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Second language (L2) listening requires efficient processing of continuing incoming information (Vandergrift & Goh, 2012). Even so, research into individual differences in L2 listening has mostly shed light on the role of linguistic knowledge measured without time pressure (e.g., Mecarty, 2000; Wang & Treffers-Daller, 2017; cf. Vafaee & Suzuki, 2020), leaving the role of processing speed and automaticity largely unexplored. To close this gap, we explored the determinants of successful listening using three processing tasks at lexical, syntactic, and propositional levels. Participants were 44 Chinese learners of English. Response accuracy afforded measures of vocabulary size, syntactic parsing skills, and formulation of propositional meaning. Reaction times and the coefficient of variation (Segalowitz & Segalowitz, 1993) afforded processing speed and automaticity measures at each level. We found a hierarchical relationship between different levels of processing, whereby lower-level, lexical effects cascade up and are mediated by propositional comprehension in accounting for listening comprehension. The results highlight the importance of considering processing accuracy and speed at different levels of the linguistic hierarchy to explain variability among L2 listeners. Different from most previous studies, we argue for a need to consider the temporal aspects of processing, along with linguistic knowledge, in modeling L2 listening.

I investigated the trajectory of processing variability, as measured by coefficient of variation (CV), using an intentional word learning experiment and reanalyzing published eye-tracking data of an incidental word learning study (Elgort et al., 2018). In the word learning experiment, native English speakers (N = 35) studied Swahili-English word pairs (k = 16) before performing 10 blocks of animacy judgment tasks. Results replicated the initial CV increase reported in Solovyeva and DeKeyser (2018) and, importantly, captured a roughly inverted U-shaped development in CV. In the reanalysis of eye-tracking data, I computed CVs based on reading times on the target and control words. Results did not reveal a similar inverted U-shaped development over time but suggested more stable processing of the high-frequency control words. Taken together, these results uncovered a fuller trajectory in CV development, differences in processing demands for different aspects of word knowledge, and the potential use of CV with eye-tracking research.

This commentary discusses the issue of small samples in instructed second language acquisition research. We discuss the current state of affairs, and consider the disadvantages of small samples. We also explore other considerations regarding sample size, such as research ethics and ecological validity. We present a range of recommendations for researchers to address the issue of small samples. Recommendations include (a) engaging in sample size planning, (b) opting for appropriate designs and analytical approaches, (c) reporting the constraints that sample size places on the interpretation of findings, and (d) cultivating a culture of collaboration and open science.

Much of the literature surrounding bilingual spoken word recognition is based on bilinguals of non-tonal languages. In the Mandarin spoken word recognition literature, lexical tones are often considered as equally important as segments in lexical processing. It is unclear whether and how lexical tones contribute to bilingual language processing. One recent study demonstrates that tonal bilinguals require the availability of both tonal and segmental information to induce cross-language lexical competition during bilingual lexical access, even without phonological overlap between the target and non-target language. The current study investigates whether overt phonological overlap between the target and non-target language would equally require both tonal and segmental information available to induce cross-language lexical competition. We employed two auditory lexical decision experiments with both Mandarin-English bilinguals and English monolinguals to test whether inter-lingual homophones (IH) would induce lexical competition from the non-target language, L1 Mandarin. Our results show that cross-language lexical competition was only observed with the presence of lexical tones, in addition to segmental overlap.

A slowdown or a speedup in response times across experimental conditions can be taken as evidence of online deployment of knowledge. However, response-time difference measures are rarely evaluated on their reliability, and there is no standard practice to estimate it. In this article, we used three open data sets to explore an approach to reliability that is based on mixed-effects modeling and to examine model criticism as an outlier treatment strategy. The results suggest that the model-based approach can be superior but show no clear advantage of model criticism. We followed up these results with a simulation study to identify the specific conditions in which the model-based approach has the most benefits. Researchers who cannot include a large number of items and have a moderate level of noise in their data may find this approach particularly useful. We concluded by calling for more awareness and research on the psychometric properties of measures in the field.

Eye tracking has become an increasingly popular research methodology among language researchers to examine online cognitive processing of second-language (L2) speakers and bilinguals. As the scope of eye-tracking research expands, there is a need to ensure that the methodology is used properly, so as to safeguard the validity of research findings and the empirical basis upon which theories are built. We present five pitfalls in eye-tracking research that may threaten the internal or external validity of the study. We offer concrete recommendations to avoid each pitfall and demonstrate how these recommendations can be implemented so researchers may take full advantage of the opportunities of eye-tracking research. The five pitfalls occur at all stages of the research cycle. We emphasize (1) adopting a confirmatory (theory-driven, hypothesis-testing) approach to research, (2) selecting and handling eye-movement measures in a principled manner, (3) aligning the research design with the eye tracker properties (spatial accuracy, precision), (4) inspecting raw, sample-level eye-tracking data and their preprocessing by the eye-tracking software. We conclude by inviting eye-tracking researchers (5) to embrace open science practices to enhance the replication and reproducibility of their work.

