3.1 The learning difficulties perspective and the Lingusitic Coding Differences Hypothesis (LCDH) model

Inspired by their own earlier studies of L1 literacy research investigating learning difficulties, the two educational psychologists Richard Sparks and Leonore Ganschow, together with their colleagues (Reference Sparks and Ganschow1991, Reference Sparks and Ganschow2001; see also Sparks, Ganschow & Patton Reference Sparks, Ganschow and Patton1995, Reference Sparks, Ganschow, Patton, Kormos and Kontra2008), proposed the LCDH. Supported by a series of empirical studies, the basic premise of the LCDH model lies in the argument that native language (L1) literacy skills are essential for predicting L2 learning. For example, if a student experiences difficulties in L1 phonology/orthography, his/her subsequent L2 learning will likely suffer as well (Sparks & Ganschow Reference Sparks and Ganschow2001: 97). As was suggested by a recent factor-analytical study (Sparks et al. Reference Sparks, Patton, Ganschow and Humbach2011), four basic components of L2 aptitude, including students’ L1 and L2 phonology/orthography skills (subsuming phonemic coding and phonological processing ability); their L1 and L2 language analysis skills (comprising comprehension, grammar, vocabulary, and inductive language learning); their IQ/memory skills (including L1 intelligence and L2 paired-associate learning measures); and self-perceptions of L2 motivation and anxiety, combined to explain 76% of the variance in ultimate oral and written L2 proficiency.

Therefore, in developing the LCDH, Sparks & Ganschow further argue that it is necessary to examine the similarities and the differences between the two languages in question (participants’ L1 and L2). The issue is particularly important regarding potential negative transfer effects from morphosyllabic languages such as Chinese to alphabetic languages such as English and vice versa. The researchers propose to include a certain phonological measure of L1 and L2 in FL aptitude tests (Sparks & Ganschow Reference Sparks and Ganschow2001: 100). In this sense, the LCDH aptitude model as advocated by Sparks and colleagues further complements Carroll's original four-factor view of FL aptitude by adding the extra (sub-)component of ‘L1 and L2 phonology/orthographic decoding skill’ as an important factor of aptitude (see Chan, Skehan & Gong Reference Chan, Skehan and Gong2011 for related suggestions). Arising from this model there is a call for greater depth in cross-linguistic analyses between the relevant L1 and L2 in future research of FL aptitude.

3.2 The successful intelligence perspective and the CANAL-F model

Grigorenko, Sternberg & Ehrman (2000), drawing on Sternberg's (Reference Sternberg1997, Reference Sternberg and Robinson2002) triadic conception of human intelligence (i.e. his so-called ‘successful intelligence’ perspective in which intelligence is conceived as comprising three distinct levels: analytical, creative and practical), proposed a new interpretation of FL aptitude: the Cognitive Ability for Novelty in Language Acquisition-Foreign (CANAL-F) theory. This theory emphasizes the ability to handle novelty and ambiguity when learning an L2. In line with this conceptualization, the authors also devised a new approach to assessment procedures for measuring FL aptitude, i.e. the CANAL-F test.

Specifically, the CANAL-F test focuses on measuring test takers’ recall and inferencing ability to process and acquire new linguistic materials under both immediate and delayed conditions (as cited in Dörnyei & Skehan Reference Dörnyei, Skehan, Doughty and Long2003: 595). The test is administered to the participants in a simulated, naturalistic language learning environment where they are gradually introduced to an artificial language (Ursulu) and then instructed to complete several small learning tasks. The test is postulated by its authors to tap into five knowledge acquisition processes, which include selective encoding, accidental encoding, selective comparison, selective transfer, and selective combination (Grigorenko et al. Reference Grigorenko, Sternberg and Ehrman2000: 392). These cognitive processes are operationalized at the lexical, morphological, semantic and syntactic levels of language and include both visual and oral input and output materials.

The CANAL-F test contains nine sections. The first section involves learning the meanings of neologisms from context, where 24 short paragraphs are presented to participants (orally and visually) and the participants have to guess which of five English alternatives corresponds to each unknown neologism in the paragraph. The second section involves comprehension of the whole passage. The third section deals with continuous paired-associate learning that involves the learning of 60 word-pairs (presented visually and orally). Participants need to produce the correct paired-associate in English (one half) and in Ursulu (the other half). In the fourth section, 20 sets of three to five Ursulu sentences are presented to participants (half visually and half orally) together with their English translations. After this, the participants have to select the best translation out of five for a completely new sentence (in both directions of translation). The final, fifth, section involves learning language rules. Participants are provided with vocabulary, grammar and examples of the workings of Ursulu and are expected to generalize the most salient rules of the language. This learning is then tested with 12 items measuring understanding of Ursulu.

