Twenty-five Years Of Research On Foreign Language Aptitude -

Erlam (2005) found that learners with high grammatical sensitivity (a subcomponent of aptitude) performed better after explicit deductive instruction, whereas learners with high rote memory skills benefited equally from inductive instruction. More recently, Vatz et al. (2013) showed that high-analytic learners excel with explicit corrective feedback, while learners with strong phonetic coding ability benefit more from recasts.

Twenty-Five Years of Research on Foreign Language Aptitude: From Cognitive Measurement to Dynamic Systems

Granena (2013) demonstrated that traditional aptitude tests (MLAT) strongly predict explicit learning but weakly predict implicit learning. Conversely, implicit sequence learning ability (measured via reaction-time tasks) is dissociable from explicit aptitude. This finding has profound implications for age: younger learners, who rely more on implicit mechanisms, may show different aptitude profiles than older learners, who rely on explicit analysis. twenty-five years of research on foreign language aptitude

This research effectively expanded the aptitude construct. Aptitude was no longer just “learning ability” but included the online cognitive machinery necessary for real-time language processing. 3. Aptitude-Treatment Interactions (ATIs): Matching Learner to Method (2010–2018) If aptitude is multidimensional, then different learners should thrive under different instructional conditions. This led to a resurgence of Aptitude-Treatment Interaction (ATI) research. The classic hypothesis—that high-analytic learners benefit from explicit grammar instruction while high-memory learners benefit from immersion—was refined.

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Contrary to the Critical Period Hypothesis’s strong version, research shows that older learners often outperform younger learners in initial explicit learning due to superior working memory and inductive ability. However, high aptitude in younger learners may manifest as superior phonological attainment in the long term (DeKeyser, 2020). Aptitude is thus not a static trait but interacts developmentally with age and learning context. 5. The Dynamic Turn: Aptitude as a Complex System (2020–2024) The most radical shift of the last five years is the proposal that aptitude is not a fixed attribute but a dynamic, emergent property of the learner’s cognitive resources interacting with task demands, motivation, and anxiety.

The past twenty-five years have witnessed a remarkable renaissance. Researchers have moved beyond simple prediction to ask deeper questions: How does aptitude interact with instructional conditions? Is aptitude a unitary construct or a constellation of flexible resources? Can it be developed? This paper synthesizes the key empirical and theoretical contributions to FLA research from 1999 to 2024, organizing the literature into four thematic waves. The first major shift was the integration of working memory (WM) into the aptitude framework. While traditional aptitude tests emphasized crystallized knowledge and analytical reasoning, WM—the ability to simultaneously store and process information—offered a process-oriented explanation for individual differences. Erlam (2005) found that learners with high grammatical

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