Models of L1 writing and processes of L2 writing

One of the early L1 writing models which is particularly relevant for the analysis of the complex interaction of individual differences, L2 proficiency, writing task type, and writing processes is the comprehensive model originally proposed by Flower and Hayes (Reference Flower and Hayes1981), and further revised by Hayes in 1996 and 2012. Hayes’s (Reference Hayes, Levy and Ransdell1996) model consists of two key components: the task environment and the individual. The task environment is subdivided into two categories: the social (the audience and collaborators) and the physical (the text produced so far and the composing medium) environments. The addition of composing medium to the original model by Hayes and Flower (Reference Flower and Hayes1981) recognizes the different affordances provided by digital writing tools as opposed to handwriting. At the level of the individual writer, Hayes (Reference Hayes, Levy and Ransdell1996) postulates that WM, motivation, and affective factors (e.g., the writer’s goals, predispositions, and beliefs) play a role in the implementation of writing processes. Thus, in the model, each writing process that is not fully automatized draws on cognitive resources in WM. Hayes also assumes that writing processes are influenced by affective factors, such as self-perceived writing competence, and hypothesizes that motivation impacts on writers’ goals as well as on the strategies they use to achieve these goals.

In Hayes and Flower’s (Reference Flower and Hayes1981) original model, the writing process is comprised of cyclically recursive phases of planning, translating, and reviewing. In planning their text, writers retrieve relevant information from long-term memory (LTM) and use information from the task environment to establish writing goals and design a writing plan. The writing plan is realized in the process called translation. Reviewing, which incvolves both reading and editing, serves the function of improving the quality of the text produced. When editing their texts, writers assess whether they have successfully achieved their writing goals, identify language and content-related issues in need of attention, and carry out textual changes to enhance the quality of their text. Hayes and Flower (Reference Flower and Hayes1981) distinguish between editing, which can occur any time during writing, and reviewing, which is a deliberate attempt to improve the text after the translation process has finished. In Hayes’s (Reference Hayes, Levy and Ransdell1996) revised model, the three key phases of writing are reconceptualized, and planning is replaced by reflection, which includes processes of problem solving, inferencing, and decision making. The process of text production, which involves generating textual and graphic output, takes the place of translation in the original model. Hayes (Reference Hayes, Levy and Ransdell1996) also substituted the revision phase with a broader concept of text interpretation, which generates internal representations of the graphic and linguistic input and also encompasses the process of critical reading.

Hayes’s (Reference Hayes2012) model is based on a more detailed cognitive conceptualization of the writing process originally proposed by Chenoweth and Hayes (Reference Chenoweth and Hayes2001), which views writing as consisting of three levels: control, resource, and process. The control level includes the task schema, which is a mental representation of information used by the writer to regulate the interaction between writing processes, motivation and goals for planning, and writing and revising. The process level is subdivided into writing processes and the task environment; the latter comprises collaborators and critics, transcribing technology, task materials, written plans, and the text written so far. The writing process is overseen by the evaluator, which is responsible for monitoring the products of the other three processors: the proposer, the translator, and the transcriber. The proposer generates ideas and conceptualizes prelinguistic input for writing. The translator gives ideas linguistic form, and this linguistic output is kept active in WM for the evaluator. If the language output is found to be appropriate by the evaluator, it moves on to the transcriber, which transcribes it into textual form. If the output of the translator is judged inappropriate or inaccurate, the proposer and/or translator is reactivated. In comparison with the earlier model from Chenoweth and Hayes (Reference Chenoweth and Hayes2001), the resource level includes not only long-term memory, WM, and the process of reading but also attention.

As can be seen in the previous description of the writing models proposed by Hayes and colleagues, WM has been given a prominent role as an important cognitive factor in and resource for composing processes. Kellogg (Reference Kellogg, Levy and Ransdell1996), whose model is often taken as the basis for studies examining the role of WM in L2 writing, gives a detailed account of how WM influences various stages of writing. This model will be discussed in depth later, and thus in this section only the main phases of writing outlined in Kellogg’s (Reference Kellogg, Levy and Ransdell1996) model are briefly presented.

