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1. Age of sign language acquisition has lifelong effect on syntactic preferences in sign language users

   https://doi.org/10.1177/0165025420958193  

   Krebs, Julia; Roehm, Dietmar; Wilbur, Ronnie B.; Malaia, Evie A. (September 2020, International Journal of Behavioral Development)

   null (Ed.)

   Acquisition of natural language has been shown to fundamentally impact both one’s ability to use the first language and the ability to learn subsequent languages later in life. Sign languages offer a unique perspective on this issue because Deaf signers receive access to signed input at varying ages. The majority acquires sign language in (early) childhood, but some learn sign language later—a situation that is drastically different from that of spoken language acquisition. To investigate the effect of age of sign language acquisition and its potential interplay with age in signers, we examined grammatical acceptability ratings and reaction time measures in a group of Deaf signers (age range = 28–58 years) with early (0–3 years) or later (4–7 years) acquisition of sign language in childhood. Behavioral responses to grammatical word order variations (subject–object–verb [SOV] vs. object–subject–verb [OSV]) were examined in sentences that included (1) simple sentences, (2) topicalized sentences, and (3) sentences involving manual classifier constructions, uniquely characteristic of sign languages. Overall, older participants responded more slowly. Age of acquisition had subtle effects on acceptability ratings, whereby the direction of the effect depended on the specific linguistic structure. 

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2. Neural Network Acceptability Judgments

   https://doi.org/10.1162/tacl_a_00290  

   Warstadt, Alex; Singh, Amanpreet; Bowman, Samuel R. (March 2019, Transactions of the Association for Computational Linguistics)

   This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.’s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions. 

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3. Investigating representations of verb bias in neural language models

   https://doi.org/10.18653/v1/2020.emnlp-main.376  

   Hawkins, Robert D; Yamakoshi, Takateru; Griffiths, Thomas; Goldberg, Adele (January 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   null (Ed.)

   Languages typically provide more than one grammatical construction to express certain types of messages. A speaker’s choice of construction is known to depend on multiple factors, including the choice of main verb – a phenomenon known as verb bias. Here we introduce DAIS, a large benchmark dataset containing 50K human judgments for 5K distinct sentence pairs in the English dative alternation. This dataset includes 200 unique verbs and systematically varies the definiteness and length of arguments. We use this dataset, as well as an existing corpus of naturally occurring data, to evaluate how well recent neural language models capture human preferences. Results show that larger models perform better than smaller models, and transformer architectures (e.g. GPT-2) tend to out-perform recurrent architectures (e.g. LSTMs) even under comparable parameter and training settings. Additional analyses of internal feature representations suggest that transformers may better integrate specific lexical information with grammatical constructions. 

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4. Language at a glance: How our brains grasp linguistic structure from parallel visual input

   https://doi.org/10.1126/sciadv.adr9951  

   Fallon, Jacqueline; Pylkkänen, Liina (October 2024, Science Advances)

   Human brains grasp the gists of visual scenes from a single glance, but to what extent is this possible for language? While we typically think of language in terms of sequential speech, our everyday experience involves numerous rapidly flashing written notifications, which we understand instantly. What do our brains detect in the first few hundred milliseconds after seeing such a stimulus? We flashed short sentences during magnetoencephalography measurement, revealing sentence-sensitive neural activity in left temporal cortex within 130 milliseconds. These signals emerged for subject-verb-object sentences regardless of grammatical or semantic well-formedness, suggesting that at-a-glance language comprehension begins by detecting basic phrase structure, independent of meaning or other grammatical details. Our findings unveil one aspect of how our brains process information rapidly in today’s visually saturated world. 

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5. Understanding How Dialect Differences Shape How AAE-Speaking Children Process Sentences in Real-Time

   https://doi.org/10.1055/a-2626-3292  

   Byrd, A; Huang, YT; Edwards, J (July 2025, Seminars in Speech and Language)

   Abstract Dialect differences between African American English (AAE) and Mainstream American English (MAE) impact how children comprehend sentences. However, research on real-time sentence processing has the potential to reveal the underlying causes of these differences. This study used eye tracking, which measures how children interpret linguistic features as a sentence unfolds, and examined how AAE- and MAE-speaking children processed “was” and “were,” a morphology feature produced differently in MAE and AAE. Fifty-nine participants, ages 7;8 to 11;0 years, completed standardized measures of dialect density and receptive vocabulary. In the eye tracking task, participants heard sentences in MAE with either unambiguous (e.g., “Jeremiah”) or ambiguous (e.g., “Carolyn May”), subjects and eye movements were measured to singular (image of one person) or plural referents (image of two people). After the onset of the auxiliary verb, AAE-speaking children were sensitive to “was” and “were” when processing sentences but were less likely than MAE-speaking children to use “was” as a basis for updating initial predictions of plural referents. Among African American children, dialect density was predictive of sensitivity to “was” when processing sentences. Results suggest that linguistic mismatch impacts how contrastive verb morphology is used to update initial interpretations of MAE sentences. 

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