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1. Enhanced pitch discrimination in autistic children with unexpected bilingualism

   Dumont, C; Belenger, M; Eigsti, I M; Kissine, M (September 2024, Autism research)

   Some autistic children acquire foreign languages from exposure to screens. Such Unexpected Bilingualism (UB) is therefore not driven by social interaction; rather, language acquisition appears to rely on less socially mediated learning and other cognitive processes. We hypothesize that UB children may rely on other cues, such as acoustic cues, of the linguistic input. Previous research indicates enhanced pitch processing in some autistic children, often associated with language delays and difficulties in forming stable phonological categories due to sensitivity to subtle linguistic variations. We propose that repetitive screen-based input simplifies linguistic complexity, allowing focus on individual cues. This study hypothesizes that autistic UB children exhibit superior pitch discrimination compared to both autistic and non-autistic peers. From a sample of 46 autistic French-speaking children aged 9 to 16, 12 were considered as UB. These children, along with 45 non-autistic children, participated in a two-alternative forced-choice pitch discrimination task. They listened to pairs of pure tones, 50% of which differed by 3% (easy), 2% (medium), or 1% (hard). A stringent comparison of performance revealed that only the autistic UB group performed above chance for tone pairs that differed, across all conditions. This group demonstrated superior pitch discrimination relative to autistic and non-autistic peers. This study establishes the phenomenon of UB in autism and provides evidence for enhanced pitch discrimination in this group. Acute perception of auditory information, combined with repeated language content, may facilitate UB children's focus on phonetic features, and help acquire a language with no communicative support or motivation. 

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2. Using language to discover categories: More nameable features improve category learning

   https://doi.org/10.12775/3991-1.136  

   Zettersten, M.; Lupyan, G. (January 2018, Proceedings of the 12th International Conference on the Evolution of Language (Evolang12))

   Does the lexicon of a language have consequences for cognition? Here, we provide evidence that the ease with which category features can be named can influence category learning. Across two experiments, participants learned to distinguish images composed of colors (Experiment 1) and shapes (Experiment 2) that were either easy or more difficult to name in English. Holding the category structure constant, when the underlying features of the category were easy to name, participants were faster and more accurate in learning the novel category. We argue that these findings suggest that labels allow learners to form more compact hypotheses, which in turn can be confirmed or disconfirmed in the course of learning. These results have consequences for considering how cross-linguistic differences in lexical inventory affect how readily novel categories are learned. 

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3. How Good Does This Sound? Examining Listeners’ Second Language Proficiency and Their Perception of Category Goodness in Their Native Language

   https://doi.org/10.3390/languages8010043  

   Nagle, Charlie; Baese-Berk, Melissa M.; Diantoro, Carissa; Kim, Haeun (March 2023, Languages)

   Language learners often transfer the sounds and prosody of their native language into their second language, but this influence can also flow in the opposite direction, with the second language influencing the first. Among other variables, language proficiency is known to affect the degree and directionality of cross-linguistic influence. However, little is known about how second language learning affects listeners’ perception of their native language. To begin addressing this gap, we examined the relationship between learners’ second language proficiency and their category goodness ratings in their native language. Thirty-nine English-speaking learners of Spanish listened to English words that began with voiced and voiceless stop consonants and were asked to rate how well the word represented the intended word on a 5-point scale. To create a voicing continuum, we manipulated the voice onset time of the word-initial stop in each target item from 125 ms of prevoicing to 100 ms of aspiration, in 25 ms steps. Proficiency did not affect the perception of voiced targets, but both proficiency and L2 stop production affected the perception of voiceless targets. 

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4. Investigating the Generalizability of Physiological Characteristics of Anxiety

   https://doi.org/10.1109/BIBM58861.2023.10385292  

   Zhou, Emily; Soleymani, Mohammad; Matarić, Maja J (December 2023, IEEE)

   Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. Specifically, we examine features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from the following three datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. We aim to understand whether these features are specific to anxiety or general to other high-arousal emotions through a statistical regression analysis, in addition to a within-corpus, cross-corpus, and leave-one-corpus-out cross-validation across instances of stress and arousal. We used the following classifiers: Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of the aforementioned models. We found that models trained on an arousal dataset perform relatively well on a previously unseen stress dataset, and vice versa. Our experimental results suggest that the evaluated models may be identifying emotional arousal instead of stress. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection. 

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5. Hao Fayin: Developing Automated Audio Assessment Tools for a Chinese Language Course

   Varatharaj, A.; Botelho, A. F.; Lu, X.; Heffernan, N. T. (July 2019, The Twelfth International Conference on Educational Data Mining)

   We present and evaluate a machine learning based system that automatically grades audios of students speaking a foreign language. The use of automated systems to aid the assessment of student performance holds great promise in augmenting the teacher’s ability to provide meaningful feedback and instruction to students. Teachers spend a significant amount of time grading student work and the use of these tools can save teachers a significant amount of time on their grading. This additional time could be used to give personalized attention to each student. Significant prior research has focused on the grading of closed-form problems, open-ended essays and textual content. However, little research has focused on audio content that is much more prevalent in the language-study education. In this paper, we explore the development of automated assessment tools for audio responses in a college-level Chinese language-learning course. We analyze several challenges faced while working with data of this type as well as the generation and extraction of features for the purpose of building machine learning models to aid in the assessment of student language learning. 

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