Abstract
It has been suggested that the Romance first person singular indicative constitutes a natural class with the present subjunctive paradigm for the purposes of stem selection (Maiden 2005), thus forming a kind of ‘diagonal syncretism’, as the latter shares no morphosyntactic features with the former. The existence of such patterns has been taken to be an argument for autonomous morphology and the existence of unnatural ‘morphomes’, in the sense of Aronoff (1994). Our experimental investigations with native speakers of Portuguese, Italian, and Spanish reveal that this pattern is underlearned, and that speakers do not generalize it to novel forms, instead preferring the 2nd person singular indicative to the 1st person as the base for the derivation of the subjunctive paradigm (and the 2nd person indicative as opposed to the 2nd person subjunctive as the base for the derivation of the 1st person indicative as well). The results implicate a role for naturalness biases in morphological structure, and an awareness that the first person singular is an unreliable and idiosyncratic base for productive inflectional identity. We then study the underlearning of the L-morphome in terms of historical change in the salience of these patterns. We demonstrate, through means of diachronic corpus studies spanning five centuries, a change in the ratio of first conjugation verbs to second & third conjugation verbs, and a resulting decrease in the relative type frequency of where morphomic verbs reside. If indeed learners need increased evidence in order to incorporate and actively uptake unnatural patterns, this lexical support has dwindled over time. Even though many of the morphomic verbs have maintained a very high token frequency (allowing them to survive as memorized), their productivity has diminished over time, and hence they go unlearned as a generalizable pattern. When the distribution of irregular alternations is overshadowed in the lexicon, a morphologically unnatural pattern may cease to maintain its status as part of the grammar.
Appendix 1: construction of best mixed-models
This section describes the procedures used to construct the best mixed models for each of the languages analyzed in the experiments, before proceeding to overall analyses of the best predictors of variance and the ratio of Natural responses to L-shaped responses in each, as detailed in Sections 4.1.2, 5.1.2, and 6.1.2, respectively.
Beginning with European Portuguese, we followed the modelling strategy as documented in Barr et al. (2013): we began with a saturated model, with fully crossed and fully specified random effects. This kind of model has an interaction term for all the predictors as fixed effects, with random intercepts and slopes:
Saturated model for Portuguese results:
Response ~ Group * Frame * Conjugation * Place + (1 + Group *
Frame * Conjugation * Place|Participant) + (1 + Group * Frame *
Conjugation * Place|item)
Due to non-convergence, we simplified the model until it partially-converged. We followed two principles for choosing which term to exclude for the purpose of simplification, 1) hierarchically: most complex (the largest interaction terms) to the least complex (single terms), and 2) by-item slopes before by-participant slopes. The latter principle is justified by the fact that our data were collected from controlled experiments – item variations tend to be smaller than participant variations. A model was deemed to have converged if the relative gradient is below 0.002, as recommended by Ben Bolker (one of the developers of lme4 (R-sig-ME mailing list n.d.)). By inspecting the partially-converged model, we excluded the slope associated with smallest variance. This process was repeated until the model converged, and the resulting converged model is shown below.
Converged model for Portuguese results:
Response ~ Group * Frame * Conjugation * Place + (1 + Group +
Frame + Conjugation + Place|Participant) + (1 + Frame + Place|item)
Next, we followed a data-driven approach to determine the random effect structure of our model, using the backward best-path algorithm, guided by which step of removal of a predictor would lead to the best next model. The model comparison was performed using an anova (test=
Model with the maximal effect structure supported by the data for
Portuguese results
Response ~ Group * Frame * Conjugation * Place + (1 + Group + Frame +
Conjugation|Participant) + (1|item)
We then performed a series of nested model comparison using anova (test=
Model with the maximal effect structure justified by model comparison
for Portuguese results:
Response ~ Group + Frame + Conjugation + Place + Group:Frame +
(1 + Group + Frame|Participant) + (1|item)
This model thereby included main effect terms for Group, Frame, Conjugation, and Place, an interaction term for Group x Frame as well as random effect terms such as a random intercept term for Item, and a random intercept for Participant with random slopes for Group and Frame.
For Italian, the same model selection procedure was used as for Portuguese, reported above. We began with a saturated model as shown below.
Saturated model for Italian:
Response ~ Group * Frame * Alternation + (1 + Group * Frame *
Alternation|Participant) + (1 + Group * Frame * Alternation|item)
The converged model is as shown below.
Converged model for Italian:
Response ~ Group * Frame * Alternation + (1 + Group * Frame *
Alternation – Group:Frame:Alternation|Participant) + (1 + Group +
Frame + Alternation|item)
The model with the maximal effect structure supported by the data is as shown below.
Model with the maximal effect structure supported by the data for Italian:
Response ~ Group * Frame * Alternation + (1|Participant) + (1|item)
We then performed a series of nested model comparison using anova (test=
Model with the maximal effect structure justified by model comparison for Italian results:
Response ~ Group * Frame * Alternation + (1|Participant) + (1|item)
In summary, this model included an interaction term for Group x Frame x Alternation as well as random effect terms such as a random intercept term for Item and for Participant.
For Spanish, the same model selection procedure was used as with the other two languages. We began with a saturated model as shown below.
Saturated model for Spanish:
Response ~ Group * Frame * Person * Place + (1 + Group * Frame * Person *
Place|Participant) + (1 + Group * Frame * Person * Place|item)
The converged model is as shown below.
Converged model for Spanish:
Response ~ Group * Frame * Person * Place + (1 + Group + Frame +
Person + Place|Participant) + (1 + Group + Frame|item)
The model with the maximal effect structure supported by the data is as shown below.
Model with the maximal effect structure supported by the data for Spanish:
Response ~ Group * Frame * Person * Place + (1 + Group + Frame + Place|
Participant) + (1|item)
We then performed a series of nested model comparison using anova (test=
Model with the maximal effect structure justified by model comparison for Spanish:
Response + Group + Frame + Person + Place + Group:Frame + Group:
Person + Group:Place + Frame:Place + Place:Person + Group:Frame:
Place + Group:Place:Person + (1 + Group + Frame + Place|Participant) +
(1|item)
In sum, the model for Spanish included main effect terms for Group, Frame, Person, and Place, a number of interaction effects, and random effect terms such as a random intercept term for Item, and a random intercept for Participant with random slopes for Group, Frame, and Place.
Acknowledgement
Many thanks to our interlocutors along the course of this research: Asaf Bachrach, Michael Becker, Ricardo Bermúdez-Otero, Ana Castro, Maria Garraffa, Thomas Graf, Kyle Gorman, Martin Maiden, Gertjan Postma, Erica Rodrigues, Leticia Sicuro Corrêa, Donca Steriade, Leo Wetzels, and Marcos Zampieri.
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Note
The authors’ names are listed in alphabetical order. Earlier descriptions of the experimental research were presented in handout form as Nevins and Rodrigues (2012), which the present work supersedes.
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