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Abstract

Abstract

This study applies the computational theory of the ‘discriminative lexicon’ (Baayen, Chuang, and Blevins, 2019) to the modeling of the production of English verbs in aphasic speech. Under semantic impairment, speakers have been reported to have greater difficulties with irregular verbs, whereas speakers with phonological impairment are described as having greater problems with regulars. Joanisse and Seidenberg (1999) were able to model this dissociation, but only by adding noise to the semantic units of their model. We report two simulation studies in which topographically coherent regions of phonological and semantic networks were selectively damaged. Our model replicated the main findings, including the high variability in the consequences of brain lesions for speech production. Importantly, our model generated these results without having to lesion the semantic system more than the phonological system. The model’s success hinges on the use of a corpus-based distributional vector space for representing verbs’ meanings. Irregular verbs have denser semantic neighborhoods than do regular verbs (Baayen and Moscoso del Prado Martín, 2005). In our model this renders irregular verbs more fragile under semantic impairment. These results provide further support for the central idea underlying the discriminative lexicon: that behavioral patterns can, to a considerable extent, be understood as emerging from the distributional properties of a language and basic principles of human learning.

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2021-01-22
2025-02-10

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References

  1. Arnold, D. , Tomaschek, F. , Lopez, F. , Sering, T. , and Baayen, R. H.
    (2017) Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit. PLOS ONE, 12(4):e0174623. 10.1371/journal.pone.0174623
     https://doi.org/10.1371/journal.pone.0174623 [Google Scholar]
  2. Baayen, R. H. , Chuang, Y. , and Blevins, J. P.
    (2018) Inflectional morphology with linear mappings. The Mental Lexicon, 13(2):232–270. 10.1075/ml.18010.baa
     https://doi.org/10.1075/ml.18010.baa [Google Scholar]
  3. Baayen, R. H. , Chuang, Y.-Y. , Shafaei-Bajestan, E. , and Blevins, J.
    (2019) The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity 2019(1):1–39. 10.1155/2019/4895891
     https://doi.org/10.1155/2019/4895891 [Google Scholar]
  4. Baayen, R. H. , Milin, P. , Filipović Durdević, D. , Hendrix, P. , and Marelli, M.
    (2011) An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118:438–482. 10.1037/a0023851
     https://doi.org/10.1037/a0023851 [Google Scholar]
  5. Baayen, R. H. and Moscoso del Prado Martín, F.
    (2005) Semantic density and past-tense formation in three Germanic languages. Language, 81:666–698. 10.1353/lan.2005.0112
     https://doi.org/10.1353/lan.2005.0112 [Google Scholar]
  6. Baayen, R. H. , Piepenbrock, R. , and Gulikers, L.
    (1995) The CELEX lexical database (CD-ROM). Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA.
     [Google Scholar]
  7. Baayen, R. H. , Shaoul, C. , Willits, J. , and Ramscar, M.
    (2016) Comprehension without segmentation: A proof of concept with naive discriminative learning. Language, Cognition, and Neuroscience, 31(1):106–128. 10.1080/23273798.2015.1065336
     https://doi.org/10.1080/23273798.2015.1065336 [Google Scholar]
  8. Baayen, R. H. and Smolka, E.
    (2020) Modelling morphological priming in German with naive discriminative learning. Frontiers in Communication5 (2020): 17. 10.3389/fcomm.2020.00017
     https://doi.org/10.3389/fcomm.2020.00017 [Google Scholar]
  9. Bellec, G. , Scherr, F. , Hajek, E. , Salaj, D. , Legenstein, R. , and Maass, W.
    (2019) Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets. arXiv preprint arXiv:1901.09049.
