Individual Differences in First and Second Language Ultimate Attainment and Their Causes

Andringa, Sible; Dąbrowska, Ewa

doi:10.1111/lang.12328

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…Moreover, in conducting processing experiments, it is important to keep in mind that there might be huge differences between the members of a given language community. Some of this variance will be random noise, but some of it will be systematic (i.e., related to individual variables like age, idiosyncratic differences in working memory and executive functions, differential language exposure, multilingualism) (Kidd et al, 2018;Andringa and Dąbrowska, 2019;Dąbrowska, 2019). Likewise, it is important to acknowledge that the processing strategies adopted by individuals might vary as a function of task, interlocutor, and communicative situation, among other things (McConnell and Blumenthal-Dramé, 2019).…”

Section: Complexity Metrics and Cognitive Researchmentioning

confidence: 99%

Meaning and Measures: Interpreting and Evaluating Complexity Metrics

et al. 2021

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Research on language complexity has been abundant and manifold in the past two decades. Within typology, it has to a very large extent been motivated by the question of whether all languages are equally complex, and if not, which language-external factors affect the distribution of complexity across languages. To address this and other questions, a plethora of different metrics and approaches has been put forward to measure the complexity of languages and language varieties. Against this backdrop we address three major gaps in the literature by discussing statistical, theoretical, and methodological problems related to the interpretation of complexity measures. First, we explore core statistical concepts to assess the meaningfulness of measured differences and distributions in complexity based on two case studies. In other words, we assess whether observed measurements are neither random nor negligible. Second, we discuss the common mismatch between measures and their intended meaning, namely, the fact that absolute complexity measures are often used to address hypotheses on relative complexity. Third, in the absence of a gold standard for complexity metrics, we suggest that existing measures be evaluated by drawing on cognitive methods and relating them to real-world cognitive phenomena. We conclude by highlighting the theoretical and methodological implications for future complexity research.

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Section: Complexity Metrics and Cognitive Researchmentioning

confidence: 99%

Meaning and Measures: Interpreting and Evaluating Complexity Metrics

et al. 2021

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“…An extensive body of evidence exists on the important role of personal characteristics, referred to as IDs, in L2 classroom learning and achievement (e.g., Adringa & Dabrowska, 2019;Carroll, 1989;Dörnyei & Ryan, 2015). Individual traits such as personality, motivation, language aptitude, and learning strategies and styles have been explored with the goal of identifying the prominent factors that are likely to modulate L2 skill development-thus modifying and personalizing the overall trajectory of the language acquisition processes.…”

Section: Theoretical Framework 21 Individual Differences and Adult Le...mentioning

confidence: 99%

“…IDs, now seen as dynamic and evolving parameters, affect language development and explain the variation in overall language proficiency. This is particularly true in adult learners who generally develop dynamic repertoires of resources from their life-world experiences (The Douglas Fir Group, 2016), and hence varied learning trajectories over time (Adringa & Dabrowska, 2019).…”

Section: Theoretical Framework 21 Individual Differences and Adult Le...mentioning

confidence: 99%

Accounting for Individual Trajectories in L2 Learning: A One‑Year Pilot Case Study with Adult Professionals

Zoghlami¹,

Grosbois²

2022

lidil

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“…However, usage-based approaches posit that human skills are highly plastic and shaped by experience (Evans and Levinson, 2009;Dąbrowska, 2015;Dąbrowska, 2018). Recent research has highlighted that language attainment within adult native speakers is modulated by both endogenous constraints (e.g., executive functions, statistical learning abilities, personality traits) and exogenous, experience-related variables (notably, the quality and quantity of the input) (Andringa and Dąbrowska, 2019;Dąbrowska, 2019;Frost et al, 2019;Medimorec et al, 2019;Divjak and Milin, 2020;Kidd and Donnelly, 2020;Ryskin et al, 2020;Kerz and Wiechmann, 2021).…”

Section: Experience-based Individual Differencesmentioning

confidence: 99%

Usage-Based Individual Differences in the Probabilistic Processing of Multi-Word Sequences

McConnell

Blumenthal-Dramé

2021

Front. Commun.

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While it is widely acknowledged that both predictive expectations and retrodictive integration influence language processing, the individual differences that affect these two processes and the best metrics for observing them have yet to be fully described. The present study aims to contribute to the debate by investigating the extent to which experienced-based variables modulate the processing of word pairs (bigrams). Specifically, we investigate how age and reading experience correlate with lexical anticipation and integration, and how this effect can be captured by the metrics of forward and backward transition probability (TP). Participants read more and less strongly associated bigrams, paired in sets of four to control for known lexical covariates such as bigram frequency and semantic meaning (i.e., absolute control, total control, absolute silence, total silence) in a self-paced reading (SPR) task. They additionally completed assessments of exposure to print text (Author Recognition Test, Shipley vocabulary assessment, Words that Go Together task) and provided their age. Results show that both older age and lesser reading experience individually correlate with stronger TP effects. Moreover, TP effects differ across the spillover region (the two words following the noun in the bigram).

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Individual Differences in First and Second Language Ultimate Attainment and Their Causes

Cited by 10 publications

References 29 publications

Meaning and Measures: Interpreting and Evaluating Complexity Metrics

Meaning and Measures: Interpreting and Evaluating Complexity Metrics

Accounting for Individual Trajectories in L2 Learning: A One‑Year Pilot Case Study with Adult Professionals

Usage-Based Individual Differences in the Probabilistic Processing of Multi-Word Sequences

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