In Kellogg’s (Reference Kellogg, Levy and Ransdell1996) model, writing is assumed to consist of four interactive and recursive processes: planning, translating, execution, and monitoring. Translating includes the conceptual planning of the text as well as the linguistic encoding of the conceptual plan, which entails the processes of lexical retrieval, syntactic encoding, and the expression of cohesive relationships. At the execution stage, actual motor movements (using either handwriting or typing) transform the linguistic plan into written output. Monitoring, comprised of revision and editing, oversees that the composed text matches the conceptual plan of the writer.

L2 writing research has been informed by models of L1 writing, particularly that of Hayes and Flower (Reference Flower and Hayes1981). Based on the models described above that view writing as a reflective problem-solving process, L2 writing has been assumed to entail the use of similar cognitive mechanisms and to draw on the same knowledge-based resources as writing in one’s L1. Similar to L1 writers, L2 writers have been found to engage in cyclically recursive phases of planning, translating and reviewing, and monitoring their output (for a review see Roca de Larios et al., Reference Nicolás- Conesa, Coyle, Manchón and Matsuda2016). However, as linguistic encoding processes tend to be less automatic and more effortful in L2 than in L1, lexical retrieval, syntactic encoding, and, in some cases, orthographic transcription might be slower, require more attentional resources, exceed the capacity and processing limits of WM, and be more prone to breakdowns resulting in errors (cf. ibid.). L2 writers who have already developed literacy skills in their L1 need to acquire the necessary L2 linguistic and genre-based knowledge that complements their existing L1 knowledge base in LTM. Although a considerable proportion of knowledge about cohesion, textual organization, genres, task schemas, and the audience can be transferred from L1 to L2 in these cases (cf. Cummins’s [Reference Cummins1981] Interdependence Hypothesis), some L2 proficiency threshold is required for this type of transfer to be possible (cf. Cummins’s [Reference Cummins1981] Threshold Hypothesis). With the development of L2 writing competence, L1 and L2 knowledge stores relevant for writing are likely to become integrated, and this unified and multidimensional knowledge base can support L2 writers in conceptual and linguistic formulation and monitoring (Kobayashi & Rinnert, Reference Kobayashi and Rinnert1992).

One of the consequences of limitations in L2 knowledge and the lower level of automaticity of L2 linguistic encoding processes is that linguistic formulation might consume larger amounts of L2 writers’ attention, and fewer cognitive resources might be available for higher-level writing processes such as conceptual planning and text-level revision (cf. Stevenson et al.’s [Reference Stevenson, Schoonen and De Glopper2007] Inhibition Hypothesis). Empirical research has provided evidence that translating ideas into linguistic form takes longer in L2 than in L1 writing (e.g., Manchón & Roca de Larios, Reference Manchón and de Larios2007, Reference Manchón, Roca de Larios, Soler and Jordá2008), and the overload of attentional resources at the text generation stage often results in a reduction of higher-level writing goals (Chenoweth & Hayes, Reference Chenoweth and Hayes2001). The challenging nature of L2 writing is also observable in decreased L2 writing fluency (Chukharev-Hudilainen et al., Reference Chukharev-Hudilainen, Saricaoglu, Torrance and Feng2019; Manchón et al., Reference Manchón, McBride, Mellado Martínez and Vasylets2023, in this issue), the high frequency of online revisions (Lindgren et al., Reference Lindgren, Miller and Sullivan2008), and the fragmentary nature of formulation processes (Chenoweth & Hayes, Reference Chenoweth and Hayes2001).

