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What a thousand children tell us about grammatical complexity and working memory: A cross-sectional analysis on the comprehension of clitics and passives in Italian

Published online by Cambridge University Press:  28 November 2023

Vincenzo Moscati
Affiliation:
Dipartimento di Science Sociali, Politiche e Cognitive, University of Siena, Siena, Italy
Andrea Marini*
Affiliation:
Dipartimento di Lingue e Letterature, Comunicazione, Formazione e Società, University of Udine, Udine, Italy
Nicoletta Biondo
Affiliation:
Basque Center on Cognition, Brain, and Language (BCBL), Donostia-San Sebastián, Spain Department of Psychology, University of California Berkeley, Berkeley, CA, USA
*
Corresponding author: Andrea Marini; Email: andrea.marini@uniud.it
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Abstract

Data from 996 Italian-speaking children were collected and analyzed to assess whether a movement-based notion of grammatical complexity is adequate to capture the developmental trend of clitics and passives in Italian. A second goal of the study was to address the relationship between working memory and syntactic development, exploring the hypothesis that higher digit span values predict better comprehension of complex matrix sentences. The results confirm the validity of a ranking of grammatical structures based on constituent movement, with both clitics and passives developing in parallel and later than canonical SVO sentences. Working memory also shows an effect on sentence comprehension in general, but standard measures (digit span forward/backward) do not show a selective advantage in handling complex constructions such as clitics and passives.

Information

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press
Figure 0

Table 1. Sample size, mean and standard deviation (SD) of children’s age across groups

Figure 1

Figure 1. Examples of the picture-matching task in the 3 conditions. Correct Image: SVO, top-right; Passive, top-right; Clitic, bottom-right.

Figure 2

Table 2. Examples of transitive matrix clauses for each grammatical construction

Figure 3

Figure 2. Overall accuracy (proportion of correct answers on total) on the Grammatical Comprehension task. Median, interquartile range, and probability density estimation are reported for each age group.

Figure 4

Figure 3. Average accuracy values for single items in SVO, Passives, and Clitic structures.

Figure 5

Figure 4. Developmental trend across ages and structures: accuracy for SVO, Passives, and Clitics. Error bars indicate 2 * SE. SVO = Canonical Subject – Verb – Object sentence; Clitic = Sentences with a 3rd person object clitic; Passive: Long-passive sentences.

Figure 6

Table 3. Summary of model estimates with Syntactic Movement and Age as predictors, including comparisons between Complex structures. Standard errors, z-values, and p-values for the accuracy data in the first analysis. Formula of the model: Accuracy ∼1 + (Complex structures + Movement) * Age + (Complex Structures +Movement | Subj) + (1 | Item)

Figure 7

Table 4. Sample size, mean and standard deviation (SD) of children’s age across groups, in the analysis assessing the role of memory

Figure 8

Table 5. Correlations between Accuracy and Digit Recall Scores. Results from partial Pearson’s correlation analyses controlling for a potential effect of age

Figure 9

Table 6. Forward digit recall. Summary of model selection and estimates, standard errors, z-values, and p-values of the best-fitting model for forward memory. Formula of the best-fitting model: Accuracy ∼1 + (Complex structures + Movement) * Age + Forward + (Complex structures + Movement | Subj) + (1 | Item)

Figure 10

Table 7. Backward digit recall. Summary of model selection and estimates, standard errors, z-values, and p-values of the best-fitting model for backward memory. Formula of the best-fitting model: Accuracy ∼1 + (Complex Structures + Movement) * Age + Backward + (Complex Structures + Movement | Subj) + (1 | Item)