The role of word frequency and morpho-orthography in agreement processing

Brehm, Laurel; Hussey, Erika; Christianson, Kiel

doi:10.1080/23273798.2019.1631456

Cited by 4 publications

(7 citation statements)

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“…Prior work has shown R, D, P, and M traits improve learnability and usability, and examples of experimental evidence supporting (1) statistical and relational learning of language and/or (2) processing and production in use can be found for redundancy (e.g., Wang and Mintz, 2018;Bahrick et al, 2019;Brehm et al, 2020;Lany and Shoaib, 2020;Tal and Arnon, 2022), degeneracy (e.g., Vulchanova et al, 2015Vulchanova et al, , 2019Gentner and Asmuth, 2017;Thibodeau et al, 2019;Starr et al, 2021), pluripotentiality (e.g., Brocher et al, 2018;Srinivasan et al, 2019;Floyd and Goldberg, 2021), and modularity (e.g., Boers and Lindstromberg, 2012;Conklin and Schmitt, 2012;Christiansen and Arnon, 2017). However, since an individual construction can have multiple overlapping R, D, P, and/or M characteristics, meaning those traits amenable to learning and use are also overlapping, it might be difficult to separate out individually the effects of any particular trait of a certain chunk.…”

Section: The Role Of Chunks In Foreign Language Teachingmentioning

confidence: 99%

Identification of commonalities across different languages

Green

2023

Front. Lang. Sci.

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This article fulfills the need for quantifiable, physical, common characteristics across different languages, which is needed to support the theory that humans use domain-general neurocognitive machinery to acquire, process, and produce language. It is shown that four different languages—English, German, Slovak and Japanese—contain linguistic chunks characterized by at least one redundancy, degeneracy, pluripotentiality, or modularity (R, D, P, or M, respectively) trait, following precedent from other fields of signal investigation. It is first established that language can be regarded as a set of signals between individuals within a complex adaptive system framework and that all signals in all signaling systems exhibit R, D, P, and/or M traits. It is then shown that linguistic chunks can be regarded as individual signals and that the chunks examined from the aforementioned languages express at least one R, D, P, and/or M trait. The present contribution thereby indicates the potential provision of a new source of data for quantifying some of the pressures involved in language production and processing, and the work concludes by assessing the value of the present work for further investigation in related fields.

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Section: The Role Of Chunks In Foreign Language Teachingmentioning

confidence: 99%

Identification of commonalities across different languages

Green

2023

Front. Lang. Sci.

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“…It also changes rates of plural agreement with conjoined noun phrases, which are inherently flexible in their verb agreement because the number of the phrase sometimes conflicts with a notional number (e.g., The name and address is/are ; Lorimor, Adams, & Middleton, 2018). Word frequency, an important factor in long‐term language experience, also impacts attraction such that high‐frequency local nouns do not cause agreement attraction in comprehension (correct and erroneous verbs are read equally fast no matter the local noun number; Brehm, Hussey, & Christianson, 2020). Base rates of structure frequencies also change what structure is comprehended, such that infrequent agreement patterns are sometimes initially misread (Keshev & Meltzer‐Asscher, 2021).…”

Section: Introductionmentioning

confidence: 99%

PIPS: A Parallel Planning Model of Sentence Production

et al. 2022

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Subject–verb agreement errors are common in sentence production. Many studies have used experimental paradigms targeting the production of subject–verb agreement from a sentence preamble (The key to the cabinets) and eliciting verb errors (… *were shiny). Through reanalysis of previous data (50 experiments; 102,369 observations), we show that this paradigm also results in many errors in preamble repetition, particularly of local noun number (The key to the *cabinet). We explore the mechanisms of both errors in parallelism in producing syntax (PIPS), a model in the Gradient Symbolic Computation framework. PIPS models sentence production using a continuous‐state stochastic dynamical system that optimizes grammatical constraints (shaped by previous experience) over vector representations of symbolic structures. At intermediate stages in the computation, grammatical constraints allow multiple competing parses to be partially activated, resulting in stable but transient conjunctive blend states. In the context of the preamble completion task, memory constraints reduce the strength of the target structure, allowing for co‐activation of non‐target parses where the local noun controls the verb (notional agreement and locally agreeing relative clauses) and non‐target parses that include structural constituents with contrasting number specifications (e.g., plural instead of singular local noun). Simulations of the preamble completion task reveal that these partially activated non‐target parses, as well the need to balance accurate encoding of lexical and syntactic aspects of the prompt, result in errors. In other words: Because sentence processing is embedded in a processor with finite memory and prior experience with production, interference from non‐target production plans causes errors.

