Event Abstract

Morphosyntactic production in agrammatic aphasia: A cross-linguistic machine learning approach.

  • 1 Department of Linguistics and Scandinavian Languages, University of Oslo, Norway
  • 2 Department of Communication Sciences and Disorders, Long Island University Brooklyn, United States
  • 3 Department Linguistik, Universität Potsdam, Germany
  • 4 Department of Neurology and Neurosurgery, Johns Hopkins University School of Medicine, United States
  • 5 Department of Swedish, University of Gothenburg, Sweden

Introduction Recent studies on agrammatic aphasia by Fyndanis et al. (2012, 2017) reported evidence against the cross-linguistic validity of unitary accounts of agrammatic morphosyntactic impairment, such as the Distributed Morphology Hypothesis (DMH) (Wang et al., 2014), the two versions of the Interpretable Features’ Impairment Hypothesis (IFIH-1: Fyndanis et al., 2012; IFIH-2: Fyndanis et al., 2018b), and the Tree Pruning Hypothesis (TPH) (Friedmann & Grodzinsky, 1997). However, some of the features/factors emphasized by the accounts above (i.e. involvement of inflectional alternations (DMH), involvement of integration processes (IFIH-1), involvement of both integration processes and inflectional alternations (IFIH-2), position of a morphosyntactic feature/category in the syntactic hierarchy (TPH)) may still play a role in agrammatic morphosyntactic production. These features may act in synergy with other factors in determining the way in which morphosyntactic production is impaired across persons with agrammatic aphasia (PWA) and across languages. Relevant factors may include language-independent and language-specific properties of morphosyntactic categories, as well as subject-specific and task/material-specific variables. The present study addresses which factors determine verb-related morphosyntactic production in PWA and what is their relative importance. Methods We collapsed the datasets of the 24 Greek-, German-, and Italian-speaking PWA underlying Fyndanis et al.’s (2017) study, added the data of two more Greek-speaking PWA, and employed machine learning algorithms to analyze the data. The unified dataset consisted of data on subject-verb agreement, time reference (past reference, future reference), grammatical mood (indicative, subjunctive), and polarity (affirmatives, negatives). All items/conditions were represented as clusters of theoretically motivated features: ±involvement of integration processes, ±involvement of inflectional alternations, ±involvement of both integration processes and inflectional alternations, and low/middle/high position in the syntactic hierarchy. We included 14 subject-specific, category-specific and task/material-specific predictors: Verbal Working Memory (WM), (years of formal) Education, Age, Gender, Mean Length of Utterance in (semi)spontaneous speech (Index 1 of severity of agrammatism), Proportion of Grammatical Sentences in (semi)spontaneous speech (Index 2 of severity of agrammatism), Words per Minute in (semi)spontaneous speech (Index of fluency), Involvement of inflectional alternations, Involvement of integration processes, Involvement of both integration processes and inflectional alternations, Position of a given morphosyntactic category in the syntactic hierarchy (high, middle, low), Item Presentation mode (cross-modal, auditory), Response mode (oral, written), and Language (Greek, German, Italian). Different machine learning models were employed: Random Forest, C5.0 decision tree, RPart, and Support Vector Machine. Results & Discussion Random Forest model outperformed all the other models achieving the highest accuracy (0.786). As shown in Figure 1, the best predictors of accuracy on tasks tapping morphosyntactic production were the involvement of both integration processes and inflectional alternations (categories involving both integration processes and inflectional alternations were more impaired than categories involving one or neither of them), verbal WM capacity (the greater the WM capacity, the better the morphosyntactic production), and severity of agrammatism (the more severe the agrammatism, the worse the morphosyntactic production). Results are consistent with IFIH-2 (Fyndanis et al., 2018b) and studies highlighting the role of verbal WM in morphosyntactic production (e.g., Fyndanis et al., 2018a; Kok et al., 2007).

Figure 1


We are grateful to all individuals who participated in the study. This research was supported by a Marie Curie Intra European Fellowship (awarded to V. Fyndanis) within the 7th European Community Framework Programme, project reference 329795, and by the Research Council of Norway through its Centres of Excellence funding scheme, project number 223265.


Friedmann, N., & Grodzinsky, Y. (1997). Tense and Agreement in agrammatic production: Pruning the syntactic tree. Brain and Language, 56, 397–425.
Fyndanis, V., Arcara, G., Christidou, P., & Caplan, D. (2018a). Morphosyntactic production and verbal working memory: Evidence from Greek aphasia and healthy aging. Journal of Speech, Language and Hearing Research. DOI: 10.1044/2018_JSLHR-L-17-0103
Fyndanis, V., Arfani, D., Varlokosta, S., Burgio, F., Maculan, A., Miceli, G., Arcara, G., Palla, F., Cagnin, A., Papageorgiou, S., & Semenza, C. (2018b). Morphosyntactic production in Greek- and Italian-speaking individuals with probable Alzheimer’s disease: Evidence from subject-verb agreement, tense/time reference, and mood. Aphasiology, 32, 61–87.
Fyndanis, V., Miceli, G., Semenza, C., Capasso, R., Christidou, P., de Pellegrin, S., Gandolfi, M., Killmer, H., Messinis, L., Papathanasopoulos, P., Panagea, E., Smania, N., Burchert, F., & Wartenburger, I. (2017). (Morpho)syntactic production in agrammatic aphasia: Testing three hypotheses within a cross-linguistic approach. Poster presentation at the 9th Annual Meeting of the Society for the Neurobiology of Language. (8 November 2017; Baltimore, Maryland, USA)
Fyndanis, V., Varlokosta, S., & Tsapkini, K. (2012). Agrammatic production: Interpretable features and selective impairment in verb inflection. Lingua, 122, 1134–1147.
Kok, P., van Doorn, A., & Kolk, H. (2007). Inflection and computational load in agrammatic speech. Brain and Language, 102, 273–283.
Wang, H., Yoshida, M., & Thompson, C. K. (2014). Parallel functional category deficits in clauses and nominal phrases: The case of English agrammatism. Journal of Neurolinguistics, 27, 75–102.

Keywords: agrammatic aphasia, morphosyntactic production, Cross-linguistic study, machine learning, tense/time reference, mood, subject-verb agreement, polarity, Greek, Italian, German

Conference: Academy of Aphasia 56th Annual Meeting, Montreal, Canada, 21 Oct - 23 Oct, 2018.

Presentation Type: oral presentation

Topic: not eligible for a student prize

Citation: Fyndanis V and Themistocleous C (2019). Morphosyntactic production in agrammatic aphasia: A cross-linguistic machine learning approach.. Conference Abstract: Academy of Aphasia 56th Annual Meeting. doi: 10.3389/conf.fnhum.2018.228.00075

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Received: 12 Apr 2018; Published Online: 22 Jan 2019.

* Correspondence: Dr. Valantis Fyndanis, Department of Linguistics and Scandinavian Languages, University of Oslo, Oslo, Norway, valantis.fyndanis@gmail.com