Cognitive factors: Working memory and aptitude

Models of the role of WM in L1 writing and their relevance and application to L2 writing

As mentioned earlier, in the fields of both L1 and L2 writing, Kellogg’s (Reference Kellogg, Levy and Ransdell1996) Componential Model of Working Memory in Writing has been highly influential. Based on Baddeley and Hitch’s (Reference Baddeley and Hitch1974) componential model of WM, Kellogg argued that because writing is a conscious and effortful cognitive activity, the CE, which is responsible for the regulation of attention, is involved in every stage of writing. He hypothesized that capacity limitations of the visuo-spatial sketchpad could play a role in the planning phase, as writers often apply visualizations such as diagrams or images for content generation and structuring their text. In his view, translating ideas into linguistic form, monitoring, and reviewing could be constrained by the capacity limitations of the phonological loop. Although the differential roles of the visuo-spatial sketchpad and phonological loop in these stages have been empirically supported by a number of L1 writing studies (e.g., Olive, Reference Olive and Berninger2012), some studies also show that processing constraints in the phonological loop can equally influence editing (Hayes & Chenoweth, Reference Hayes and Chenoweth2006) and planning processes (Kellogg et al., Reference Kellogg, Turner, Whiteford and Mertens2016). An interesting finding in this regard was provided by Olive et al. (Reference Olive, Kellogg and Piolat2008), who found that planning with a diagram required higher spatial WM capacity and resulted in richer ideas than textual outlining, which might also have relevant implications for L2 writing instruction. Although the result shows that visual planning strategies can be beneficial for idea generation, those with lower visuo-spatial WM abilities might not be able to exploit their advantages. Therefore, giving L2 writers autonomy over the choice of visual and textual planning strategies might be recommended. Kellogg also argued that if handwriting and typing are automatized, the execution stage would not draw on WM resources. Hayes’s (Reference Hayes, Levy and Ransdell1996) model of writing also adapted Kellogg’s (Reference Kellogg, Levy and Ransdell1996) conceptualizations in his model and attributed a central role to WM.

Another influential model of the impact of WM on writing processes and outcomes was proposed by McCutchen (Reference McCutchen2011), who built on Daneman and Carpenter’s (Reference Daneman and Carpenter1980) capacity theory of WM. Based on research that compared high- and low-skilled L1 writers, she argued that WM regulates the efficient interaction of writing processes, and until low-level writing skills (such as handwriting) are sufficiently automatized, high-level writing processes (planning, translating, and revising) cannot be used in parallel with low-level processes. Research findings seem to support her assumptions as unskilled L1 writers have been found to produce text in sequential bursts of handwriting between planning phases (see e.g., Olive & Kellogg, Reference Olive and Kellogg2002).

Berninger and Swanson’s (Reference Berninger, Swanson, Carlson and Butterfield1994) work extended McCutchen’s model, and showed that beginner L1 writers’ WM resources are not only depleted because of lack of automaticity in handwriting but also due to effortful spelling. This can result in difficulties in paying attention to other aspects of writing, such as rhetorical structure and the use of complex and sophisticated language. This work led to the development of the Not-so-Simple View of Writing (Berninger & Winn, Reference Berninger, Winn, MacArthur, Graham and Fitzgerald2006) that further refined the role of the CE in writing processes and the development of writing skills. The key components of CE, inhibition, attention shifting, and updating have been found to influence not only lower-level transcription processes but also higher-level writing processes such as keeping strategic goals and multiple ideas active while writing, the retrieval and application of relevant linguistic knowledge and encoding procedures, as well as revising and editing (e.g., Bourdin & Fayol, Reference Bourdin and Fayol1994; Berninger & Swanson, Reference Berninger, Swanson, Carlson and Butterfield1994). Torres’ study (Reference Torres2023, in this issue) also demonstrates that, regardless of whether they write in their L1 or L2, individuals with higher scores on the Ospan test (which taps into the updating function of CE) spend more time pausing within words to resolve linguistic encoding problems. The impact of individual differences in the functioning of the CE can also be observed in L1 writing quality and fluency and can potentially account for the writing difficulties of children who have learning disabilities in the writing and reading domains (Costa et al., Reference Costa, Edwards and Hooper2016). The Not-so-Simple View of Writing not only includes the cognitive construct of CE that is responsible for attention regulation but also postulates a broader role for CE functions that are additionally comprised of goal setting, self-monitoring, and regulation (Berninger & Winn, Reference Berninger, Winn, MacArthur, Graham and Fitzgerald2006). In this model, the extended construct of CE functions has a direct effect on lower-level writing skills (spelling and handwriting), as well as on planning, editing, and revision. A recent study by Ahmed et al. (Reference Ahmed, Kent, Cirino and Keller-Margulis2022) on struggling young L1 readers and writers, however, only verified a direct link between CE and spelling, but found an indirect effect of CE on editing and revision with the mediation of spelling skills. In the field of L2 writing, the role of CE functions has only been recently considered (e.g., Mavrou, Reference Mavrou2020; Michel et al., Reference Michel, Kormos, Brunfaut and Ratajczak2019; Peng et al., Reference Peng, Orosco, Wang, Swanson and Reed2021; Révész et al., Reference Révész, Michel and Lee2023, in this issue), and very few studies have assessed all three functions (inhibition, updating, and attention shifting) (Miyake et al., Reference Miyake, Friedman, Emerson, Witzki, Howerter and Wager2000).

