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Language Usage and Second Language Morphosyntax: Effects of Availability, Reliability, and Formulaicity.

Rundi Guo¹, Nick C Ellis¹

¹Language Learning Laboratory, Department of Psychology, University of Michigan, Ann Arbor, MI, United States.

Frontiers in Psychology

|May 17, 2021

Summary

This summary is machine-generated.

Second language learners

Keywords:

SLA availability elicited imitation task formulaicity morphosyntax phrase-superiority effects reliability

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Area of Science:

Psycholinguistics
Second Language Acquisition
Morphology

Background:

Language processing and acquisition are influenced by linguistic construction distributions.
Statistical learning is a key mechanism in language acquisition.
Understanding how learners process morphosyntax is crucial for effective teaching.

Purpose of the Study:

Investigate how statistical distributions at morphological, lexical, and phrasal levels impact second language learners' morphosyntax processing and production.
Analyze Chinese English as a Second Language (ESL) learners' knowledge of four English inflectional morphemes.
Test hypotheses regarding morpheme availability and reliability in processing.

Main Methods:

Elicited Imitation Tasks (EITs) with length- and difficulty-matched sentences.
Analysis of Chinese ESL learners' reproduction of English inflectional morphemes (-ed, -ing, verb -s, plural -s).
Generalized Linear Mixed-Effects Logit Models (GLMMs) to analyze accuracy based on morpheme type, availability, reliability, and phrase frequency.

Main Results:

Morpheme type, availability, and reliability significantly affected accurate morpheme provision.
Lemma frequency did not impact morpheme accuracy.
Phrasal formulaicity (phrase-superiority effect) independently enhanced morpheme reproduction in high-frequency contexts.

Conclusions:

Second language morpheme acquisition is shaped by distributional properties in learner experience.
Mappings between lexis, morphology, phraseology, and semantics are critical.
Supports an emergentist view of statistical symbolic learning in morphology across multiple unit grain sizes.