hard to reach agreement on terminology? The case of developmental language disorder (DLD). Int. J. Lang. Commun. Disord. 2017, 52, 671–680. DOI: https://doi.org/10.1111/1460-6984.12335
  48. Bender, E.M.; Friedman, B. Data statements for natural language processing: Toward mitigating system bias and enabling better science. Trans. Assoc. Comput. Linguist. 2018, 6, 587–604. DOI: https://doi.org/10.1162/tacl_a_00041
  49. Berghoff, R.; Bylund, E. Diversity in research on the psychology of language: A large-scale examination of sampling bias. Cognition 2025, 256, 106043. DOI: https://doi.org/10.1016/j.cognition.2024.106043
  50. Zhu, S.; Supryadi; Xu, S.; et al. Multilingual large language models: A systematic survey. arXiv preprint 2024, arXiv:2411.11072. DOI: https://doi.org/10.48550/arXiv.2411.11072
  51. Joshi, A.; Dabre, R.; Kanojia, D.; et al. Natural language processing for dialects of a language: A survey. ACM Comput. Surv. 2025, 57, 149. DOI: https://doi.org/10.1145/3712060
  52. Wilcox, E.G.; Hu, M.Y.; Mueller, A.; et al. Bigger is not always better: The importance of human-scale language modeling for psycholinguistics. J. Mem. Lang. 2025, 144, 104650. DOI: https://doi.org/10.1016/j.jml.2025.104650
  53. Su, F. Research on integration of emotion analysis in English modular teaching based on natural language processing. Front. Psychol. 2022, 13. DOI: https://doi.org/10.3389/fpsyg.2022.928883
  54. Bender, E.M.; Koller, A. Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5–10 July 2020; pp. 5185–5198. DOI: https://doi.org/10.18653/v1/2020.acl-main.463
  55. Nguyen, D.; Doğruöz, A.S.; Rosé, C.P.; et al. Computational sociolinguistics: A survey. Comput. Linguist. 2016, 42, 537–593. DOI: https://doi.org/10.1162/COLI_a_00258
  56. Gesing, P.; Tornwall, J.; Kulo, V.; et al. Team science in interdisciplinary health professions education research: A multi-institutional case study. Adv. Health Sci. Educ. 2025, 30, 1123–1141. DOI: https://doi.org/10.1007/s10459-024-10393-5
  57. Language Science Press. Available online: https://langsci-press.org (accessed on 27 May 2025).
  58. Grieve, J.; Bartl, S.; Fuoli, M.; et al. The sociolinguistic foundations of language modeling. Front. Artif. Intell. 2025, 7. DOI: https://doi.org/10.3389/frai.2024.1472411
  59. Blasi, D.E.; Henrich, J.; Adamou, E.; et al. Over-reliance on English hinders cognitive science. Trends Cogn. Sci. 2022, 26. DOI: https://doi.org/10.1016/j.tics.2022.09.015
  60. Huth, A.G.; de Heer, W.A.; Griffiths, T.L.; et al. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 2016, 532, 453–458. DOI: https://doi.org/10.1038/nature17637
  61. Anumanchipalli, G.K.; Chartier, J.; Chang, E.F. Speech synthesis from neural decoding of spoken sentences. Nature 2019, 568, 493–498. DOI: https://doi.org/10.1038/s41586-019-1119-1
  62. Dreyer, F.R.; Doppelbauer, L.; Büscher, V.; et al. Increased recruitment of domain-general neural networks in language processing following intensive language-action therapy: fMRI evidence from people with chronic aphasia. Am. J. Speech Lang. Pathol. 2021, 30, 455–465. DOI: https://doi.org/10.1044/2020_AJSLP-19-00150
  63. Morgan, S.E.; Diederen, K.; Vértes, P.E.; et al. Natural language processing markers in first episode psychosis and people at clinical high-risk. Transl. Psychiatry 2021, 11, 630. DOI: https://doi.org/10.1038/s41398-021-01722-y
  64. Palaniyappan, L. More than a biomarker: Could language be a biosocial marker of psychosis? NPJ Schizophr. 2021, 7, 42. DOI: https://doi.org/10.1038/s41537-021-00172-1
  65. Balabin, H.; Tamm, B.; Spruyt, L.; et al. Natural language processing‐based classification of early Alzheimer’s disease from connected speech. Alzheimers Dement. 2025, 21, e14530. DOI: https://doi.org/10.1002/alz.14530
  66. Barbieri, C.; Blasi, D.E.; Arango-Isaza, E.; et al. A global analysis of matches and mismatches between human genetic and linguistic histories. Proc. Natl. Acad. Sci. U.S.A. 2022, 119, e2122084119. DOI: https://doi.org/10.1073/pnas.2122084119
  67. Carroll, S.R.; Garba, I.; Figueroa-Rodríguez, O.L.; et al. The CARE Principles for Indigenous Data Governance. Data Sci. J. 2020, 19. DOI: https://datascience.codata.org/articles/dsj-2020-043
  68. Nekoto, W.; Marivate, V.; Matsila, T.; et al. Participatory research for low-resourced machine translation: A case study in African languages. In Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, Online, 16–20 November 2020; pp. 2144–2160. DOI: https://doi.org/10.18653/v1/2020.findings-emnlp.195
  69. Weidinger, L.; Uesato, J.; Rauh, M.; et al. Taxonomy of risks posed by language models. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, 21–24 June 2022; pp. 214–229. DOI: https://doi.org/10.1145/3531146.3533088
  70. Andringa, S.; Godfroid, A. Sampling bias and the problem of generalizability in applied linguistics. Annu. Rev. Appl. Linguist. 2020, 40, 134–142. DOI: https://doi.org/10.1017/S0267190520000033
  71. Spano, I.; Zhang, Y. Indigenous data sovereignty in intangible cultural heritage governance: A complementary approach to public–private partnerships. Int. J. Cult. Prop. 2025, 32, 167–193. DOI: https://doi.org/10.1017/S0940739125100064