Kim and Schatschneider (Reference Kim and Schatschneider2017) extended the Not-so-Simple View of Writing to develop the Direct and Indirect Effects Model of Writing. As its name suggests, the model proposes that foundational language (vocabulary and grammar) and cognitive skills (WM, attention control) have a direct impact, as well as an indirect effect, with the mediation of higher-order cognitive skills (inferencing, comprehension monitoring, perspective taking) on text production. In their empirical validation of the model with young L1 writers, WM was found to be directly related to children’s vocabulary and grammar knowledge (foundational language skills) and also strongly linked to performance on tasks assessing inferencing and perspective taking (higher-order cognitive skills), as well as handwriting fluency and spelling. An additional novel feature of Kim and Schatschneider’s model is that it acknowledges the relevance of oral discourse production abilities in the development of writing skills and acknowledges the important role that WM plays in children’s ability to retell narrative and informative texts coherently and accurately. The structural equation modeling of their data also revealed that WM did not have a direct effect on writing quality; rather, its indirect effect was mediated by oral discourse production abilities and transcription skills (handwriting fluency and spelling).

Kim and Schatschneider’s (Reference Kim and Schatschneider2017) model has important implications for L2 writing research as well. First of all, it highlights that, particularly for younger L2 writers, the role of other higher-order cognitive factors that are rarely examined in the field of SLA, such as perspective taking and inferencing (Kendeou et al., Reference Kendeou, Bohn-Gettler, White and den Broek2008), should also be considered. These higher-order factors should be given more consideration, especially in bi- and multilingual contexts where children might be developing writing skills in an L2 without being fully literate in their L1. Similarly, the role of oral language abilities in L2 writing in multilingual contexts should be acknowledged and attention should be paid to developing children’s and older learners’ oral discourse construction skills to support them becoming effective L2 writers.

The concept of LT-WM (cf. Ericsson & Kintsch, Reference Ericsson and Kintsch1995) has also been attributed an important role in Kellogg’s (Reference Kellogg2001) and McCutchen’s (Reference McCutchen2011) later work. Research findings suggest that if L1 writers have relevant domain-specific knowledge (e.g., genre-based knowledge, writing schema), they can efficiently activate it and keep it accessible for processing during writing. This has been found to decrease the limitations of short-term WM, and to allow writers not just to retell but also to transform knowledge as they write (Kellogg, Reference Kellogg2001). McCutchen (Reference McCutchen2011) also argues that to easily access relevant knowledge in LT-WM writers need to have developed efficient linguistic encoding and transcription processes, as well as rich linguistic and genre-based knowledge.