As claimed by the authors (Grigorenko et al. Reference Grigorenko, Sternberg and Ehrman2000), the CANAL-F test distinguishes itself from previous aptitude tests in five significant ways: (a) it is cognitively based and theoretically oriented; (b) it is contextualized; (c) it is situated and dynamic since different sections inter-relate and build cumulatively; (d) it is multi-functional; and (e) it is adaptive. Furthermore, in the CANAL-F test, language learning is believed to involve encoding knowledge in working memory, and storing in and retrieving from long-term memory, all of which are assessed by the immediate and delayed recall tasks. Above all, its emphasis on cumulative learning and assessment should be able to shed new light on designing aptitude tests beyond the traditions of MLAT (Skehan Reference Skehan2015a). Unfortunately, despite the bold move of the authors to offer a theory-driven view on FL aptitude, validation results of the CANAL-F test, as obtained in their empirical studies, did not significantly outperform the MLAT in predictive validity (Sternberg & Grigorenko Reference Sternberg and Grigorenko2002).

3.3 The information processing perspective and the Macro-SLA aptitude model

In contrast to previous attempts to theorize FL aptitude which have treated the concept as self-contained and unrelated to broader issues in SLA, Skehan (Reference Skehan and Robinson2002, Reference Skehan, Gass and Mackey2012, Reference Skehan2015a; also see Dörnyei & Skehan Reference Dörnyei, Skehan, Doughty and Long2003; Chan et al. Reference Chan, Skehan and Gong2011) proposed an aptitude model that builds on developments from accumulating SLA research. In this SLA-compatible aptitude model (as shown in Table 3), Skehan stipulated that different putative components of aptitude should be effectively linked to various SLA developmental stages and their associated cognitive processes. For example, it can be argued that the aptitude components of ‘phonetic coding ability’ and working memory are most likely to be related to the initial stage of input processing and to noticing. As for language analytical ability (a combination of Carroll's original ‘grammar sensitivity’ and ‘inductive language learning’ as well, again, as working memory), Skehan believes they should be related to the stages of pattern identification and restructuring, and extending.

Table 3

Skehan's Macro-SLA aptitude model (based on Skehan Reference Skehan, Granena, Jackson and Yilmaz2016)

Note: Aptitude constructs in italics are new components as opposed to John Carroll's classic model.

Recently, Skehan (Reference Skehan, Granena, Jackson and Yilmaz2016) has related these stages to the burgeoning ‘micro’ literature on language aptitude which avoids the pre-post correlational design typical of the past and instead focuses, usually with relatively brief quasi-experimental designs, on different types of instruction or of feedback (Skehan Reference Skehan2015a). Such studies show more clearly, and at close to a process level, the impact of aptitude variables such as working memory, or the capacity to benefit from different types of feedback, for example. Skehan (Reference Skehan2015a) argues that focal grammar points which have salience, for example, with inversion, or redundancy, with the use of articles, benefit differentially from feedback for higher aptitude learners. In a similar vein, as Ellis (Reference Ellis2012: 313) cogently points out, it can be also hypothesized that L2 learners strong in phonetic coding ability will likely benefit from input-based forms of instruction, while those strong in language analytical ability will fare better with consciousness-raising instructions.

In its more recent development, Skehan (Reference Skehan, Granena, Jackson and Yilmaz2016) further argues that there is something of a split between the first five stages, which are, broadly, concerned with knowledge acquisition, and the remaining stages, which are concerned with control over that knowledge in actual performance, as well as the capacity to sound more native-like. These bring out more clearly the issue of whether greater control is the result of implicit learning taking place, or the automatization to enable very rapid use of explicit learning from the earlier stages (DeKeyser Reference DeKeyser2007).

Skehan's conception of FL aptitude can have significant implications for theoretical advancement. First, instead of solely relying on an aptitude score to predict L2 learning outcomes (typical in Carroll's time), Skehan opts for an approach that serves to explain the underlying causes for the more superficial IDs in the final learning outcome of an L2. This change in approach represents a significant move in that it triggers a revolution in research paradigms of FL aptitude (i.e. from ‘predicting’ to ‘explaining’). Then, besides this enhancement in explanatory power, Skehan also proposes several additional aptitude components that are presumably functioning within various L2 cognitive processes (but were lacking in Carroll's original model), which can in turn impact L2 learners’ performance in corresponding developmental stages of SLA. Despite its theoretical appeal, Skehan's conception of FL aptitude remains at a speculative stage and, therefore, further empirical support is necessary before this model can be fully operationalized and put into practice (but see Skehan Reference Skehan, Granena, Jackson and Yilmaz2016, for relevant discussion). For example, it still lacks a comprehensive account of the nature of the putative aptitude components (e.g. attentional control, working memory and memory retrieval) as well as their fine-grained functioning within SLA processes and domains. In addition, the model may need to provide more articulate specifications of the measures and procedures for assessing these putative aptitude components in practical research.

