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1. English‐learning infants developing sensitivity to vowel phonotactic cues to word segmentation

   https://doi.org/10.1111/desc.13564  

   Katsuda, Hironori; Sundara, Megha (September 2024, Developmental Science)

   Abstract Previous research has shown that when domain‐general transitional probability (TP) cues to word segmentation are in conflict with language‐specific stress cues, English‐learning 5‐ and 7‐month‐olds rely on TP, whereas 9‐month‐olds rely on stress. In two artificial languages, we evaluated English‐learning infants’ sensitivity to TP cues to word segmentation vis‐a‐vis language‐specific vowel phonotactic (VP) cues—English words do not end in lax vowels. These cues were either consistent or conflicting. When these cues were in conflict, 10‐month‐olds relied on the VP cues, whereas 5‐month‐olds relied on TP. These findings align with statistical bootstrapping accounts, where infants initially use domain‐general distributional information for word segmentation, and subsequently discover language‐specific patterns based on segmented words. Research HighlightsResearch indicates that when transitional probability (TP) conflicts with stress cues for word segmentation, English‐learning 9‐month‐olds rely on stress, whereas younger infants rely on TP.In two artificial languages, we evaluated English‐learning infants’ sensitivity to TP versus vowel phonotactic (VP) cues for word segmentation.When these cues conflicted, 10‐month‐olds relied on VPs, whereas 5‐month‐olds relied on TP.These findings align with statistical bootstrapping accounts, where infants first utilize domain‐general distributional information for word segmentation, and then identify language‐specific patterns from segmented words. 

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2. Modeling ON Cone Bipolar Cells for Electrical Stimulation

   https://doi.org/10.1109/EMBC46164.2021.9629884  

   Paknahad, Javad; Kosta, Pragya; Iseri, Ege; Farzad, Shayan; Bouteiller, Jean-Marie C.; Humayun, Mark S.; Lazzi, Gianluca (November 2021, Modeling ON Cone Bipolar Cells for Electrical Stimulation)

   Retinal prosthetic systems have been developed to help blind patients suffering from retinal degenerative diseases gain some useful form of vision. Various experimental and computational studies have been performed to test electrical stimulation strategies that can improve the performance of these devices. Detailed computational models of retinal neurons, such as retinal ganglion cells (RGCs) and bipolar cells (BCs), allow us to explore the mechanisms underlying the response of cells to electrical stimulation. While electrophysiological studies have shown the presence of voltage-gated ionic channels in different regions of BCs, many of the existing cone BCs models are assumed to be passive or only contain calcium channels at the synaptic terminals. We have utilized our Admittance Method (AM)-NEURON computational platform to implement a more realistic model of ON-BCs. Our model closely replicates the recent patch-clamp experiments directly measuring the response of ON-BCs to epiretinal electrical stimulation and thereby predicts the regional distributions of the ionic channels. Our computational results further indicate that outward potassium current strongly contributes to the depolarizing voltage transient of ON-BCs in response to electrical stimulation. 

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3. The syntax and pragmatics of clause-internal wh-phrases in Valdôtain Patois

   https://doi.org/10.1075/lv.24061.lui  

   Luisa, Seguin (July 2025, Linguistic Variation)

   Abstract In Valdôtain Patois, an understudied Francoprovençal language spoken in Aosta Valley (Italy), wh-phrases can either be fronted or occur clause-internally. In this paper, I analyze the syntax and pragmatics of clause internal wh-phrases, showing that they require to be either activated in the preceding linguistic context or inferable. Based on evidence from word order and parasitic gaps, I argue that in Valdôtain Patois clause-internal wh-phrases are not in-situ, but move to an A′-position at the edge of vP. 

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4. 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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5. Seismic Tomographic Model of the Hawaiian Ridge Line 01

   https://doi.org/10.6084/m9.figshare.25097531  

   Dunn, Robert; MacGregor, Brandon; Watts, Anthony; Xu, Chong; Shillington, Donna J. (January 2024, figshare)

   Two-dimensional seismic Vp profile from MacGregor et al. (2023), including positions of the seafloor, the upper reflector, and the lower reflector along the profile. The Vp model is in netCDF-4 format and the others are in ascii format and contain the position along the line and depth below sea level. The origin of the profile is 20.49˚N, 155.8237˚W, and the azimuth of the profile is 46˚ from north.Reference: MacGregor, B. G., Dunn, R. A., Watts, A. B., Xu, C., & Shillington, D. J. (2023). A seismic tomography, gravity, and flexure study of the crust and upper mantle structure of the Hawaiian Ridge: 1. Journal of Geophysical Research: Solid Earth, 128, e2023JB027218. https://doi. org/10.1029/2023JB027218 

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