Skip to main content Skip to journal navigation menu Skip to site footer
contact@ukscip.com
Review

Retiring Linguistics for a Unified Language Science

Irina Albu ORCID ,  Torben Edward Voß

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

    Abstract

    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

    References

    1. 1. Chomsky, N. Persistent topics in linguistic theory. Diogenes 1965, 13, 13–20. DOI: https://doi.org/10.1177/039219216501305102
    2. 2. Pinker, S. The Language Instinct: The New Science of Language and Mind; Penguin Books: London, UK, 1994.
    3. 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. 4. Bybee, J.L. Language Change; Cambridge University Press: Cambridge, UK, 2015.
    5. 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. 6. Goldberg, A.E. Explain Me This: Creativity, Competition, and the Partial Productivity of Constructions; Princeton University Press: Princeton, NJ, USA, 2019.
    7. 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. 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. 9. ILST. Available online: https://web.sas.upenn.edu/langscience/ (accessed on 27 May 2025).
    10. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 26. Blümel, A.; Collins, C. Smuggling and labeling theory. Biolinguistics 2025, 19. DOI: https://doi.org/10.5964/bioling.17229
    27. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 44. Linzen, T.; Oseki, Y. The reliability of acceptability judgments across languages. Glossa 2018, 3. DOI: https://doi.org/10.5334/gjgl.528
    45. 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. 46. Grieve, J. Observation, experimentation, and replication in linguistics. Linguistics 2021, 59, 1343–1356. DOI: https://doi.org/10.1515/ling-2021-0094
    47. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 57. Language Science Press. Available online: https://langsci-press.org (accessed on 27 May 2025).
    58. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
    ×