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Article published In:
The Mental Lexicon
Vol. 15:3 (2020) ► pp.385–421

Simulating phonological and semantic impairment of English tense inflection with linear discriminative learning

| University of Tübingen
| University of Tübingen
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.
Keywords: inflectional morphology, linear discriminative learning, linear mappings, word and paradigm morphology, aphasia, distributional semantics
Article outline
  • 1.Introduction
  • 2.Previous computational modeling studies
  • 3.The discriminative lexicon
  • 4.Lesioning a discriminative lexicon
    • a.Topological maps for form and meaning
    • b.Lesioning topological maps
  • 5.Simulations
    • a.Experiment 1: Stimuli controlled for frequency
      • Materials and procedure
      • Results
    • b.Experiment 2: Controlling for phonological complexity
      • Materials and procedure
      • Results
    • c.Discussion
  • 6.Summary and conclusions
  • Notes
  • References
Published online: 22 January 2021
DOI logo
https://doi.org/10.1075/ml.20003.hei
References (73)
References
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. DOI logoGoogle Scholar
Baayen, R. H., Chuang, Y., and Blevins, J. P. (2018). Inflectional morphology with linear mappings. The Mental Lexicon, 13(2):232–270. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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, 1181:438–482. DOI logoGoogle Scholar
Baayen, R. H. and Moscoso del Prado Martín, F. (2005). Semantic density and past-tense formation in three Germanic languages. Language, 811:666–698. DOI logoGoogle Scholar
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
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. DOI logoGoogle Scholar
Baayen, R. H. and Smolka, E. (2020). Modelling morphological priming in German with naive discriminative learning. Frontiers in Communication 51 (2020): 17. DOI logoGoogle Scholar
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
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. DOI logoGoogle Scholar
Blacoe, W. and Lapata, M. (2012). A comparison of vector-based representations for semantic composition. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pages 546–556. Association for Computational Linguistics.Google Scholar
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. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 4964–4967. IEEE. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Burzio, L. (2002). Missing players: Phonology and the past-tense debate. Lingua, 1121:157–199. DOI logoGoogle Scholar
Caplan, D. (1992). Language: Structure, processing, and disorders. The MIT Press.Google Scholar
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
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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, pages 1–51. DOI logoGoogle Scholar
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, pages 119–14. DOI logoGoogle Scholar
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, 1471:66–75. DOI logoGoogle Scholar
Colangelo, A., Stephenson, K., Westbury, C., and Buchanan, L. (2003). Word associations in deep dyslexia. Brain and Cognition, 53(2):166–170. DOI logoGoogle Scholar
Corkery, M., Matusevych, Y., and Goldwater, S. (2019). Are we there yet? Encoderdecoder neural networks as cognitive models of English past tense inflection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3868–3877. DOI logoGoogle Scholar
Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems:1695.Google Scholar
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. DOI logoGoogle Scholar
Faroqi-Shah, Y. (2007). Are regular and irregular verbs dissociated in non-fluent aphasia? Brain Research Bulletin, 74(1–3):1–13. DOI logoGoogle Scholar
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
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Gurney, K., Prescott, T., and Redgrave, P. (2001). A computational model of action selection in the basal ganglia. Biol. Cybern., 841:401–423. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Ivens, S. H. and Koslin, B. L. (1991). Demands for Reading Literacy Require New Accountability Methods. Touchstone Applied Science Associates.Google Scholar
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, 961:7592–7597. DOI logoGoogle Scholar
Johnson, K. (2004). Massive reduction in conversational American English. In Spontaneous speech: data and analysis. Proceedings of the 1st session of the 10th international symposium, pages 29–54, Tokyo, Japan. The National International Institute for Japanese Language.Google Scholar
