Structure in mind, structure in vocal tract

Abstract

We update our understanding of the view that grammar regulates inter-segmental temporal coordination and present an extension of that view to a new domain: we argue that inter-segmental coordination is basic to prosody. It is the glue joining segments together differently in different languages (here, illustrated with examples from Arabic and Spanish) and orchestrates their unfolding in ways corresponding to constructs posited in theoretical analysis. The correspondence is one between organization in mind-brain and organization in vocal tract. Moreover, for both mind-brain and vocal tract, the organization is phonological and abstract. It is so because it holds over segments of various identities: in Arabic, the first segment in // is not prosodified as part of the same unit as // and this holds true also for //, // and so on, regardless of sonority. In contrast, in English or Spanish, a different organization holds. Crucially, uniformity in organization (same organization presiding over sequences with varying segmental makeup) does not imply uniqueness of phonetic exponents: prosodic organization is pleiotropic, simultaneously expressed by more than one phonetic exponent. Finally, two properties of coordination relations are underscored: lawful flexibility and abstractness. The first is revealed in the degrees of freedom with which movements corresponding to any given effector begin; the second in invariances of task-relevant kinematic signatures regardless of the effectors implicated in any given segmental sequence. Once again, abstract phonological structure is mirrored in vocal tracts via coordination relations holding across physiology and the particular modes of its operation.

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Notes

  1. 1.

    See, among others, Angermeyer (2003), Benus et al. (2004), Bradley (2002, 2006, 2007), Davidson (2003, 2006), Hall (2003), Borroff (2007), Goldstein (2011), and Casserly (2012) for other analyses of phonological phenomena with grammar models based on gestural representations and or dynamical principles. See Pouplier (2011) for a review.

  2. 2.

    The chosen sentential and phonetic context was maximally similar across MA, SP. In both, the clusters were at word initial position after /i/, di\(\underline{~~}\)por favor ‘say \(\underline{~~}\) please’ and ʒibi\(\underline{~~}\)hnaja, ‘bring \(\underline{~~}\) here’. The items quantified for MA were 26 repetitions of // and 36 repetitions of //. For SP, 39 repetitions of /, , / and 50 repetitions of // were quantified.

  3. 3.

    See Katz (2012) for the potentially confounding role of VOT regarding the timing of English clusters with respect to the vowel and its import for syllables. Also, the two properties mentioned here are potentially related (see Bombien and Hoole 2013).

  4. 4.

    In Tashlhyit, Ridouane et al. (2014) offer evidence from a metalinguistic task, a game wherein speakers responded to a given word by producing either its ‘first part’ or its ‘second part.’ The words used contained some word-medial clusters as in // wherein the possible answers for the ‘first part’ would be any of /u, , / and for the ‘second part’ /, , /. The summary of the reported results does not allow us to discern how many medial items were used or whether there were any differences between word-medial and word-initial clusters. In any case, participants overwhelmingly responded in a way that was interpreted as evidence for the ban on complex onsets, e.g., responses to [] ‘guide’ and [] ‘it is empty’ were /g/+// for the former and for the latter.

  5. 5.

    Movements were recorded using the Carstens AG500 3-D Electromagnetic Articulometry (EMA) system (Hoole and Zierdt 2010; Hoole et al. 2003; Zierdt et al. 1999). Sensor coils attached to the articulators were recorded in an alternating electromagnetic field (Perkell et al. 1992). Three EMA sensors were attached to the tongue. These will be referred to as the ‘tongue tip,’ ‘tongue mid’ and ‘tongue back,’ and were located approximately 1.5 cm, 3 cm, 5 cm from the anterior tip (or apex) of the tongue, with the tongue at rest in the mouth. In addition, sensors were attached to the lower lip, the upper lip, the jaw, as well as to gums of the upper incisors, the bridge of the nose and the left and right side of the head behind the ears (the latter four sensors being used for head correction).

  6. 6.

    Model predictors were the main effects of cluster size and interval type and their interaction. Significant interactions were examined in nested contrasts comparing cluster sizes within interval type. Contrasts between CV vs CCV and CCV vs CCCV within every interval type were specified as sum contrasts by assigning −1 to a baseline level and 1 to the corresponding contrast level, 0 otherwise. Sum coding allows us to determine the effect magnitude for changes in cluster size for each interval type. All models were fitted with full random effects structure, including random intercept adjustments for each subject, triplet set, and repetition nested within stimulus word (Barr et al. 2013). Parsimonious models were achieved by removing random slopes that did not contribute to the model fit (Bates et al. 2015b). The most parsimonious model included random by-subject and by-triplet set slope adjustments for cluster size and interval type. Varying random intercepts and slopes allows us to take individual differences (i.e., between subjects) into account as well as variance differences depending on the triplet set and variance associated with different repetitions of the same stimulus word. All data processing and analysis was performed in R (R Core Team 2013). The lmerTest extension of lme4 (Bates et al. 2015a) was used to estimate the degrees of freedom for the t-statistics, by means of the Satterthwaite approximation, providing relatively conservative p-values (Kuznetsova et al. 2013). All R-scripts and data are available on https://figshare.com/s/a5055436db9a4e463b58. Accessed 2 January 2019.

  7. 7.

    In the previous analyses, we aimed to isolate effects of cluster size and interval type and effectively treated sonority as a random factor (because triplet was a random factor and each triplet has its own sonority profile). Here, we do not treat the sonority profile as a random factor anymore.

  8. 8.

    We use intervals delimited by the articulatory anchor. The results from intervals delimited by the acoustic anchor are qualitatively identical to those we present here.

  9. 9.

    A potential minor exception may be the uniformly level case, shown in the top-left panel, where the t-value is slightly above 2 indicating a minor influence of either the profile or the specific segments in it (or both). Assessing these options properly would require data from several different level sonority profiles.

  10. 10.

    A consequence is that analytical options multiply fast. To wit, Boudlal (2001:71) takes the form [əb] to be iambic, with a second heavy syllable. Dell and Elmedlaoui (2002:295) assign the same syllabic form [əb] but with a second light syllable as we have seen earlier with the FinL templatic constraint.

  11. 11.

    Any theory that makes reference to sequences of speech units (and predicates over them) must necessarily make explicit how such sequences unfold in time but are stable enough to be reproducible in behavior. The problem is unrecognizable in conventional theories precisely because stability in representations is an axiom in these theories. But the challenge is fundamental to a viable theory of phonology. The tools to address this challenge do not derive from the theory of formal languages (a subfield of the mathematical theory of computation, Hopcroft and Ullman 1969; Hopcroft et al. 2007), from which Chomsky and Halle’s (1968) “Sound Pattern of English” and its lineage derive. Rather, the formal foundation for addressing this fundamental challenge is dynamical systems theory, which concerns itself with recurrence in patterning over time.

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Acknowledgements

Thanks to Astrid Assmann and Nomiki Koutsoumpari for help with data measurements. Philip Hoole acknowledges support from German Research Council Grant HO3271/3-1. Adamantios I. Gafos, Jens Roeser and Stavroula Sotiropoulou acknowledge support from ERC Advanced Grant 249440 and, for AIG and SS, German Research Council Grant SFB 1287, Project C04.

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Gafos, A.I., Roeser, J., Sotiropoulou, S. et al. Structure in mind, structure in vocal tract. Nat Lang Linguist Theory 38, 43–75 (2020). https://doi.org/10.1007/s11049-019-09445-y

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Keywords

  • Coordination
  • Moroccan arabic
  • Spanish
  • Electromagnetic articulometry
  • Syllables
  • Prosody
  • Pleiotropy