Hi‐LAB: A New Measure of Aptitude for High‐Level Language Proficiency

Abstract

Few adult second language (L2) learners successfully attain high‐level proficiency. Although decades of research on beginning to intermediate stages of L2 learning have identified a number of predictors of the rate of acquisition, little research has examined factors relevant to predicting very high levels of L2 proficiency. The current study, conducted in the United States, was designed to examine potential cognitive predictors of successful learning to advanced proficiency levels. Participants were adults with varying degrees of success in L2 learning, including a critical group with high proficiency as indicated by standardized language proficiency tests and on‐the‐job language use. Results from a series of group discrimination analyses indicate that high‐level attainment was related to working memory (including phonological short‐term memory and task set switching), associative learning, and implicit learning. We consider the implications for the construct of high‐level language aptitude and identify future directions for aptitude research.

Examining the LLAMA aptitude tests

“…However, Linck et al (2013) argued that phonological short-term memory was of relevance to advanced learners, suggesting that different aspects of memory or aptitude may be relevant at different ages (cf. Abrahamsson and Hyltenstam, 2008;Muñoz, 2014).…”

“…This has given rise to a number of different aptitude tests (e.g. MLAT (Carroll & Sapon, 1959); Pimsleur Aptitude Battery (Pimsleur, 1966); DLAB (Petersen & Al-Haik, 1976); CANAL-FT (Grigornko, Sternberg & Ehrman, 2000); LLAMA (Meara, 2005); HiLAB (Linck et al, 2013)). These tests all have slightly different emphases in what they (claim to) measure and many are not currently available to researchers (see Skehan, 2016, for a fuller discussion).…”

The interface between research on individual difference variables and teaching practice: The case of cognitive factors and personality

“…A contemporary definition of WM is offered by Bunting and Engle (2015): "A convenient analogy for working memory is to think of it as the mental workspace of the mind: The small amount of memory that holds information and the capacity for attention control to manipulate that information for ongoing use" (p. xvii). As far as the relationship between WM and SLA is concerned, the studies conducted to date have addressed all target language skills and subsystems, different levels of proficiency and ultimate attainment, as well as different aspects of bilingualism, with WM having been found to act as an influential factor across all these areas (e.g., Biedroń & Szczepaniak, 2012;DeKeyser & Koeth, 2011;Doughty et al, 2010;Juffs & Harrington, 2011;Kormos & Sáfár, 2008;Linck et al, 2013;Linck, Osthus, Koeth, & Bunting, 2014;Miyake & Friedman, 1998;Robinson, 2003;Sawyer & Ranta, 2001;Skehan, 2015;Wen, 2015Wen, , 2016Wen & Skehan, 2011). WM as a construct composed of four subsystems, that is phonological loop, central executive, visuospatial sketchpad and episodic buffer, was first conceptualized by cognitive psychologists Baddeley and Hitch (1974) and Baddeley (2000).…”

“…This ID is a powerful variable, covering a number of cognitively-based learner differences, which explains the largest proportion of variation in the outcomes of FL learning among all IDs. FL aptitude is the strongest predictor of the rate of progress as well as high levels of achievement in learning an additional language after the closure of critical or sensitive periods (Doughty et al, 2010;Linck et al, 2013;Long, 2013). It is a complex, multi-faceted factor, which means that there is no single FL aptitude, but, rather, there exists a whole range of FL aptitudes which are included in the domain of cognitive IDs (see Abrahamson & Hyltenstam, 2008;Granena & Long, 2013;Robinson, 2002) and operate differently under various learning conditions.…”

Animacy, Frequency and Working Memory Effects in the Acquisition of a Noun-Adjective Agreement Pattern in L2 under Incidental Learning Conditions

“…Moreover, in the high token frequency condition there was a correlation with OS test scores rather than with RS scores, which would suggest that, instead of simply activating automatic knowledge, a learner had to perform some knowledge manipulation. During language processing different WM and executive functions may be engaged (Linck et al, 2013), but little is yet known about what exactly these functions are and how they are involved; we propose that the correlation with OS found in this study indicates that the processes of maintenance, updating and shifting are engaged during knowledge acquisition in a condition where a learner is incidentally exposed to a high number tokens of the target structure (Miyake et al, 1999;Miyake & Friedman, 2012). Thus, such a correlation suggests effortful learning and the attempt of a learner to "make sense" of the knowledge initially tapped implicitly under the incidental learning conditions.…”