     [Google Scholar]
  10. Bird, H. , Lambon Ralph, M. A. , Seidenberg, M. S. , McClelland, J. L. , and Patterson, K.
    (2003) Deficits in phonology and past-tense morphology: What’s the connection?Journal of Memory and Language, 48(3):502–526. 10.1016/S0749‑596X(02)00538‑7
     https://doi.org/10.1016/S0749-596X(02)00538-7 [Google Scholar]
  11. Blacoe, W. and Lapata, M.
    (2012) A comparison of vector-based representations for semantic composition. InProceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pages546–556. Association for Computational Linguistics.
     [Google Scholar]
  12. Blakely, T. , Miller, K. J. , Rao, R. P. , Holmes, M. D. , and Ojemann, J. G.
    (2008) Localization and classification of phonemes using high spatial resolution electrocorticography (ecog) grids. In2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages4964–4967. IEEE. 10.1109/IEMBS.2008.4650328
     https://doi.org/10.1109/IEMBS.2008.4650328 [Google Scholar]
  13. Blumstein, S. E. , Milberg, W. , and Shrier, R.
    (1982) Semantic processing in aphasia: Evidence from an auditory lexical decision task. Brain and language, 17(2):301–315. 10.1016/0093‑934X(82)90023‑2
     https://doi.org/10.1016/0093-934X(82)90023-2 [Google Scholar]
  14. Burzio, L.
    (2002) Missing players: Phonology and the past-tense debate. Lingua, 112:157–199. 10.1016/S0024‑3841(01)00041‑9
     https://doi.org/10.1016/S0024-3841(01)00041-9 [Google Scholar]
  15. Caplan, D.
    (1992) Language: Structure, processing, and disorders. The MIT Press.
     [Google Scholar]
  16. Cheng, X. , Khomtchouk, B. , Matloff, N. , and Mohanty, P.
    (2018) Polynomial regression as an alternative to neural nets. arXiv preprint arXiv:1806.06850.
     [Google Scholar]
  17. Chersi, F. , Ferro, M. , Pezzulo, G. , and Pirrelli, V.
    (2014) Topological self-organization and prediction learning support both action and lexical chains in the brain. Topics in cognitive science, 6(3):476–491. 10.1111/tops.12094
     https://doi.org/10.1111/tops.12094 [Google Scholar]
  18. Cholin, J. , Rapp, B. , and Miozzo, M.
    (2010) When do combinatorial mechanisms apply in the production of inflected words?Cognitive Neuropsychology, 27(4):334–359. 10.1080/02643294.2010.524467
     https://doi.org/10.1080/02643294.2010.524467 [Google Scholar]
  19. Chuang, Y-Y. , Vollmer, M-l. , Shafaei-Bajestan, E. , Gahl, S. , Hendrix, P. , and Baayen, R. H.
    (2020) The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning. Behavior Research Methods, pages1–51. 10.3758/s13428‑020‑01356‑w
     https://doi.org/10.3758/s13428-020-01356-w [Google Scholar]
  20. Chuang, Y.-Y. , Lõo, K. , Blevins, J. P. , and Baayen, R. H.
    (2020) Estonian case inflection made simple. A case study in Word and Paradigm morphology with Linear Discriminative Learning. In Körtvélyessy, L. , and Štekauer, P. (Eds.) Complex Words: Advances in Morphology, pages119–14. 10.1017/9781108780643.008
     https://doi.org/10.1017/9781108780643.008 [Google Scholar]
  21. Cibelli, E. S. , Leonard, M. K. , Johnson, K. , and Chang, E. F.
    (2015) The influence of lexical statistics on temporal lobe cortical dynamics during spoken word listening. Brain and language, 147:66–75. 10.1016/j.bandl.2015.05.005
     https://doi.org/10.1016/j.bandl.2015.05.005 [Google Scholar]
  22. Colangelo, A. , Stephenson, K. , Westbury, C. , and Buchanan, L.
    (2003) Word associations in deep dyslexia. Brain and Cognition, 53(2):166–170. 10.1016/S0278‑2626(03)00102‑7
     https://doi.org/10.1016/S0278-2626(03)00102-7 [Google Scholar]
  23. Corkery, M. , Matusevych, Y. , and Goldwater, S.
    (2019) Are we there yet? Encoderdecoder neural networks as cognitive models of English past tense inflection. InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages3868–3877. 10.18653/v1/P19‑1376
     https://doi.org/10.18653/v1/P19-1376 [Google Scholar]
  24. Csardi, G. and Nepusz, T.
    (2006) The igraph software package for complex network research. InterJournal, Complex Systems:1695.