3.4 Aptitude-treatment interaction and the Aptitude Complexes framework

Another recent proposal to re-conceptualize FL aptitude in applied linguistics is Robinson's (Reference Robinson2005, Reference Robinson and DeKeyser2007, Reference Robinson and Pawlak2012) ‘Aptitude Complexes/Ability Differential’ framework. As its name clearly denotes, there are two closely interlocking hypotheses incorporated in the framework. The first, the Aptitude Complexes Hypothesis (based on the ideas of ‘aptitude complexes’ by Snow Reference Snow, Snow and Farr1987, Reference Snow, Sternberg and Wagner1994) claims that a set of basic cognitive abilities (such as ‘processing speed’, ‘pattern recognition’, ‘phonological working memory capacity,’) combine to form higher-order aptitude complexes (such as ‘noticing the gap’ and ‘memory for contingent speed’) that are being drawn on in learning a certain task. The second, the Ability Differentiation Hypothesis claims that L2 learners demonstrate variations within the set of cognitive abilities (with strengths in some of them and weaknesses in others), thus leading to differentiated profiles in corresponding aptitude complexes. The overall schematic representation of Robinson's framework resembles a wheel-shape that consists of several layers of circles embedded within each other (Robinson Reference Robinson2005: 52), where the inner circle consists of ‘core’ aptitudes or abilities, such as grammatical sensitivity, processing speed, and so on, and the next circle is concerned with aptitude complexes, which relate the core abilities to various learning processes. Then, moving outwards, the remaining two circles are concerned with task aptitudes and finally pragmatic/interactional abilities/traits. These are concerned with more directly instructional factors and contexts (task aptitudes) and broader communicational influences and capacities; these will not be developed here.

The heart of the model is in the two inner circles. The first, abilities, represents a view of fundamental aptitudes, similar in conception to the work of other researchers covered here, although slightly different in detail and emphasis. The next circle, aptitude complexes, represents the most innovative contribution to aptitude theorizing. More specifically, there are four ‘aptitude complexes’ in the framework. They are namely, an aptitude for a focus on form; an aptitude for incidental learning, via oral content; an aptitude for incidental learning via written content; and an aptitude for explicit rule learning. We will describe the first in greater detail, to clarify how the different variables interact. As demonstrated in Figure 1 (see also Ellis & Shintani Reference Ellis and Shintani2013), an aptitude for focus on form is influenced by (a) capacity to notice and (b) by memory for contingent speech, and then each of these can have high or low values, potentially generating four combinations: high noticing and high memory for contingent speech, low noticing but high memory for contingent speech, and so on. The other aptitude complexes similarly are influenced by two ability factors: incidental learning via oral content (memory for contingent speech and deep semantic processing) and via written content (deep semantic processing and memory for contingent text); and complex rule learning (memory for contingent text and metalinguistic rule rehearsal).

Figure 1

Primary and second order abilities and aptitude complexes in Robinson's model (based on Robinson Reference Robinson and DeKeyser2007)

Note: Phonol.WM, PWM = Phonological working memory; WM = Working memory.

The first and third complexes (aptitude for focus on form; aptitude for incidental learning via written content) are represented visually in Figure 1. In this way Robinson is proposing that relevant ‘aptitude’ is not always the same, and that content of learning, or instructional conditions interact with the aspects of aptitude that are most relevant. In effect therefore Robinson is offering hypotheses about the range of different contexts that we need to consider, the basic range of aptitude sub-components, and then how different sub-components may interact with one another in different learning contexts. There is, as yet, little data on how these hypotheses have been tested.

In a sense, Robinson's conception of FL aptitude is similar to Skehan's model in that it also seeks to provide an explanation that aims to account for observable IDs in the final learning outcome among L2 learners. However, it slightly differs from Skehan's approach in that it tries to provide a finer-grained representation of the complex structure of human cognitive abilities (Carroll Reference Carroll1993) and also makes clearer reference to specific educational contexts. In particular, Robinson presents the framework as involving several embedded levels of analysis. For example, basic cognitive abilities belong to the most deeply embedded and fundamental level, while aptitude complexes represent less core, embedded abilities, and finally task aptitudes, as well as actual performance, are the least embedded and most real-world aspects of the model. There is a progression, in other words, from fundamental and general abilities to more specific and variable educational contexts.

Besides depicting a more intricate picture of human cognitive abilities, Robinson's theoretical conception of FL aptitude differs from previous models with its most articulate emphasis on the dynamic interactions between learner ability factors (either of lower-level core cognitive abilities or higher-level aptitude complexes) and the inherent task characteristics (or ‘task complexity’ in his own terms, Robinson Reference Robinson2005, Reference Robinson and DeKeyser2007, Reference Robinson and Pawlak2012). Such aptitude-treatment interactions (ATIs), as Robinson proposes, take place at all levels, with their ultimate influence readily permeating learners’ task performance and their real-world performance.

3.5 Cognitive science and the High Level Language Aptitude Battery (Hi-LAB) model

The most significant contribution to aptitude theory construction and testing in the last few years has been the development of the ‘High-Level Language Aptitude Battery (Hi-LAB) by researchers at the University of Maryland Center for Advanced Study of Language (see Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010; Doughty Reference Doughty, Granena and Long2013, Reference Doughty and Kunnan2014; Linck et al. Reference Linck, Hughes, Campbell, Silbert, Tare, Jackson and Doughty2013). The motivation for this development has been a perceived need to develop an aptitude battery suitable for the prediction and explanation of high levels of L2 proficiency, targeted particularly at talented post-critical-period language learners. The authors raise the possibility that the components of aptitude suitable for high-level learning may be different from those at lower levels, defining high-level aptitude ‘as a composite of domain-general cognitive abilities and specific perceptual abilities’ (Linck et al. Reference Linck, Hughes, Campbell, Silbert, Tare, Jackson and Doughty2013: 535) that are relevant to high-level achievement. The Hi-LAB model is based on the constructs, with associate aptitude measures, shown in Table 4.

