The study evaluated whether the direction (inhibitory or facilitative) of the phonological neighborhood density effect in English spoken word recognition was modulated by the relative strength of competitor activation (neighborhood type) in two groups of English-dominant learners of Spanish who differed in language experience. Classroom learners and heritage learners of Spanish identified spoken English words from dense (e.g., BEAR) and sparse (e.g., BOAT) phonological neighborhoods presented in moderate noise. The phonological neighborhood was separately manipulated at word onset (cohort) and word offset (rhyme). Classroom learners were overall slower in recognizing spoken words from denser neighborhoods. Strongly active (onset) neighbors exerted inhibitory effects in both classroom and heritage learners. Critically, weakly active (offset) neighbors exerted inhibitory effects in classroom learners but facilitative effects in heritage learners. The results suggest that the activation of both within and cross-language neighbors should be considered in determining the direction of neighbor effects in bilingual lexical processing.

Meta-analyses play an instrumental role in informing second language (L2) theory and practice. However, current (i.e., classic) approaches to meta-analysis are limited in their ability to do so because they often fail to capture the complexity inherent in primary studies’ research designs. As we argue in this article, when complex L2 studies are represented by simplistic meta-analyses, the latter cannot reach its potential to contribute to the development of cumulative knowledge. To mitigate this issue, we first discuss the fundamental problems of the classic approaches to meta-analysis of complex L2 research. Second, we introduce an alternative meta-analytic framework that will address those problems. Third, we apply the meta-analytic framework discussed to a complex L2 domain. Fourth, we offer free software to facilitate the conduct of the alternative meta-analytic approach described. Finally, we discuss the implications of this alternative framework for making evidence-based recommendations to the relevant stakeholders.

Manual production enhances learning and recall of signs by hearing second language learners; however, the mechanisms enabling this effect are unclear. We examined whether the motor encoding (somatosensory feedback) that occurs during sign production benefits learning and whether it interacts with sign iconicity, which also enhances learning. American Sign Language (ASL) signs varying in iconicity were learned either via production (repetition) with the eyes closed or via observation without production. Signs learned via production were recalled more accurately than signs learned via observation, indicating that motor encoding from manual production enriches the representations of signs. Moreover, the effect of motor encoding interacted with iconicity, suggesting that motor encoding may particularly enhance the recall of signs low in iconicity. Together, these results reveal the importance of somatosensory feedback as a key mechanism underlying the beneficial effect of production on sign learning, demonstrating that feeling one’s own signing promotes learning and recall of signs.

This study expands on previous research into filler-gap dependency processing in second language (L2) English, by means of a replication of Canales’s (2012) self-paced reading study. Canales, among others, found that advanced L2-English speakers exhibited the same processing behavior that Stowe (1986) found for native English processing: On encountering a filler, they posited gaps in licensed positions and avoided positing gaps in grammatically unlicensed island positions. However, the previous L2 studies focused on advanced-level L2 proficiency and did not test specifically for first language (L1) influence. The present study compares two groups of intermediate-level L2-English speakers with contrasting non-wh-movement L1s, Jordanian Arabic and Mandarin, to investigate the effects of L1 influence and individual differences in proficiency. Our results provide evidence that at intermediate level, too, L2 filler-gap processing adheres to grammatical constraints. L1 did not affect this behavior, but proficiency effects emerged, with larger licensed filled-gap effects at higher proficiency.

Elicited imitation (EI) tasks are a practical tool for measuring second language (L2) knowledge and skills. In this study, we implemented a web-based EI task that measures English morphosyntactic knowledge and compared its measurement properties to a traditional laboratory-based EI. A cohort of 149 L2 English learners engaged in the web-based EI task, and 151 participants completed a traditional lab-based EI counterpart. Correlation analyses revealed a significant, comparable relationship between English proficiency and the two EI versions, with the ungrammatical items showing less consistency that neither improved nor harmed the overall EI effectiveness. Factor analyses corroborated the validity of web-programmed EI, with both EI versions relating similarly to time-pressured, implicit knowledge and untimed, explicit knowledge measures. Our results suggest the potential for utilizing web-based EI to substitute lab-based tasks, enabling larger-scale, more diverse sampling. We end with implications for future web-based EI task users and include a coding guideline for customized web-based EI use.

Although lexical diversity is often used as a measure of productive proficiency (e.g., as an aspect of lexical complexity) in SLA studies involving oral tasks, relatively little research has been conducted to support the reliability and/or validity of these indices in spoken contexts. Furthermore, SLA researchers commonly use indices of lexical diversity such as Root TTR (Guiraud’s index) and D (vocd-D and HD-D) that have been preliminarily shown to lack reliability in spoken L2 contexts and/or have been consistently shown to lack reliability in written L2 contexts. In this study, we empirically evaluate lexical diversity indices with respect to two aspects of reliability (text-length independence and across-task stability) and one aspect of validity (relationship with proficiency scores). The results indicated that neither Root TTR nor D is reliable across different text lengths. However, support for the reliability and validity of optimized versions of MATTR and MTLD was found.

A growing body of evidence demonstrates that individual differences in declarative memory may be an important predictor of second language (L2) abilities. However, the evidence comes from studies using different declarative memory tasks that vary in their reliance on verbal abilities and task demands, which preclude estimating the size of the relationship between declarative memory and L2 learning. To address these concerns, we examined the relationship between verbal and nonverbal declarative memory abilities within the same task while controlling for task demands and stimulus modality, to estimate the upper bound of the relationship between verbal and nonverbal declarative memory. Results indicate that when task demands and stimulus modality are controlled, verbal and nonverbal declarative memory abilities shared a medium-to-large amount of underlying variance. However, future studies should exercise caution in appraising associations between declarative memory abilities and L2 learning until a more precise understanding of the underlying mechanisms is achieved.

Researchers in second language (L2) and education domain use different statistical methods to assess their constructs of interest. Many L2 constructs emerge from elements/parts, i.e., the elements define and form the construct and not the other way around. These constructs are referred to as emergent variables (also called components, formative constructs, and composite constructs). Because emergent variables are composed of elements/parts, they should be assessed through confirmatory composite analysis (CCA). Elements of emergent variables represent unique facets of the construct. Thus, such constructs cannot be properly assessed by confirmatory factor analysis (CFA) because CFA and its underlying common factor model regard these elements to be similar and interchangeable. Conversely, the elements of an emergent variable uniquely define and form the construct, i.e., they are not similar or interchangeable. Thus, CCA is the preferred approach to empirically validate emergent variables such as language skills L2 students’ behavioral engagement and language learning strategies. CCA is based on the composite model, which captures the characteristics of emergent variables more accurately. Aside from the difference in the underlying model, CCA consists of the same steps as CFA, i.e., model specification, model identification, model estimation, and model assessment. In this paper, we explain these steps. and present an illustrative example using publicly available data. In doing so, we show how CCA can be conducted using graphical software packages such as Amos, and we provide the code necessary to conduct CCA in the R package lavaan.