     [Google Scholar]
  25. Eliasmith, C. , Stewart, T. C. , Choo, X. , Bekolay, T. , DeWolf, T. , Tang, Y. , and Rasmussen, D.
    (2012) A large-scale model of the functioning brain. Science, 338(6111):1202–1205. 10.1126/science.1225266
     https://doi.org/10.1126/science.1225266 [Google Scholar]
  26. Faroqi-Shah, Y.
    (2007) Are regular and irregular verbs dissociated in non-fluent aphasia?Brain Research Bulletin, 74(1–3):1–13. 10.1016/j.brainresbull.2007.06.007
     https://doi.org/10.1016/j.brainresbull.2007.06.007 [Google Scholar]
  27. Ferro, M. , Marzi, C. , and Pirrelli, V.
    (2011) A self-organizing model of word storage and processing: implications for morphology learning. Lingue e linguaggio, 10(2):209–226.
     [Google Scholar]
  28. Griffis, J. C. , Nenert, R. , Allendorfer, J. B. , Vannest, J. , Holland, S. , Dietz, A. , and Szaflarski, J. P.
    (2017) The canonical semantic network supports residual language function in chronic post-stroke aphasia. Human brain mapping, 38(3):1636–1658. 10.1002/hbm.23476
     https://doi.org/10.1002/hbm.23476 [Google Scholar]
  29. Grossman, M. , Carvell, S. , Stern, M. B. , Gollomp, S. , and Hurtig, H. I.
    (1992) Sentence comprehension in parkinson’s disease: The role of attention and memory. Brain and language, 42(4):347–384. 10.1016/0093‑934X(92)90074‑O
     https://doi.org/10.1016/0093-934X(92)90074-O [Google Scholar]
  30. Gurney, K. , Prescott, T. , and Redgrave, P.
    (2001) A computational model of action selection in the basal ganglia. Biol. Cybern., 84:401–423. 10.1007/PL00007984
     https://doi.org/10.1007/PL00007984 [Google Scholar]
  31. Hodges, J. R. and Patterson, K.
    (1995) Is semantic memory consistently impaired early in the course of alzheimer’s disease? neuroanatomical and diagnostic implications. Neuropsychologia, 33(4):441–459. 10.1016/0028‑3932(94)00127‑B
     https://doi.org/10.1016/0028-3932(94)00127-B [Google Scholar]
  32. Ivens, S. H. and Koslin, B. L.
    (1991) Demands for Reading Literacy Require New Accountability Methods. Touchstone Applied Science Associates.
     [Google Scholar]
  33. Joanisse, M. F. and Seidenberg, M. S.
    (1999) Impairments in verb morphology after brain injury: a connectionist model. Proceedings of the National Academy of Sciences, 96:7592–7597. 10.1073/pnas.96.13.7592
     https://doi.org/10.1073/pnas.96.13.7592 [Google Scholar]
  34. Johnson, K.
    (2004) Massive reduction in conversational American English. InSpontaneous speech: data and analysis. Proceedings of the 1st session of the 10th international symposium, pages29–54, Tokyo, Japan. The National International Institute for Japanese Language.
     [Google Scholar]
  35. Juola, P.
    (2000) Double dissociations and neurophysiological expectations. Brain and cognition, 43:206–324.