Table 4

Constructs and measures in Hi-LAB (based on Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010)

Note: ALTM = Available long-term memory.

The rationale for this factor-based aptitude model and test is clearly rooted in contemporary cognitive science. In particular, there is a major role for working memory, and indeed a very richly interpreted conception of working memory (Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010; Bunting & Engle Reference Bunting, Engle, Wen, Mota and McNeill2015). The different functions of its central executive are probed with care, and then phonological short-term memory has two measures associated with it. Several measures of longer-term memory are also involved, along with the more traditional associative memory as well as indicators of retrieval, implicit learning, and processing speed (with the latter two measured by different facets of the same serial reaction test). Finally, there are two measures of perceptual acuity, for phonemic discrimination and categorization.

At a construct validity level this is a very impressive test and it is likely to be a milestone for high-level aptitude testing for some time to come. At present, however, many projects related to the Hi-LAB model are still in progress (Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010), so there is only limited empirical evidence available to the public. Notwithstanding this, Linck et al. (Reference Linck, Hughes, Campbell, Silbert, Tare, Jackson and Doughty2013) do report an important study where they used Hi-LAB to try to separate a group of ‘successful’ from a group of ‘very successful’ learners. The different sub-tests from Hi-LAB were then examined to see how effective they were in distinguishing between the two groups. The most effective sub-tests in this regard were the Paired Associates test, the Letter Span of phonological short-term memory test, (the Non-word Span was marginal) and the Implicit Learning test. To put it another way, the tests of executive working memory did not distinguish between the groups, nor did processing speed or the auditory tests. This evidence is intriguing and encouraging, but not conclusive in terms of validation for the Hi-LAB. What is needed most, of course, is more evidence from additional groups of language learners. For now, we will simply observe that the tests which seemed more effective here tended to probe long-term memory (consistent with Skehan's Reference Skehan1998 speculation that memory is more important at higher levels of achievement) and also tended to be tests with more prominent language-specific material (for example, the letter span test was more effective at separating the groups than the non-word span test). Overall, with its strong construct validity rooted in contemporary cognitive psychology, the Hi-LAB model will have enormous implications for FL aptitude theory building and testing development in the future.

3.6 Cognitive neuroscience and brain networks as FL aptitude

Recent years have also witnessed cognitive neuroscience research paradigms and techniques foraging into the territory of SLA (Gullberg & Indefrey Reference Gullberg and Indefrey2006; Dörnyei Reference Dörnyei2009; Dien et al. Reference Dien, Weinberg, Blok, O'Rourke, Kayton and Hamedani2011). Findings from these brain-based neurological studies have increasingly become an important source of evidence that complements the earlier dependence on behavioural research conventions (Schumann Reference Schumann and Schumann2004; Li & Grant Reference Li and Grant2015). Among the ID factors outlined in most textbooks (e.g. Skehan Reference Skehan1998; Dörnyei Reference Dörnyei2005, Reference Dörnyei2009), the number of empirical studies on the neurological substrates of FL aptitude is growing, producing accumulating evidence that is consistent and replicable across studies (Biedroń Reference Biedroń2015; also see Li, Legault & Litcofsky Reference Li, Legault and Litcofsky2014). Broadly speaking, these brain-based neurological explorations can be divided into two types: those that focus on the structural aspects of brain anatomy and those connected with functional aspects of brain activation. The anatomical or activation differences are then associated with distinct aspects and levels of L2 learning and processing. In terms of research methodology, most of these neurolinguistic explorations have used a series of hemodynamic methods such as positron emission tomography (PET; measures radioactive tracers), event related potential (ERP; measures temporal aspects of neural events), functional magnetic resonance imaging (fMRI; measures the magnetic resonance signals) and magnetoencephalography (MEG; measures the magnetic resonance signals as a result of electrical activity), and finally diffusion tensor imaging (DTI; maps white matter fiber tracks of the brain) (see Biedroń Reference Biedroń2015: 18, Table 1 for a summary).

In terms of brain anatomy and FL aptitude, the phonological aspect (i.e. pronunciation) of an L2, and in particular, phonetic perception is believed to be the most thoroughly investigated area within these neurolinguistic perspectives (Biedroń Reference Biedroń2015). Amid these strands of research, the most noticeable findings came from Golestani and colleagues (Golestani & Zatorre Reference Golestani and Zatorre2009; Golestani, Price & Scott Reference Golestani, Price and Scott2011; Golestani Reference Golestani2014; see also Hu et al. Reference Hu, Ackermann, Martin, Erb, Winkler and Reiterer2013), who investigated brain structure among talented phoneticians and found that they tend to have greater amounts of grey matter and white matter in the parietal regions of the left hemisphere of their brains. This area (left parietal cortex), generally assumed to be pertinent to auditory and articulatory aspects, is believed to sub-serve phonological working memory as well. Indeed, these strands of studies suggest that the brain anatomy feature of phonological working memory in the left auditory cortex can be a reliable predictor of phonological aptitude to a certain extent, which is compatible with Baddeley, Gathercole & Papagno's (Reference Baddeley, Gathercole and Papagno1998) postulation of the phonological loop of working memory as a ‘language learning device’.

