This paper investigates the comprehension of long and short passives in 15 Mandarin preschool children with Developmental Language Disorder (DLD) (aged 4;2–5;11 years), 15 Typically Developing Age-matched (TDA) (aged 4;3–5;8 years) children, and 15 Typically Developing Younger (TDY) (aged 3;2–4;3 years) children by using the picture-sentence matching task. The results reveal that children with DLD encounter more difficulty comprehending long passives compared with short passive, that they perform worse on the comprehension task than TDA children and TDY children, and that this population is more likely to commit thematic role reversal errors and point to pictures with the incorrect agent (patient) than typically developing children. Given that Mandarin passives are Topic Structures, we maintain that children with DLD are insensitive to the edge feature of the moved element in long passives, leading to Relativized Minimality effect and causing the asymmetry between the comprehension of long and short passives. These results align well with the Edge Feature Underspecification Hypothesis. Errors found in the children with DLD in the comprehension task point toward impaired syntactic knowledge and the lexical semantic deficit.
In this conceptual replication of Sparks and Dale ([2023]. The prediction from MLAT to L2 achievement is largely due to MLAT asessment of underlying L1 abilities. Studies in Second Language Acquisition, 1–25) utilizing a dataset previously reported by Sparks et al. ([2009]. Long-term relationships among early L1 skills, L2 aptitude, L2 affect, and later L2 proficiency. Applied Psycholinguistics, 30, 725–755.), L1 achievement scores over 1st–5th grades and L2 aptitude scores from the Modern Language Aptitude Test (MLAT) in 9th grade were examined as predictors of L2 achievement for U.S. secondary students completing L2 courses in 9th and 10th grades. The study’s focus was on the uniqueness and efficiency of MLAT with respect to measuring L1 achievement in predicting L2 achievement. All L1 measures and MLAT predicted L2 literacy and language, and L1 measures predicted MLAT scores. Word decoding was the strongest overall L1 predictor, though there was variation across the L2 measures. The unique contribution of MLAT was modest, as the majority of total prediction (77–86%) was due to L1 measures. The efficiency of MLAT in capturing predictive variance from L1 abilities was moderately high (median ∼73%) but variable across the L1 and L2 measures. Findings are generally consistent with those of Sparks and Dale (2023) showing that prediction from MLAT to L2 is largely due to MLAT’s assessment of L1 abilities, even though a substantial amount of L2 prediction-relevant L1 variance is missed by MLAT.
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