     [Google Scholar]
  36. Kirov, C. and Cotterell, R.
    (2018) Recurrent neural networks in linguistic theory: Revisiting Pinker and Prince (1988) and the past tense debate. Transactions of the Association for Computational Linguistics, 6:651–665. 10.1162/tacl_a_00247
     https://doi.org/10.1162/tacl_a_00247 [Google Scholar]
  37. Lambon Ralph, M. A. , Braber, N. , McClelland, J. L. , and Patterson, K.
    (2005) What underlies the neuropsychological pattern of irregular regular past-tense verb production?Brain and Language, 93(1):106–119. 10.1016/j.bandl.2004.09.001
     https://doi.org/10.1016/j.bandl.2004.09.001 [Google Scholar]
  38. Landauer, T. , Foltz, P. , and Laham, D.
    (1998) Introduction to latent semantic analysis. Discourse Processes, 25:259–284. 10.1080/01638539809545028
     https://doi.org/10.1080/01638539809545028 [Google Scholar]
  39. Linke, M. , Broeker, F. , Ramscar, M. , and Baayen, R. H.
    (2017) Are baboons learning “orthographic” representations? probably not. PLOS-ONE, 12(8):e0183876. 10.1371/journal.pone.0183876
     https://doi.org/10.1371/journal.pone.0183876 [Google Scholar]
  40. Lyons, J.
    (1968) Introduction to Theoretical Linguistics. Cambridge University Press, Cambridge. 10.1017/CBO9781139165570
     https://doi.org/10.1017/CBO9781139165570 [Google Scholar]
  41. Maass, W.
    (1997) Networks of spiking neurons: the third generation of neural network models. Neural networks, 10(9):1659–1671. 10.1016/S0893‑6080(97)00011‑7
     https://doi.org/10.1016/S0893-6080(97)00011-7 [Google Scholar]
  42. Maaten, L. V. D. and Hinton, G.
    (2008) Visualizing data using t-sne. Journal of machine learning research, 9(Nov):2579–2605.
     [Google Scholar]
  43. MacWhinney, B. and Leinbach, J.
    (1991) Implementations are not conceptualizations: revising the verb learning model. Cognition, 40:121–157. 10.1016/0010‑0277(91)90048‑9
     https://doi.org/10.1016/0010-0277(91)90048-9 [Google Scholar]
  44. Marslen-Wilson, W. D. and Tyler, L. K.
    (1997) Dissociating types of mental computation. Nature, 387(6633):592–594. 10.1038/42456
     https://doi.org/10.1038/42456 [Google Scholar]
  45. Marusch, T. , Jäger, L. A. , Burchert, F. , and Nickels, L.
    (2017) Verb morphology in speakers with agrammatic aphasia. The Mental Lexicon, 12(3):373–403. 10.1075/ml.17006.mar
     https://doi.org/10.1075/ml.17006.mar [Google Scholar]
  46. Meteyard, L. , Price, C. J. , Woollams, A. M. , and Aydelott, J.
    (2013) Lesions impairing regular versus irregular past tense production. NeuroImage: Clinical, 3:438–449. 10.1016/j.nicl.2013.10.005
     https://doi.org/10.1016/j.nicl.2013.10.005 [Google Scholar]
  47. Mikolov, T. , Chen, K. , Corrado, G. , and Dean, J.
    (2013a) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
     [Google Scholar]
  48. Mikolov, T. , Sutskever, I. , Chen, K. , Corrado, G. S. , and Dean, J.
    (2013b) Distributed representations of words and phrases and their compositionality. InAdvances in neural information processing systems, pages3111–3119.
     [Google Scholar]
  49. Milin, P. , Feldman, L. B. , Ramscar, M. , Hendrix, P. , and Baayen, R. H.
    (2017) Discrimination in lexical decision. PLOS-one, 12(2):e0171935. 10.1371/journal.pone.0171935
     https://doi.org/10.1371/journal.pone.0171935 [Google Scholar]
  50. Miozzo, M.
    (2003) On the processing of regular and irregular forms of verbs and nouns: evidence from neuropsychology. Cognition, 87(2):101–127. 10.1016/s0010‑0277(02)00200‑7
     https://doi.org/10.1016/s0010-0277(02)00200-7 [Google Scholar]
  51. Mirman, D. and Britt, A. E.
    (2014) What we talk about when we talk about access deficits. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1634):20120388. 10.1098/rstb.2012.0388
     https://doi.org/10.1098/rstb.2012.0388 [Google Scholar]
  52. Mitchell, J. and Lapata, M.
    (2008) Vector-based models of semantic composition. InACL, pages236–244.