In terms of brain activity and FL aptitude, the study by Tan et al. (Reference Tan, Chen, Yip, Chan, Yang, Gao and Siok2011) is revealing. In this study, the researchers demonstrated through fMRI that bilingual learners’ later skills in L2 reading can be predicted by the activation level of the fusiform–caudate circuit in the participants’ left hemisphere over a one-year span. The left caudate and fusiform regions are usually thought to mediate language control functions and likely are involved in resolving competition arising from L1 during L2 use. This finding thus suggests that the activity level of these brain regions serves as an important neurobiological marker for predicting FL aptitude in the area of L2 reading.

Finally, a recent trend in cognitive neuroscience research in the last decade has been a shift from focusing on tapping functions of individual brain regions to pinning down the spatial and temporal dynamics of brain connectivity networks (Li & Grant Reference Li and Grant2015). A series of studies conducted by Li and colleagues in their lab at Pennsylvania State University have amply demonstrated that brain connectivity networks can indeed serve as a reliable predictor for learning both L2 novel words and (artificial) grammar/sequence rules in different learning contexts (natural, virtual etc.). For example, Yang et al. (Reference Yang, Gates, Molenaar and Li2015) have found that L2 learning success was not only associated with better auditory perception ability (as already found in some of Golestani's brain-imaging studies) but also with more efficient and more flexible brain connectivity detected among more successful learners as opposed to their less successful counterparts. Most interesting of all, even within the learner group that received training, distinct brain connectivity patterns were also observed as a function of explicit vs implicit instruction of the learned grammar/sequence rules. These results echo an earlier study (Yang & Li Reference Yang and Li2012) which found that the explicit learners engaged more of a network that relies on the insula as a relay station, whereas their implicit counterparts evoked a more direct frontal-striatal network in language learning and processing. As we will argue later, it is this latter finding of the distinctive brain networks underlying explicit-implicit learning that represents a promising research paradigm for advancing FL aptitude theory construction and testing in the future (also see Xiang et al. Reference Xiang, Dediu, Roberts, Oort, Norris and Hagoort2012).

3.7 Summary of current theoretical perspectives on FL aptitude

To sum up this section, compared with the earlier conception of FL aptitude (in Carroll's time) and its conventional research paradigm (pre-post correlations), recent theoretical perspectives and models of language aptitude as reviewed here have contributed to a deeper understanding of the concept, each with their unique contributions. To begin with, the LCDH model as proposed by educational psychologists Sparks & Ganschow (Reference Sparks and Ganschow2001) has complemented Carroll's four-factor model by emphasizing the importance of L1-L2 language analysis skills (which echoes an earlier call by Skehan Reference Skehan1986a) and the role of cross-linguistic phonology/orthography analysis (Sparks et al. Reference Sparks, Patton, Ganschow and Humbach2011). These clarify the limitations of Carroll's original conception. In addition, Sternberg & Grigorenko's CANAL-F theory and test have further advanced Carroll's MLAT by advocating the construction of more adaptive and situated aptitude tests (i.e. ‘more dynamic’ and ‘contextualized’, to use their own words). In a similar vein, the Hi-LAB model has significantly expanded the construct validity of FL aptitude in previous conceptions by relying on more current cognitive psychology research in theory construction and test development. It has also been the basis for ATI research (Vatz et al. Reference Vatz, Tare, Jackson, Doughty, Granena and Long2013).

Next we have discussed two acquisition-based approaches from applied linguistics in which Skehan's aptitude model has underscored the importance of integrating language aptitude theories with established developments in SLA, while Robinson's Aptitude Complexes framework successfully pins down the dynamic interactions between aptitude profiles and task features and thus teases out their possible ramifications for L2 instruction and real-life performance. On the other hand, the brain-network-based cognitive neuro-scientific perspectives on FL aptitude have utilized the state-of-the-art technology that offers unique insights not affordable by conventionally psychometric and behaviour-oriented methods in previous aptitude research paradigms.

Most important of all, an in-depth analysis of these more contemporary aptitude models has given further support to a significant role for working memory in FL aptitude, either implicitly or directly. For example, the proponents of the LCDH and CANAL-F models have alluded to the concept of working memory. So, for both the two SLA-oriented proposals and the Hi-LAB model, the construct of working memory occupies a central place in their conception of FL aptitude. Last but not the least, the neurobiological perspectives (such as those studies conducted by Golestani and Yang) have produced the most compelling evidence for the critical role of phonological working memory in essential aspects of L2 learning, including phonology, vocabulary, and grammar/sequence rules (also see Xiang et al. Reference Xiang, Dediu, Roberts, Oort, Norris and Hagoort2012; Li & Grant Reference Li and Grant2015). For these reasons, in the next section we will argue for incorporating working memory as a core component of FL aptitude in the future.