Juola, P. (2000). Double dissociations and neurophysiological expectations. Brain and cognition, 431:206–324.Google Scholar
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, 61:651–665. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Landauer, T., Foltz, P., and Laham, D. (1998). Introduction to latent semantic analysis. Discourse Processes, 251:259–284. DOI logoGoogle Scholar
Linke, M., Broeker, F., Ramscar, M., and Baayen, R. H. (2017). Are baboons learning “orthographic” representations? probably not. PLOS-ONE, 12(8):e0183876. DOI logoGoogle Scholar
Lyons, J. (1968). Introduction to Theoretical Linguistics. Cambridge University Press, Cambridge. DOI logoGoogle Scholar
Maass, W. (1997). Networks of spiking neurons: the third generation of neural network models. Neural networks, 10(9):1659–1671. DOI logoGoogle Scholar
Maaten, L. V. D. and Hinton, G. (2008). Visualizing data using t-sne. Journal of machine learning research, 91(Nov):2579–2605.Google Scholar
MacWhinney, B. and Leinbach, J. (1991). Implementations are not conceptualizations: revising the verb learning model. Cognition, 401:121–157. DOI logoGoogle Scholar
Marslen-Wilson, W. D. and Tyler, L. K. (1997). Dissociating types of mental computation. Nature, 387(6633):592–594. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Meteyard, L., Price, C. J., Woollams, A. M., and Aydelott, J. (2013). Lesions impairing regular versus irregular past tense production. NeuroImage: Clinical, 31:438–449. DOI logoGoogle Scholar
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
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111–3119.Google Scholar
Milin, P., Feldman, L. B., Ramscar, M., Hendrix, P., and Baayen, R. H. (2017). Discrimination in lexical decision. PLOS-one, 12(2):e0171935. DOI logoGoogle Scholar
Miozzo, M. (2003). On the processing of regular and irregular forms of verbs and nouns: evidence from neuropsychology. Cognition, 87(2):101–127. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Mitchell, J. and Lapata, M. (2008). Vector-based models of semantic composition. In ACL, pages 236–244.Google Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Pinker, S. (1991). Rules of language. Science, 253(5019):530–535. DOI logoGoogle Scholar
(1999). Words and rules: The ingredients of language. Basic Books, New York, NY, US.Google Scholar
Pinker, S. and Ullman, M. (2002). Combination and structure, not gradedness, is the issue. Trends in Cognitive Sciences, 61:472–474. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Ramscar, M. (2002). The role of meaning in inflection: Why the past tense does not require a rule. Cognitive Psychology, 45(1):45–94. DOI logoGoogle Scholar
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, pages 64–99. Appleton Century Crofts, New York.Google Scholar
Rumelhart, D. E. and McClelland, J. L. (1986). Parallel distributed processing:Explorations in the microstructure of cognition, volume Volume 21: Psychological and biological models, chapter On Learning the Past Tenses of English Verbs, pages 216–271. MIT Press, Cambridge, MA. DOI logoGoogle Scholar
Seidenberg, M. S. and Gonnerman, L. M. (2000). Explaining derivational morphology as the convergence of codes. Trends in Cognitive Sciences, 4(9):353–361. DOI logoGoogle Scholar
Shafaei-Bajestan, E. and Baayen, R. H. (2018). Wide learning for auditory comprehension. In Yegnanarayana, B., editor, Proceedings of Interspeech 2018, pages 966–970, Hyderabad, India: International Speech Communication Association (ISCA). DOI logoGoogle Scholar
Shafaei-Bajestan, E., Moradipour-Tari, M., and Baayen, R. H. (2020). LDL-AURIS: Error-driven learning in modeling spoken word recognition. PsyArXiv preprint [URL]Google Scholar
Shapiro, K. and Caramazza, A. (2003). Grammatical processing of nouns and verbs in left frontal cortex? Neuropsychologia, 41(9):1189–1198. DOI logoGoogle Scholar
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, 721:16–36. DOI logoGoogle Scholar
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. [URL]. DOI logo
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Westermann, G. and Ruh, N. (2009). Synthetic brain imaging of english past tense inflection. In Proceedings of the Cognitive Science Society, volume 311.Google Scholar
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. DOI logoGoogle Scholar
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