As the preceding review illustrates, Kellogg’s (Reference Kellogg, Levy and Ransdell1996) original model of the role of WM in writing processes has been substantially extended and refined in the field of L1 writing. Therefore, it is important to incorporate these new models into research on the role of cognitive factors in L2 writing. Other advances in L1 writing research on the role of WM also have high relevance for L2 writing research and pedagogy. One such area is the role of social regulation of cognition and the effect of cognitive dissonance (i.e., when one has to argue for a position with which they do not agree), which can be influential factors in argumentative essays, particularly in assessment contexts when students cannot choose what stance they take on a debated issue. For example, Martinie et al. (Reference Martinie, Olive and Milland2010) found that composing a cognitively dissonant text was more taxing on WM resources than writing from a standpoint with which the participants agreed. A further area that is important to investigate in research and consider in L2 writing pedagogy is the interaction of anxiety and WM and the combined role of these factors in influencing writing processes and text quality. This is so because anxiety is known to deplete WM resources by means of intrusive thoughts that are irrelevant to the task at hand (Derakshan & Eysenck, Reference Derakshan and Eysenck2010), and thus can exert a substantial effect on the quality of written output.

The role of WM in L2 writing

In her conceptual review paper, Kormos (Reference Kormos2012) argued that WM resources are involved in every stage of the L2 writing process given the effortful and conscious nature of writing processes in L2 and hypothesized that there is a strong link between individual writers’ cognitive abilities and the quality of the written product. At that point there was limited research evidence to support these hypotheses but a number of studies have been conducted since the publication of that paper. However, the overall results have been somewhat contradictory due to key methodological differences, such as sample characteristics, the writing task, and measures of WM and writing quality. In this section, I discuss the relevant literature on the role of WM in L2 spelling, accuracy, complexity, fluency, and overall writing quality. I also describe the intricate relationship between WM, writing processes, and L2 proficiency.

The role of aptitude in L2 writing

Aptitude components, such as rote learning, phonetic coding, and language analytic ability, can also influence L2 writing processes and the quality of the writing product. In an earlier discussion of the role of aptitude in L2 writing, Kormos (Reference Kormos2012) proposed a number of hypotheses about how various aptitude components might impact on L2 writing. She argued that high levels of aptitude support the translation stage of writing and revision processes (cf. Hayes’s [Reference Hayes2012] model). In particular, language analytic ability might assist in the syntactic and morphological encoding of the conceptual writing plan, which might be manifested in higher syntactic complexity and accuracy of the written product. Furthermore, L2 writers with high levels of metalinguistic awareness might be better at noticing and correcting their errors and might devote more attention to monitoring linguistic accuracy. Evidence for these assumptions was found in Kormos and Trebits’s (Reference Kormos and Trebits2012) study, which showed that grammatical sensitivity was positively related to clause length in a written cartoon description task, and in Kormos and Sáfár’s (Reference Kormos and Sáfár2008) study, in which metalinguistic awareness was associated with teacher ratings of L2 writing tasks. Kormos (Reference Kormos2012) also hypothesized that L2 learners with higher rote learning ability might have more vocabulary knowledge, which can lead to greater lexical variety and complexity in their written production. This hypothesis is partially supported by Yang et al.’s (Reference Yang, Sun, Chang and Li2019) and Mujtaba et al.’s (Reference Mujtaba, Kamyabi Gol and Parkash2021) findings that scores on a LLAMA E test (Meara, Reference Meara2005), which assesses associative learning ability combined with analytic learning ability (Granena, Reference Granena2013), are a significant predictor of overall writing quality. In Mujtaba et al.’s study, however, none of the aptitude components correlated with participants’ receptive or productive vocabulary size. In contrast, in a recent large-scale study conducted with bilingual children, Peng et al. (Reference Peng, Orosco, Wang, Swanson and Reed2021) found that scores on a test of morphological awareness and a productive vocabulary knowledge were related to linguistic quality, spelling and punctuation accuracy, and story composition scores in L2 narrative writing.