4.1 FL aptitude re-conceptualized from a working memory perspective

4.2 Micro approaches to language aptitude: Towards systematicity

Another major change that has taken place with FL aptitude research in the last 20 years or so is a shift from ‘macro’ to ‘micro’ research designs (Skehan Reference Skehan, Granena, Jackson and Yilmaz2016). Many studies have been published (see Li Reference Li and Grant2015 for a review) which are (a) of relatively or even very short duration, (b) focused on a particular focal grammar point (or two), (c) experimental or quasi-experimental in nature, generally manipulating certain variables relating to type of instruction or type of feedback, and (d) incorporate aptitude measures to explore speed or success in learning in relation to these variables. In addition, a contrast which underlies virtually all the studies reviewed in Li (Reference Li and Grant2015) is between explicit and implicit learning. The former involved instructional treatments with explicit presentation of focused materials and/or feedback, together with some degree of awareness. The latter were based on materials which contained implicit structure but without attention being drawn to this structure in presentation or feedback. (The same distinction will recur later in relation to the focus of aptitude sub-tests themselves).

The studies in Li's (Reference Li2015) review have broadly indicated that aptitude has an effect even in such short-duration, focused studies, and that aptitude tends to generate larger effect sizes with explicit rather than implicit treatments (see Li Reference Li and Grant2015 for a meta-analytical review). In a narrative review of such studies, Skehan (Reference Skehan2015a) argues that one can reflect upon the range of focal grammar points and offer tentative generalizations. With explicit instruction or feedback, aptitude is more likely to enter into stronger relationships with learning when focal grammar points are redundant for communication. In contrast, with implicit feedback, aptitude links with complexity of L1-L2 relationship. In addition, he notes that a relationship with aptitude may have a different time course with explicit and implicit conditions, growing in impact with implicit conditions after showing little relationship initially, whereas effects with explicit manipulations emerge sooner, and do not grow (cf. Serafini & Sanz Reference Serafini and Sanz2015).

A first point to make about this research is that aptitude has shown effects over quite short time intervals, and in a way more clearly related to acquisitional processes. Taken as a whole, these results are remarkable in that they show that aptitude does not exert its effects only over the quite long time intervals which are typical of more conventional macro research. They do suggest that aptitude can be an important explanatory variable at a process level. But an important additional point to make is that, given that this research is quite recent, the reviews now available (Li Reference Li and Grant2015; Skehan Reference Skehan2015a) indicate that different researchers, unsurprisingly, have chosen to relate aptitude to different focal grammar points, as well as different interpretations of feedback and instructional treatment. The suggestions in Skehan (Reference Skehan2015a) are post-hoc interpretations on the basis of the findings which have been reported.

In light of these recent developments, we believe what is needed now is a greater degree of systematic coverage of potentially important variables, such as salience and redundancy in this research paradigm, to give clearer insights as to whether aptitude relates to some focal grammar points better than others. This might give us insight into exactly how aptitude makes the contributions that it does. For example, we need to tease out how different lengths of treatment time have different relationships with different components of aptitude, particularly as this relates to the implicit-explicit contrast, with the possibility that some aptitude components influence learning relatively quickly while others require longer periods (cf. specifically the study by De Graaff (Reference De Graaff1997), which suggested aptitudinal factors take more time to manifest their influence when instruction is implicit). This line of research is exciting as it suggests that a new paradigm for aptitude research has been unlocked. That it has delivered such interesting results already is encouraging and very suggestive that more systematic research of this sort will be very revealing as to the more precise contribution that aptitude can make.

4.3 Knowledge vs control, and implications for aptitude tests

With the development of CANAL-F and Hi-LAB, as well as the availability of LLAMA (Meara Reference Meara2005) as an MLAT substitute, we now have a range of aptitude tests to supplement the MLAT. Earlier, though, we saw Skehan's (Reference Skehan and Robinson2002) acquisitional sequence as a framework for explicating aptitude sub-tests. Skehan (Reference Skehan, Granena, Jackson and Yilmaz2016) argues that despite these relatively new tests, there is something of an imbalance between aptitude subtests, such as grammatical sensitivity, which emphasize knowledge acquisition (which link with processes such as noticing, pattern identification, restructuring, processing of feedback and the like) – tests which exist in reasonable numbers – and aptitude sub-tests, for example, of implicit learning, which focus on control over the system which has been developed. These are not so plentiful. One can wonder, therefore, what such tests would be like. First of all, this would mean creating tests which are associated with being quicker to achieve error-free performance, even if this were slow and effortful. This would represent relatively limited control, in other words, with accuracy somewhat at the expense of fluency and speed (Skehan Reference Skehan, Wen, Mota and McNeill2015b). Beyond that, control is likely to involve speed of automatizing the knowledge which has been partially achieved so that not only accuracy but also fluency and speed of delivery are involved. Then, even beyond such a level of control, there are issues of developing aptitude tests which might predict native-likeness in terms of the lexical choices made. Finally, there is the capacity to use lexicalized language to ease processing pressures even further. These are difficult challenges, and it is difficult to see what such tests would look like. But new tests are needed if we are to be able to account for, and predict, advanced level language achievement (see also Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010 for a similar argument). Currently it seems there only exist some Hi-LAB sub-tests, and possibly LLAMA D.

