Contributions of declarative and procedural memory to accuracy and automatization during second language practice

Abstract

Extending previous research that has examined the relationship between long-term memory and second language (L2) development with a primary focus on accuracy in L2 outcomes, the current study explores the relationship between declarative and procedural memory and accuracy and automatization during L2 practice. Adult English native speakers had learned an artificial language over two weeks (Morgan-Short, Faretta-Stutenberg, Brill-Schuetz, Carpenter & Wong, 2014), producing four sessions of practice data that had not been analyzed previously. Mixed-effects models analyses revealed that declarative memory was positively related to accuracy during comprehension practice. No other relationships were evidenced for accuracy. For automatization, measured by the coefficient of variation (Segalowitz, 2010), the model revealed a positive relationship with procedural memory that became stronger over practice for learners with higher declarative memory but weaker for learners with lower declarative memory. These results provide further insight into the role that long-term memory plays during L2 development.

Processing Variability in Intentional and Incidental Word Learning

“…Second, use of a maximal random effects structure also runs a risk of losing power and hence committing Type II errors (false negatives) (Matuschek, Kliegl, Vasishth, Baayen, & Bates, 2017). Finally, from a more data-driven approach to modeling, individual differences in automatization found in previous studies (e.g., Pili-Moss et al, 2019; Suzuki, 2018) may not be observed in the data (i.e., random slopes for ot1 and ot2 may not be statistically significant).…”

“…Given the important role of processing automaticity in skill learning theory (see DeKeyser, 2015 for an overview), it is critical that this measure be valid and reliable to verify (or falsify) theoretical claims about learners' processing trajectory (e.g., learners become more automatic in processing as proficiency develops). Validating and understanding the properties of CV have become more urgent since researchers have started using CV to index lexical proficiency and measure learning gains in both vocabulary and grammar research (Elgort, 2011;Elgort & Warren, 2014;McManus & Marsden, 2019;Pili-Moss et al, 2019). CV is calculated from an individual's response time (RT) distribution in a behavioral task such as a lexical decision task.…”

“…If this finding can be replicated consistently, it challenges the fundamental assumption of a linear relationship between development and CV values. This challenge then has implications for researchers using CV to index learning (McManus & Marsden, 2019;Pili-Moss, Brill-Schuetz, Faretta-Stutenberg, & Morgan-Short, 2019) and lexical proficiency (Elgort, 2011;Elgort & Warren, 2014). For this reason, it is time researchers captured learners' development over a fuller learning trajectory in one single study.…”

Cognitive Predictors of Child Second Language Comprehension and Syntactic Learning

“…Adult studies investigating the relationship between behavioral measures of cognitive ability and L2 learning from a declarative/procedural model perspective have focused on a range of linguistic features. These have included sentence word order (e.g., Brill‐Shuetz & Morgan‐Short, 2014; Carpenter, 2008; Hamrick, 2015; Morgan‐Short, Faretta‐Stutenberg, Brill‐Schuetz, Carpenter, & Wong, 2014), derivational and inflectional morphology (e.g., Antoniou, Ettlinger, & Wong, 2016; Carpenter, 2008; Granena, 2013), and the accuracy and automatization of sentence comprehension (e.g., Pili‐Moss, Brill‐Schuetz, Faretta‐Stutenberg, & Morgan‐Short, 2020). Of particular relevance for the present investigation were studies that have employed Brocanto2 (Morgan‐Short, 2007; Morgan‐Short et al., 2014), a training paradigm adapted from Brocanto (Friederici, Steinhauer, & Pfeifer, 2002) in which participants incidentally learn novel syntactic patterns (e.g., word order, agreement morphology) via aural exposure to a miniature language with natural language characteristics in the context of a meaningful computer‐game environment.…”

“…However, Pili‐Moss et al. (2020), a study examining online sentence comprehension data collected but not analyzed in the Morgan‐Short et al. (2014) study, found that declarative learning ability consistently predicted sentence comprehension accuracy throughout exposure (72 blocks), and did not find that procedural learning ability predicted comprehension accuracy at later stages of exposure (although after extended practice higher procedural learning ability was associated with better comprehension automatization).…”