     [Google Scholar]
  53. Nickels, L. and Howard, D.
    (2004) Dissociating effects of number of phonemes, number of syllables, and syllabic complexity on word production in aphasia: It’s the number of phonemes that counts. Cognitive Neuropsychology, 21(1):57–78. 10.1080/02643290342000122
     https://doi.org/10.1080/02643290342000122 [Google Scholar]
  54. Patterson, K. , Lambon Ralph, M. A. , Hodges, J. R. , and McClelland, J. L.
    (2001) Deficits in irregular past-tense verb morphology associated with degraded semantic knowledge. Neuropsychologia, 39(7):709–724. 10.1016/S0028‑3932(01)00008‑2
     https://doi.org/10.1016/S0028-3932(01)00008-2 [Google Scholar]
  55. Pham, H. and Baayen, R. H.
    (2015) Vietnamese compounds show an anti-frequency effect in visual lexical decision. Language, Cognition, and Neuroscience, 30(9):1077–1095. 10.1080/23273798.2015.1054844
     https://doi.org/10.1080/23273798.2015.1054844 [Google Scholar]
  56. Pinker, S.
    (1991) Rules of language. Science, 253(5019):530–535. 10.1126/science.1857983
     https://doi.org/10.1126/science.1857983 [Google Scholar]
  57. (1999) Words and rules: The ingredients of language. Basic Books, New York, NY, US.
     [Google Scholar]
  58. Pinker, S. and Ullman, M.
    (2002) Combination and structure, not gradedness, is the issue. Trends in Cognitive Sciences, 6:472–474. 10.1016/S1364‑6613(02)02013‑2
     https://doi.org/10.1016/S1364-6613(02)02013-2 [Google Scholar]
  59. Pitt, M. , Johnson, K. , Hume, E. , Kiesling, S. , and Raymond, W.
    (2005) The Buckeye corpus of conversational speech: labeling conventions and a test of transcriber reliability. Speech Communication, 45(1):89–95. 10.1016/j.specom.2004.09.001
     https://doi.org/10.1016/j.specom.2004.09.001 [Google Scholar]
  60. Ramscar, M.
    (2002) The role of meaning in inflection: Why the past tense does not require a rule. Cognitive Psychology, 45(1):45–94. 10.1016/S0010‑0285(02)00001‑4
     https://doi.org/10.1016/S0010-0285(02)00001-4 [Google Scholar]
  61. Rescorla, R. A. and Wagner, A. R.
    (1972) A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H. and Prokasy, W. F. , editors, Classical conditioning II: Current research and theory, pages64–99. Appleton Century Crofts, New York.
     [Google Scholar]
  62. Rumelhart, D. E. and McClelland, J. L.
    (1986) Parallel distributed processing:Explorations in the microstructure of cognition, volume Volume2: Psychological and biological models, chapter On Learning the Past Tenses of English Verbs, pages216–271. MIT Press, Cambridge, MA. 10.7551/mitpress/5236.001.0001
     https://doi.org/10.7551/mitpress/5236.001.0001 [Google Scholar]
  63. Seidenberg, M. S. and Gonnerman, L. M.
    (2000) Explaining derivational morphology as the convergence of codes. Trends in Cognitive Sciences, 4(9):353–361. 10.1016/S1364‑6613(00)01515‑1
     https://doi.org/10.1016/S1364-6613(00)01515-1 [Google Scholar]