One might examine existing tests to see what they have to offer. CANAL-F, to some degree, but Hi-LAB, in particular, has made important progress in developing some relevant aptitude tests. For example, Hi-LAB (Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010; Linck et al. Reference Linck, Hughes, Campbell, Silbert, Tare, Jackson and Doughty2013) contains sub-tests on long-term memory retrieval (through priming), implicit learning and processing speed, which are relevant. Many of the specific CANAL-F sub-tests have an implicit component and this may apply especially strongly to the delayed retrieval variant of several of the sub-tests. Finally, LLAMA D (Meara Reference Meara2005) concerns the ability to recognize sounds in a spoken language. Granena (Reference Granena, Granena and Long2013, Reference Granena2015) argues that LLAMA D is a measure of implicit learning since it contains no explicit presentation and was also distinct from other (more explicit) aptitude measures in a factor analytic study while nonetheless loading similarly to other implicit measures. But there is still a lot to do if FL aptitude research is to go beyond how language is acquired, and to measure skills that are relevant to how it is used (Williams Reference Williams, Wen, Mota and McNeill2015). So, in coming years, it would be good to see more tests developed to capture these later processes of L2 development. They would be relevant to all levels of L2 proficiency, since beginner and also intermediate learners have to go beyond slow and effortful performance, even with a limited language repertoire. They too have to make accessible (Skehan Reference Skehan, Cook and Seidlehofer1995) and usable what they have acquired. But such tests would certainly be essential in predicting high levels of accomplishment in an L2, where the nature of improvement is less the learning of new things but rather the capacity to sustain real-time processing, and to use language more idiomatically.

4.4 Domain-specificity/generality and explicit vs implicit processes

At a deeper level, two contrasts have importance and their resolution would have implications not solely for aptitude testing itself but for SLA and applied linguistics more generally. The first concerns whether there is something special about language in language aptitude testing, on the one hand, or whether, in post-critical period learning, we are dealing with a general cognitive activity; in which case, while language may be a face-validity-oriented veneer in aptitude tests, the real processes (of pattern recognition, or automatization and so on) are general cognitive processes, and language has no special role at all. In other words, L2 language learning might be no different, essentially, from learning in other more general domains. The second fundamental issue is whether such learning is primarily explicit or implicit. In this case, the contrast is between learning, which was originally the focus of attention, and possibly rule-based, compared with learning below the level of consciousness, where judgements about correctness could be made, but without the capacity to explicate any rules that might underlie performance.

Regarding the domain-specificity argument, the practical issue concerns whether language aptitude tests should be based on language material, or whether non-linguistic material would be equally appropriate. There are two broad justifications for the former approach. First, it could be that there is the residue of a critical period, or even that a critical period does not come to an end completely: individual differences in the extent to which this occurs was a speculation of Carroll (Reference Carroll1973). As a result, the processing of language material is accomplished at least partly through specific language-equipped capacities. Second, but not necessarily completely distinct, a hierarchical theory of intelligence – sub-abilities such as verbal, mathematical, musical, mechanical were another area to which Carroll (Reference Carroll1993) made enormous contributions in psychology – might suggest that there are specialized abilities in the verbal domain which contribute to language aptitude. Whatever the nature and status of any critical period, if these accounts are correct, then FL aptitude tests should be based on verbal material (as is the case with most existing aptitude tests).

In contrast, one might consider that a critical period has completely ended, with no residue in terms of the parameters set. Alternatively, but with similar effect, one might consider a critical period never existed, given the view that learning is unified (MacWhinney Reference MacWhinney, Kroll and De Groot2005) and that all learning (L1 or L2, and learning in non-language domains) follows similar processes, and is frequency or usage based (Ellis Reference Ellis, Gass and Mackey2012b). Aptitude tests, according to this view, would not inherently require language material – general learning tasks could be used to detect abilities to learn new material and automatize it. Hi-LAB (Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010; Linck et al. Reference Linck, Hughes, Campbell, Silbert, Tare, Jackson and Doughty2013) is closest to this view. Some sub-tests do contain language material but this is generally incidental, and the qualities of the language material are not central to the way the sub-test is designed. Other sub-tests within Hi-LAB do not contain language material at all, consistent with this general cognition approach. In other words, choices about aptitude test content have connections with major issues regarding SLA and the nature of language itself. It follows that the nature of the aptitude tests which work best will have implications for how we think about language – they will be a reflection of how learning takes place in the real world (cf. Williams Reference Williams, Wen, Mota and McNeill2015). Certainly it is the case that aptitude tests which are language-based thereby would become more difficult to use in different L1 contexts since they would need to be translated and re-validated.