Another important aptitude component is phonetic coding ability, which can be seen to be very similar to the construct of phonological awareness in the field of cognitive psychology and L1 literacy research. Phonological awareness is a key ability underlying spelling as it assists individuals in converting phonemes into graphemes (Grigorenko, Reference Grigorenko2001). Many students with specific learning difficulties (SpLDs) experience difficulties with phonological awareness, which is also manifested in their spelling performance. Helland and Kaasa’s (Reference Helland and Kaasa2005) research in Norway, Palladino et al.’s (Reference Palladino, Cismondo, Ferrari, Ballagamba and Cornoldi2016), and Fazio et al.’s (Reference Fazio, Ferrari, Testa, Tamburrelli, Marra, Biancardi and Marzocchi2021) studies in Italy, Łockiewicz and Jaskulska’s (Reference Łockiewicz and Jaskulska2016) project in Poland, and van Setten et al.’s work (Reference van Setten, Tops, Hakvoort, van der Leij, Maurits and Maassen2017) in the Netherlands have demonstrated that L2 learners with an official dyslexia identification make significantly more spelling errors than their nondyslexic peers. Ndlovu and Geva (Reference Ndlovu, Geva, Kormos and Kontra2008) also found that bilingual children with reading difficulties have poorer spelling skills than typical readers. However, spelling difficulties might not always be observable in free writing as students might avoid words they do not know how to spell (Łockiewicz et al., Reference Łockiewicz, Jaskulska and Fawcett2019). Writing difficulties of L2 learners with SpLDs, who also often score lower on tests of language aptitude (Downey et al., Reference Downey, Snyder and Hill2000), are not constrained to spelling. Łockiewicz et al. (Reference Łockiewicz, Jaskulska and Fawcett2019) found that Polish dyslexic students made more grammar errors in their freewriting than their nondyslexic peers. Ndlovu and Geva (Reference Ndlovu, Geva, Kormos and Kontra2008) observed that both multi- and monolingual children with SpLDs in Canada had difficulty with spelling, punctuation, the monitoring of syntax, and the coherent and cohesive organization of their writing. The important role of phonological awareness has also been demonstrated by Peng et al.’s (Reference Peng, Orosco, Wang, Swanson and Reed2021) study, as phonological awareness was found to be a very strong predictor of linguistic quality, spelling and punctuation accuracy, and story composition scores.

The role of working memory and aptitude in writing to learn

Although the importance of the role of writers’ implicit understanding of the writing task and the implicit organization of ideas and information in semantic memory (Galbraith & Al Saadi, Reference Galbraith, Al-Saadi and Manchón2020) should not be underestimated, a majority of the writing processes of L2 users rely on conscious and effortful processes in a “problem space” (Byrnes & Manchón, Reference Byrnes, Manchón, Byrnes and Manchón2014). As originally observed by Cumming (Reference Cumming1990) and later elaborated by Williams (Reference Williams2012), the less time-constrained and more permanent nature of written modality and corrective feedback allows L2 users not only to focus on creating meaning and conveying their thoughts effectively but also to reflect on and potentially improve their L2 knowledge. Therefore, writing in an additional language offers not only opportunities for the development of writing skills but can also be an effective way to enhance L2 skills and consolidate existing and acquire novel L2 linguistic knowledge (Cumming, Reference Cumming1990; Manchón & Williams, Reference Manchón, Williams, Manchón and Matsuda2016).

In her 2012 paper, Kormos suggested that individual difference factors could potentially influence a number of cognitive processes that occur as L2 learners acquire novel L2 knowledge through writing. These cognitive processes include noticing gaps in one’s knowledge, testing hypotheses about L2, solving L2-related problems, and internalizing and consolidating L2 knowledge through writing. Since the publication of that paper, the language learning potential of writing has been investigated in much more detail and depth, and from different theoretical perspectives (e.g., Manchón, Reference Manchón2020). However, empirical studies on the role of cognitive individual differences in writing to learn L2 are still scarce and have mostly focused on the development that takes place in response to written corrective feedback (WCF) (e.g., Shintani & Ellis, Reference Shintani and Ellis2015).