  64. Shafaei-Bajestan, E. and Baayen, R. H.
    (2018) Wide learning for auditory comprehension. In Yegnanarayana, B. , editor, Proceedings of Interspeech 2018, pages966–970, Hyderabad, India: International Speech Communication Association (ISCA). 10.21437/Interspeech.2018‑2420
     https://doi.org/10.21437/Interspeech.2018-2420 [Google Scholar]
  65. Shafaei-Bajestan, E. , Moradipour-Tari, M. , and Baayen, R. H.
    (2020) LDL-AURIS: Error-driven learning in modeling spoken word recognition. PsyArXiv preprintpsyarxiv.com/v6cu4/
     [Google Scholar]
  66. Shapiro, K. and Caramazza, A.
    (2003) Grammatical processing of nouns and verbs in left frontal cortex?Neuropsychologia, 41(9):1189–1198. 10.1016/S0028‑3932(03)00037‑X
     https://doi.org/10.1016/S0028-3932(03)00037-X [Google Scholar]
  67. Smolka, E. , Preller, K. H. , and Eulitz, C.
    (2014) ‘verstehen’(‘understand’) primes ‘stehen’(‘stand’): Morphological structure overrides semantic compositionality in the lexical representation of German complex verbs. Journal of Memory and Language, 72:16–36. 10.1016/j.jml.2013.12.002
     https://doi.org/10.1016/j.jml.2013.12.002 [Google Scholar]
  68. Tomaschek, F. , Plag, I. , Ernestus, M. , and Baayen, R. H.
    (2019) Modeling the duration of word-final s in english with naive discriminative learning. Journal of Linguistics. https://psyarxiv.com/4bmwg. 10.31234/osf.io/4bmwg
     https://doi.org/10.31234/osf.io/4bmwg [Google Scholar]
  69. Tyler, L. K. , Stamatakis, E. A. , Jones, R. W. , Bright, P. , Acres, K. , and Marslen-Wilson, W. D.
    (2004) Deficits for semantics and the irregular past tense: A causal relationship?Journal of Cognitive Neuroscience, 16(7):1159–1172. 10.1162/0898929041920559
     https://doi.org/10.1162/0898929041920559 [Google Scholar]
  70. Ullman, M. T. , Corkin, S. , Coppola, M. , Hickok, G. , Growdon, J. H. , Koroshetz, W. J. , and Pinker, S.
    (1997) A neural dissociation within language: Evidence that the mental dictionary is part of declarative memory, and that grammatical rules are processed by the procedural system. Journal of Cognitive Neuroscience, 9(2):266–276. 10.1162/jocn.1997.9.2.266
     https://doi.org/10.1162/jocn.1997.9.2.266 [Google Scholar]
  71. Ullman, M. T. , Pancheva, R. , Love, T. , Yee, E. , Swinney, D. , and Hickok, G.
    (2005) Neural correlates of lexicon and grammar: Evidence from the production, reading, and judgment of inflection in aphasia. Brain and Language, 93(2):185–238. 10.1016/j.bandl.2004.10.001
     https://doi.org/10.1016/j.bandl.2004.10.001 [Google Scholar]
  72. Westermann, G. and Ruh, N.
    (2009) Synthetic brain imaging of english past tense inflection. InProceedings of the Cognitive Science Society, volume31.
     [Google Scholar]
  73. Westfall, J. , Kenny, D. A. , and Judd, C. M.
    (2014) Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli. Journal of Experimental Psychology: General, 143(5):2020–2045. 10.1037/xge0000014
     https://doi.org/10.1037/xge0000014 [Google Scholar]
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Simulating phonological and semantic impairment of English tense inflection with linear discriminative learning
The Mental Lexicon 15, 385 (2020); https://doi.org/10.1075/ml.20003.hei
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  • Article Type: Research Article
Keyword(s): aphasia; distributional semantics; inflectional morphology; linear discriminative learning; linear mappings; word and paradigm morphology

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