Added insight on this analysis comes from recent critical period work and the connection between L1 and L2 learning (Skehan Reference Skehan1986a, Reference Skehan and Meara1986b). Of course, proposals that there is a critical period for language learning are not without controversy. It is claimed that the evidence in support of a critical period is questionable (Bongaerts Reference Bongaerts2005; Birdsong Reference Birdsong, Bohn and Munro2007; Moyer Reference Moyer2007) and that alternative theoretical accounts are possible. If we do assume, though, for this discussion, that a critical period may exist, several studies have suggested (DeKeyser Reference DeKeyser2000; Abrahamsson & Hyltenstam Reference Abrahamsson and Hyltenstam2008; Biedroń & Pawlak Reference Biedroń and Pawlak2016) that high aptitude has a compensating function and is associated with the capacity to reach close to native-like L2 levels after a critical period has closed (also see Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010). On this basis, DeKeyser (Reference DeKeyser2000) proposes that such compensation derives from a high level of talent for explicit language learning. There is even an aptitude connection with resistance to L1 attrition in post-critical period speakers where exposure to the L1 ends or is minimal (Bylund, Abrahamsson & Hyltenstam Reference Bylund, Abrahamsson and Hyltenstam2010). However, there are studies that are not so supportive of this position. Granena & Long (Reference Granena and Long2013) report a less strong compensatory role for aptitude. One factor in accounting for this discrepancy is that the methods used to establish near-native-likeness may be an issue, since grammaticality judgement tests are widely used for this, and it is possible that these vary in the extent to which they are based on explicit or implicit processes, with this contrast accounting for the discrepancy in the results. The competing claims will not be discussed here (but see Skehan Reference Skehan2015a for a discussion). The point is to bring out the centrality of aptitude research for a major issue in linguistics: whether aptitude can compensate for the end of a critical period, and whether we are looking at explicit and/or implicit learning in each case.

We turn next to the explicit-implicit contrast, principally in L2 instructed settings. There are, broadly, three possible positions (Ellis Reference Ellis2015): (a) the ‘Strong Interface’ position is that explicit learning is important and gradually becomes proceduralized so that it is produced automatically (although this is simply explicit knowledge produced very fast); (b) the ‘Weak Interface’ position is that explicit learning facilitates implicit learning, for example, through noticing, so that it can be used autonomously and below the level of consciousness from an implicit knowledge-base; and (c) the ‘No Interface’ position is that direct, albeit slow, implicit learning is the most important type of learning.Footnote ¹ These are theoretical positions but they have implications for aptitude tests. The first two positions are probably the most consistent with existing aptitude test batteries, since batteries like the MLAT do present material and allow explicit processes to operate upon this material. The third position is not so well represented at present. Granena (Reference Granena, Granena and Long2013, Reference Granena2015) has provided evidence to suggest that one sub-test from LLAMA (Meara Reference Meara2005), the sound recognition sub-test, is based on implicit learning. Hi-LAB (Doughty et al. Reference Doughty, Campbell, Mislevy, Bunting, Bowles, Koeth, Prior, Watanabe and Lee2010; Linck et al. Reference Linck, Hughes, Campbell, Silbert, Tare, Jackson and Doughty2013) also contains sub-tests of implicit learning (as well as others of a more explicit nature). If a goal for aptitude tests is to achieve a balance of coverage of implicit and explicit areas, there is certainly scope to develop more implicitly oriented aptitude tests (Williams Reference Williams, Wen, Mota and McNeill2015).

But the analysis demonstrates that FL aptitude does not exist in a vacuum. The nature of aptitude sub-tests, and more especially the nature of the sub-tests that work, provides a commentary on fundamental issues in language learning. This is part of the move, already mentioned, of how aptitude research can stimulate experimental and hypothesis-testing research studies, rather than simply pre-post correlational research designs. In this case, the comparison is between the performance of aptitude sub-tests based on explicit learning and implicit learning respectively. The tests that work better could then provide a commentary on the various interface positions, suggesting whether, for example, explicitly oriented tests outperform (or vice versa) the implicitly oriented tests. This would not simply be a practical method for educational prediction and, even, intervention; it help clarify what language learning actually is.

Two further points are worth discussing. First, language learning contexts vary. It is vital therefore that the range of aptitude sub-tests that have been developed are used in a variety of contexts in order to explore possibilities of ATIs, as proposed by Robinson (Reference Robinson and DeKeyser2007). This general strategy may be particularly revealing regarding the comparative performance of explicit and implicit approaches to aptitude, since each may have areas of greater relevance, e.g. explicit for instructional contexts, implicit for more naturalistic domains. Second, there is scope here to link aptitude work to the sort of neurolinguistic research covered earlier, since there have been proposals that suggest different brain connectivity networks for implicit and explicit learning (Yang et al. Reference Yang, Gates, Molenaar and Li2015). Aptitude could be linked to this research (Schumann Reference Schumann and Schumann2004; Carpenter Reference Carpenter2008; Xiang et al. Reference Xiang, Dediu, Roberts, Oort, Norris and Hagoort2012). Such research may provide clues as to possible brain localization and neural substrates linked to performance on explicit or implicitly oriented aptitude tests (Li & Grant Reference Li and Grant2015).

The impact of this analysis is that the type of questions and research designs that aptitude measures over the coming years could be very different to those of the past. Aptitude could become an important variable within more complex research designs which probe the fundamental issues of domain-specificity and explicit-implicit tensions. Such research designs will change how FL aptitude is perceived more widely within applied linguistics. For this reason, we argue that language aptitude research directed towards these two contrasts may bring back the concept of FL aptitude into the mainstream of SLA and applied linguistics.