From the perspective of Skill Acquisition Theory (DeKeyser, Reference DeKeyser, Van Patten and Williams2015), writing can be considered a meaningful and contextualized practice activity that can consolidate L2 knowledge and support the proceduralization of explicit L2 knowledge (e.g., Nitta & Baba, Reference Nitta, Baba, Byrnes and Manchón2014), particularly if task repetition is accompanied by WCF (Sánchéz et al., Reference Sánchez, Manchón, Gilabert and Manchón2020). Manchón and Williams (Reference Manchón, Williams, Manchón and Matsuda2016) also hypothesize that writing and WCF might contribute to the restructuring of L2 knowledge through the reanalysis of implicit knowledge and engagement in meta-linguistic reflection. In this sense, writing can be viewed as the kind of “pushed output” (Swain, Reference Swain and Hinkel2005) that drives L2 users to set writing goals that exceed their current linguistic repertoire, and thereby test hypotheses about language use and seek feedback on these hypotheses (Williams, Reference Williams2012). This is demonstrated by L2 writers’ production of language-related episodes in López-Serrano et al.’s (Reference López-Serrano, de Larios and Manchón2020) study, which found that even preintermediate L2 users activated various problem-solving strategies that could be characterized by increased depth of processing (cf. Leow, Reference Leow2015). L2 writers’ attentional processes can also be guided toward noticing gaps and inaccuracies in their written product through WCF, which can then lead to further processing (Bitchener, Reference Bitchener, Hyland and Hyland2019; Kang & Han, Reference Kang and Han2015, Reference Kang, Han, Manchón and Polio2022).

However, writing processes are taxing on WM and attentional resources and, as a result, the processing and storage limitations of WM might constrain opportunities for L2 development. As described, L2 learning during writing can be explicit and occur through meta-linguistic reflection, hypothesis testing, and conscious problem solving, and it can also be implicit and exemplar based. Based on aptitude-treatment interaction research (e.g., Granena & Yilmaz, Reference Granena and Yilmaz2018; Yilmaz & Granena, Reference Yilmaz and Granena2021) one would presume that efficient WM storage and processing capacity, high levels of rote learning and inductive learning ability, and grammatical sensitivity, which are assumed to be part of explicit cognitive aptitude, would assist L2 writers in the acquisition of new L2 knowledge using conscious, controlled and explicit learning mechanisms, such as problem solving, hypothesis testing, and meta-linguistic reflection. In contrast, implicit inductive learning abilities and implicit memory might influence the extent to which L2 writers are able exploit the learning potentials of writing using implicit learning mechanisms. Yilmaz and Granena’s (Reference Yilmaz and Granena2021) study on corrective feedback on oral task performance indicates that explicit aptitude components are associated with learning gains from explicit feedback, while learners with high implicit aptitude benefited more from implicit feedback. Examining L2 learning as it happens during writing is methodologically challenging because it requires the use of introspective methods or an experimental pre- and posttest design. Furthermore, implicit learning mechanisms are particularly difficult to observe. Studies examining the impact of individual differences also require a larger sample size to have sufficient statistical power. This may explain why only a handful of studies have examined the role of cognitive factors in learning through L2 writing and why evidence for the differential role of explicit and implicit aptitude in writing to learn is not yet available.

