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1. The dynamics of bilingualism in language shift ecologies

   https://doi.org/10.1075/lab.22035.gre  

   Grenoble, Lenore A; Osipov, Boris (February 2023, Linguistic Approaches to Bilingualism)

   Abstract A large percentage of the world’s languages – anywhere from 50 to 90% – are currently spoken in what we call shift ecologies, situations of unstable bi- or multilingualism where speakers, and in particular younger speakers, do not use their ancestral language but rather speak the majority language. The present paper addresses several interrelated questions with regard to the linguistic effects of bilingualism in such shift ecologies. These language ecologies are dynamic: language choices and preferences change, as do speakers’ proficiency levels. One result is multiple kinds of variation in these endangered language communities. Understanding change and shift requires a methodology for establishing a baseline; descriptive grammars rarely provide information about usage and multilingual language practices. An additional confounder is a range of linguistic variation: regional (dialectal); generational (language-internal change without contact or shift); contact-based (contact with or without shift); and proficiency-based (variation which develops as a result of differing levels of input and usage). Widespread, ongoing language shift today provides opportunities to examine the linguistic changes exhibited by shifting speakers, that is, to zero in on language change and loss in process, rather than as an end product. 

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2. A one-man bilingual cocktail party: linguistic and non-linguistic effects on bilinguals’ speech recognition in Mandarin and English

   https://doi.org/10.1186/s41235-024-00562-w  

   Smith, Erin_D; Holt, Lori_L; Dick, Frederic (June 2024, Cognitive Research: Principles and Implications)

   Abstract Multilingual speakers can find speech recognition in everyday environments like restaurants and open-plan offices particularly challenging. In a world where speaking multiple languages is increasingly common, effective clinical and educational interventions will require a better understanding of how factors like multilingual contexts and listeners’ language proficiency interact with adverse listening environments. For example, word and phrase recognition is facilitated when competing voices speak different languages. Is this due to a “release from masking” from lower-level acoustic differences between languages and talkers, or higher-level cognitive and linguistic factors? To address this question, we created a “one-man bilingual cocktail party” selective attention task using English and Mandarin speech from one bilingual talker to reduce low-level acoustic cues. In Experiment 1, 58 listeners more accurately recognized English targets when distracting speech was Mandarin compared to English. Bilingual Mandarin–English listeners experienced significantly more interference and intrusions from the Mandarin distractor than did English listeners, exacerbated by challenging target-to-masker ratios. In Experiment 2, 29 Mandarin–English bilingual listeners exhibited linguistic release from masking in both languages. Bilinguals experienced greater release from masking when attending to English, confirming an influence of linguistic knowledge on the “cocktail party” paradigm that is separate from primarily energetic masking effects. Effects of higher-order language processing and expertise emerge only in the most demanding target-to-masker contexts. The “one-man bilingual cocktail party” establishes a useful tool for future investigations and characterization of communication challenges in the large and growing worldwide community of Mandarin–English bilinguals. 

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3. Using Machine Learning Systems to Investigate Phonological Representations

   Kostyszyn, Kalina (August 2024, ERIC)

   Language learning is a complex issue of interest to linguists, computer scientists, and psychologists alike. While the different fields approach these questions at different levels of granularity, findings in one field profoundly affect how the others proceed. My dissertation examines the perceptual and linguistic generalizations regarding the units that make up words (phonemes, morphemes, and vocal quality) in Polish and English to better understand how both humans and computers formulate these concepts in language. I use computational modeling and machine learning to investigate Polish morphophonology in two ways. First, I examine consonant clusters at the beginning of Polish words to see what parameters determine human-like learnability, compared to a survey of native speakers. I run several studies to compare learning with gradient or categorical data, each at the cluster, bigram, and featural level. Second, I examine Polish yer alternation and study whether machine learning approaches can generalize morphophonological information to target this pattern when given a larger Polish. Using low level neural networks and a classification-and-regression tree (CART) decision algorithm, I examine how well they use morphological and phonological information to make generalizations that capture a small subset of the Polish vocabulary. Additionally, I conduct a psycholinguistic experiment with English speakers to further establish what level of attention listeners may give when building phonological representations. I test this by extending a previous study finding that real word primes make rejection of nonword primes more difficult, determining that the effect generalizes across speakers. This research addresses a tension in modeling the computational problem of language learning between the formalization of representation and the mechanics of the learning apparatus. Different levels of abstraction can give more sophisticated insight into the data at hand, but at a cost that may not be representative of human learning. I argue that computational linguistic questions such as these provide an interesting window into the strengths and limitations of machine learning questions as compared to the human language learning faculty. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] ERIC # ED663172 

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4. A quantitative model of the language familiarity effect in infancy

   Thorburn, Craig A.; Feldman, Naomi H.; Schatz, Thomas (September 2019, Proceedings of the Conference on Cognitive Computational Neuroscience)

   Human listeners are better at telling apart speakers of their native language than speakers of other languages, a phenomenon known as the language familiarity effect. The recent observation of such an effect in infants as young as 4.5 months of age (Fecher & Johnson, in press) has led to new difficulties for theories of the effect. On the one hand, retaining classical accounts—which rely on sophisticated knowledge of the native language (Goggin, Thompson, Strube, & Simental, 1991)–requires an explanation of how infants could acquire this knowledge so early. On the other hand, letting go of these accounts requires an explanation of how the effect could arise in the absence of such knowledge. In this paper, we build on algorithms from unsupervised machine learning and zero-resource speech technology to propose, for the first time, a feasible acquisition mechanism for the language familiarity effect in infants. Our results show how, without relying on sophisticated linguistic knowledge, infants could develop a language familiarity effect through statistical modeling at multiple time-scales of the acoustics of the speech signal to which they are exposed. 

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5. The State of Cognitive Control in Language Processing

   https://doi.org/10.1177/17456916231197122  

   Ness, Tal; Langlois, Valerie_J; Kim, Albert_E; Novick, Jared_M (October 2023, Perspectives on Psychological Science)

   Understanding language requires readers and listeners to cull meaning from fast-unfolding messages that often contain conflicting cues pointing to incompatible ways of interpreting the input (e.g., “The cat was chased by the mouse”). This article reviews mounting evidence from multiple methods demonstrating that cognitive control plays an essential role in resolving conflict during language comprehension. How does cognitive control accomplish this task? Psycholinguistic proposals have conspicuously failed to address this question. We introduce an account in which cognitive control aids language processing when cues conflict by sending top-down biasing signals that strengthen the interpretation supported by the most reliable evidence available. We also provide a computationally plausible model that solves the critical problem of how cognitive control “knows” which way to direct its biasing signal by allowing linguistic knowledge itself to issue crucial guidance. Such a mental architecture can explain a range of experimental findings, including how moment-to-moment shifts in cognitive-control state—its level of activity within a person—directly impact how quickly and successfully language comprehension is achieved. 

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