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“…Particularly in the realm of L2 morphosyntactic processing inquiry, describing whether or not and to what extent L2 learners' or nonnative speakers' (NNSs) sensitivity to agreement violations during online comprehension differs from that of native speakers (NSs) has been in a long-standing debate in the field of L2 or bilingual 1 sentence processing (Bock & Cutting, 1992;Bock & Miller, 1991;Brehm et al, 2019;Cunnings, 2017;Foote, 2011;Keating, 2009Keating, , 2010Lim & Christianson, 2015;Reichle et al, 2016;Sagarra, 2021;Sagarra & Herschensohn, 2010Shibuya & Wakabayashi, 2008;Siriwittayakorn & Miyamoto, 2019;Wagers et al, 2009, among others). In recent years, a prolific body of research has been generated to examine the sources of difficulties and attempt to account for why establishing novel L2 linguistic representations and processing in L2 grammatical learning has been more effortful (Ellis, 2015).…”

Section: List Of Figuresmentioning

confidence: 99%

“…In recent years, a prolific body of research has been generated to examine the sources of difficulties and attempt to account for why establishing novel L2 linguistic representations and processing in L2 grammatical learning has been more effortful (Ellis, 2015). Empirical research findings to date have sufficiently provided convincing evidence that L2 morphosyntactic processing is influenced by both linguistic properties involved in the processing (Brehm et al, 2019;Keating, 2009Keating, , 2010Shibuya & Wakabayashi, 2008;Siriwittayakorn & Miyamoto, 2019) and that the L2 learners' individual differences in terms of L2 proficiency and cognitive resources necessary for morphosyntactic computations play a crucial role (e.g., Coughlin & Tremblay, 2013;Hopp, 2010Hopp, , 2017Kaan et al, 2015;Keating, 2010;Sagarra, 2021;Sagarra & Herschensohn, 2010Trenkic et al, 2014).…”

Section: List Of Figuresmentioning

confidence: 99%

“…The SPR task has been previously employed to investigate various linguistic phenomena in psycholinguistic studies (Brehm et al, 2019, In the present study, this online sentence processing data collection technique was selected to collect behavioral data on the participants' RTs as a dependent variable observable from the participants' sentence reading at their own pace. Details of the material manipulation and design, procedure, predictions, and scoring and analyses are described in 3.2.1.…”

Section: Experimental Study: Self-paced Reading Taskmentioning

confidence: 99%

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Effects of working memory on structural competition in processing English present tense morphology by L1 Thai learners

Rattanasak

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In the course of second language (L2) acquisition, L2 grammatical processing is often found to be dissimilar to that of native speakers, and nonnative processing difficulties have been attributed to various sources. In the realms of agreement processing, whether both first language (L1) and L2 linguistic systems are co-activated simultaneously within a bilingual mind during grammatical processing remains an active question in language sciences. This study examined individual differences in terms of working memory capacity and distance-based complexity in L2 agreement processing. In two word-by-word self-paced reading experiments, a total of 80 agreement-lacking Thai learners of L2 English and 80 native speakers of English read sentences involving English subject-verb agreement dependencies in two distance conditions. Distance was manipulated based on the dependency locality theory (Gibson, 1998, 2000), using semantically reversible English relative clauses (e.g., *The guy [that _ knows the driver/that the driver knows _] want to buy a new car). Two explanatory variables were crossed in a 2 x 2 design: distance (short-distance subject-extracted relative clause (SRC) vs. long-distance object-extracted relative clause (ORC)) and grammaticality of the subject-verb agreement (grammatical vs. ungrammatical). Stimuli in Experiment 1 involved singular subjects while those in Experiment 2 contained plural subjects; each consisted of 20 sentences, half grammatical and half ungrammatical, along with 40 distractors. The self-paced reading experiments were designed using the E-Prime 3.0 software package. A complex reading span task was used as a measure of working memory capacity. LexTALE scores showed L2 English proficiency to be upper-intermediate. In Experiment 1, linear mixed-effects modeling revealed that the native speakers and L2 learners were sensitive to agreement violation in both short-distance SRC ungrammatical and long-distance ORC ungrammatical conditions, shown by reading slowdowns. Their ability to show and maintain their sensitivity was, however, modulated as a function of working memory capacity and distance-based complexity. In Experiment 2, linear mixed-effects modeling showed that unlike the native speakers, who were able to show and maintain their sensitivity in both distance conditions, higher-span L2 learners showed longer reading times which were observed only in the short-distance SRC ungrammatical condition. These findings indicated that nonnative sensitivity to L2 agreement violation tended to be modulated by individual differences in terms of working memory capacity and distance-based linguistic complexity, particularly in agreement processing that involved multiple unique-to-L2 features, i.e., plural and third-person singular morphology, in long-distance agreement dependencies. To account for L2 learners' agreement processing difficulties, the findings suggested that L2 learners may labor under parallel activation, whereby working memory capacity is insufficient to resolve long-distance agreement dependencies, where the linguistic environment was more complex, thus resulting in reduced sensitivity to L2 agreement violation. The research findings were congruent with the L1-L2 structural competition account (Austin et al., 2015; Trenkic et al., 2014; Trenkic & Pongpairoj, 2013).

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The role of word frequency and morpho-orthography in agreement processing

Cited by 4 publications

References 74 publications

Identification of commonalities across different languages

Identification of commonalities across different languages

PIPS: A Parallel Planning Model of Sentence Production

Effects of working memory on structural competition in processing English present tense morphology by L1 Thai learners

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