Existing studies to date have focused on the mediating role of cognitive individual differences in the effectiveness of WCF. In all these studies, WCF was explicit and participants were provided with either direct corrective feedback or direct corrective feedback accompanied by meta-linguistic explanations of erroneous target constructions. Research by Sheen (Reference Sheen2007), Stefanou and Révész (Reference Stefanou and Révész2015), Shintani and Ellis (Reference Shintani and Ellis2015), and Benson and DeKeyser (Reference Benson and DeKeyser2019) suggests that language analytic ability and grammatical sensitivity support learning from direct corrective feedback. Therefore, it seems that these explicit aptitude components can assist L2 learners to consciously reflect on their errors, engage in problem solving, and induce linguistic regularities in the absence of explicit explanations of sources of errors. Some studies indicate that metalinguistic explanations mitigate the impact of explicit aptitude components (e.g., Benson & DeKeyser, Reference Benson and DeKeyser2019; Stefanou & Révész, Reference Stefanou and Révész2015), whereas others have found that high language analytic ability and grammatical sensitivity are still an advantage when WCF includes metalinguistic explanation (Sheen, Reference Sheen2007; Shintani & Ellis, Reference Shintani and Ellis2015). In Sheen’s (Reference Sheen2007) study, metalinguistic explanations were provided in the participants’ L2 on a large variety of article-use errors, and one of the posttests included an error-correction task. Therefore, participants with high language analytic ability might have understood the explanations better and been more efficient in applying this newly acquired knowledge in posttests. Shintani and Ellis’ (Reference Shintani and Ellis2015) study examined learning gains on two different syntactic constructions (past hypothetical conditionals and indefinite articles) and gave the participants a handout explaining these constructions in their L1. Their research demonstrates that language analytic ability supports the understanding of metalinguistic explanations of more complex constructions of the past hypothetical but is not implicated in comprehending the description of indefinite article use, which is less complex. In their research, they also asked a group of participants to revise their essays after they received WCF with metalinguistic explanations. In this group, students with higher language analytic ability used the conditional construction, which was essential for conveying the intended message in the task more accurately. Shintani and Ellis (Reference Shintani and Ellis2015) argued that the greater depth of processing required in this condition was assisted by learners’ language analytic ability. These research findings suggest that explicit aptitude components play a differential role depending on the type of feedback and the kind of metalinguistic explanation provided on L2 writing. Explicit aptitude components may also be differentially supportive of further L2 development depending on how learners are asked to apply feedback.

The role of WM in processing WCF has so far been examined in only one study by Li and Roshan (Reference Li and Roshan2019). Research on the impact of WM resources is important because while processing WCF, L2 writers need to process and actively maintain feedback in their WM concurrently with retrieving their existing knowledge of the construction on which the feedback was given and apply the feedback to refine or construct novel knowledge representations (Nowbakht & Olive, Reference Nowbakht and Olive2021). Li and Roshan (Reference Li and Roshan2019) hypothesized that phonological short-term memory (PSTM) that temporarily stores verbal information may support item-based learning through direct corrective feedback, whereas complex WM, measured with a reading span test, may assist L2 learners in processing metalinguistic information with no corrective feedback. In their study students revised their writing after receiving feedback on their use of the passive construction. Their hypotheses about the differential role of PSTM and the processing and storage capacity of WM, which they call complex WM, in different conditions were only partially confirmed. Score on the complex WM test was associated with accuracy gains through metalinguistic explanation, but the capacity of PSTM was not a predictor of improvement in the use of the target passive construction in the direct feedback condition. When students were asked to revise their essays after receiving direct feedback, complex WM scores predicted improved accuracy in the use of the passive construction, but when revision followed metalinguistic explanation, complex WM scores had no relationship with gains in the accurate use of the target structure. Interestingly, participants with high PSTM scores performed worse in the direct corrective feedback with revision condition than those with low PSTM. The findings regarding PSTM need to be interpreted with caution as PSTM was assessed in the participants’ L2 and hence it might have been influenced by their proficiency. However, Li and Roshan (Reference Li and Roshan2019) offer a feasible explanation for their finding when they argue that participants with high PSTM might have relied on the memorization of instances of corrected structures without engaging in an analysis of their errors, which then resulted in more erroneous performance in the posttest. In this study, two conditions were particularly taxing for WM resources: (a) when direct correction was followed by revision of the originally written text and L2 learners had to maintain the content and linguistic constructions of their previous text as well as corrected forms active in their WM, and (b) when L2 learners received metalinguistic explanation without WCF and revision. In the latter case, high WM learners might have been supported in storing the rule explanation in their WM and applying it in their next piece of writing. Li and Roshan explain that metalinguistic explanation with revision diminished the effect of WM as the revision served as a production practice activity. These findings suggest that the processing and storage components of WM might play a differential role in the uptake of WCF under different contexts.