Response times (RTs) have become ubiquitous in second language acquisition (SLA) research, providing empirical evidence for the theorization of the language learning process. Recently, there have been discussions of some fundamental psychometric properties of RT data, including, but not limited to, their reliability and validity. In this light, we take a step back to reflect on the use of RT data to tap into linguistic knowledge in SLA. First, we offer a brief overview of how RT data are most commonly used as vocabulary and grammar measures. We then point out three key limitations of such uses, namely that (a) RT data can lack substantive importance without considerations of accuracy, (b) RT differences may or may not be a satisfactory psychometric individual difference measure, and (c) some tasks designed to elicit RT data may not be sufficiently fine-grained to target specific language processes. Our overarching goal is to enhance the awareness among SLA researchers of these issues when interpreting RT results and stimulate research endeavors that delve into the unique properties of RT data when used in our field.

In November 2021, the UNESCO Recommendation on Open Science was adopted by the UNESCO General Conference, marking a significant milestone in the development of Open Science as the new norm for research, with intended influences on national laws and practices of the 193 UN Member States and far-reaching implications beyond the UN. It is against such a background that Open Applied Linguistics (openappliedlinguistics.org), a new Research Network affiliated with the International Association of Applied Linguistics (AILA), was established in early 2022 and a two-day online symposium on open scholarship in applied linguistics was held in June 2022.

Despite accumulating evidence, accounts for the efficacy of reading-while-listening (RWL) in facilitating vocabulary learning are largely unexamined, hindering a thorough understanding of the reasons underlying the usefulness of such bimodal input. In this article, we report a close replication of Malone (2018), purposefully manipulating the participants’ native language background to shed light on whether the auditory component in RWL promotes spoken-written form mappings. One hundred and eighty-eight English learners from Austria, Belgium, Hong Kong, and Beijing read or read and listened to four stories containing target words for learning. They completed two surprise vocabulary tests and two assessments of working memory capacity. We only replicated a correlation between working memory capacity and the form recognition test reported in the initial study. Thanks to our manipulation, we discovered an important role of L1 background in the effectiveness of RWL for form recognition knowledge. We discuss the implications for RWL research.

Vocabulary researchers have started expanding their assessment toolbox by incorporating timed tasks and psycholinguistic instruments (e.g., priming tasks) to gain insights into lexical development (e.g., Elgort, 2011; Godfroid, 2020b; Nakata & Elgort, 2020; Vandenberghe et al., 2021). These timed sensitive and implicit word measures differ qualitatively from traditional paper- or accuracy-based vocabulary tests and are believed to tap into lexical strength and representations in the mental lexicon (Elgort, 2018; Godfroid, 2020b). As a result, there have been calls to use both traditional (explicit) and these timed and implicit word measures in a complementary manner (e.g., Godfroid, 2020b; Nakata & Elgort, 2020; Vandenberghe et al., 2021). At the same time, researchers must first develop a thorough understanding of how these different types of measures (explicit vs. implicit and timed vs. untimed) relate to each other before they can make informed decisions on their measurement battery. It is thus well-motivated to examine the construct validity of these measures empirically and systematically. In this validation study, I took the first step to fill this research gap by assessing both the predictive and factorial structure validity of these measures.One hundred and forty-five learners of English took part in five vocabulary tasks: (1) a receptive form-meaning task, where they chose an option representing the meaning of the target word embedded in a sentence, (2) a productive form-meaning task, where they produced the target word to fit a sentence context, (3) a computerized Yes-No (reaction time) test, where they indicated if they knew the target word by pressing keys on their keyboard, (4) a masked repetition priming task with lexical decisions, where they judged if a letter string forms a word in English, and (5) a semantic priming task with lexical decisions. Items in all five tests were the same 40 English words sampled across the 2K - 5K frequency bands.Data analysis involved item inspection and extraction of person-related parameters based on Rasch and/or mixed-effects models. The measures of person ability obtained from individual tasks were then submitted to confirmatory factor analyses in order to assess the psychometric dimensionality of the measure battery. The resulting latent factor(s), representing a pure measure of vocabulary under a specific conceptualization, was then used to predict self-reported proficiency to shed light on their predictive validity. With method effects accounted for, the one-factor solution (“Vocabulary Knowledge”) produces a good fit and is preferred based on the principle of parsimony for both the implicit vs. explicit and timed vs. untimed distinctions. This result provides evidence for psychometric unidimensionality of these measures as representing a potential unitary construct of vocabulary knowledge. At the same time, the vocabulary construct has the most explanatory power (predictive validity) when conceptualized distinctly as lexical knowledge (measured untimed tasks) and strength (measured by timed tasks). Taken together, these results foreground the need for researchers to further specify the nature of the vocabulary construct as well as the operational task features with which it can be assessed empirically. Importantly, I call for more measurement validation work as researchers expand their assessment toolboxes in vocabulary research.