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8 results for “first language”
Figure 4. The mental spaces set up by the first interpretation of the sentence in the film, John is riding a unicorn-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>First interpretation: The reality space (let’s call it R) contains an element a associated with<br> the proper name John. The noun phrase a unicorn introduces an element b ׳ to the film space<br> (call it F). I is the connector linking a in the space B to a ׳ in the space F (Figure 4). Since the<br> elements of both mental spaces are co-referential, this connector is an identity connector.<br> The rectangles represent the internal structure of the spaces next to them. The dashed line<br> indicates that the space F is set up in relation to R and that it is subordinate to R in<br> discourse.</p>
Code for the paper "First-Class Data Types in Shallow Embedded Domain-Specific Languages using Metaprogramming": IFL 2022
<p>Code accompanying the paper First-Class Data Types in Shallow Embedded Domain-Specific Languages using Metaprogramming.</p> <p>All code is Haskell code, tested with GHC 8.8.4</p> <ul> <li>./tuple: contains the examples from Section 3.2</li> <li>./bin: contains the examples from Section 5.1</li> <li>./artifact: contains an extended version of the running example of the paper<br> </li> </ul>
Early, typical, and late talkers: an exploratory study on predictors of language development in the first two years of life
<p>Background: The consensus in scientific literature is that each child undergoes a unique linguistic development path, albeit with shared developmental stages. Some children excel or lag behind their peers in language skills. Consequently, a key challenge in language acquisition research is pinpointing factors influencing individual differences in language development.</p> <p>Methods: We observed children longitudinally from 3 to 24 months of life to explore early predictors of vocabulary size. Based on the productive vocabulary size of children at 24 months, 30 children met our sample selection criteria: 10 late talkers and 10 early talkers, and we compared them with 10 typical talkers. We evaluated interactive behaviors at 3, 6, 9 and 12 months, considering vocal production, gaze at mother's face, and gestural production during mother-child interactions, and we considered mothers' report of children's actions and gestures and receptive-vocabulary size at 15 and 18 months.</p> <p>Results: Results indicated early precursors of language outcome at 24 months identifiable as early as 3 months in vocal productions, 6 months for gaze at mother's face and 12 months for gestural productions.</p> <p>Conclusions: Our research highlights both theoretical and practical implications. Theoretically, identifying the early indicators of belonging to the group of late or early talkers underscores the significant role of this developmental period for future studies. On a practical note, our findings emphasize the crucial need for early investigations to identify predictors of vocabulary development before the typical age at which lexical delay is identified.</p>
Early, typical, and late talkers: an exploratory study on predictors of language development in the first two years of life
Open the record for dataset details and reuse information.
Comparing Action-Oriented Language in the Assessment of EFL Writing: An Action Research for Combining the First-Year Instruction of the National Curriculum and the International Baccalaureate (IB) Diploma Program in a Finnish High School
<p>The data here were used to complete my master's thesis. Data have been anonymized and can thus be used freely. The thesis can be downloaded from the following URL:</p> <p>https://helda.helsinki.fi/handle/10138/327351</p>
Baseline EEG in the first year of life: insights into the development of autism spectrum disorder and language impairments
<p>The dataset includes power values obtained from baseline EEG recorded in infants. Specifically, for each infant the Power Spectral Density (PSD) was estimated for epochs of 30 s (at least 2 epochs) using the Welch’s method (Hamming window of 10s with 50% overlap) and successively log transformed as follow: log PSD = log10(1+PSD). The power in the following bands was computed: delta (2-4 Hz), theta (4-6 Hz), low-alpha (6-9-Hz), high-alpha (9-13 Hz), beta (13-30 Hz) and gamma (30-45 Hz). Two clusters of 6 frontal electrodes were selected (frontal left cluster and frontal right cluster) and power values were then averaged across electrodes within each cluster. Thus, the database contains for each subjects and frequency band two values of power one related to the frontal left cluster and the other one related to frontal right cluster.</p> <p>Each raw in the database corresponds to a subject and each column to a different variable.</p> <p>Column A, Subject code;</p> <p>Column B, Group: 0 = typically developing (TD) infant; 1 = infant higher likelihood of developing language and learning impairments (HL-LLI); 2 = infant at higher likelihood of developing autism spectrum disorder (HL-ASD);</p> <p>Column C, Diagnosis_group: 0 = children from the original TD group (without familiarity for neither ASD nor LLI) who at 3 years of age scored above the 30th percentile in the parent-administered Language Development Survey (LDS) (Rescorla and Alley, 2001); 1 = children diagnosed with Developmental Language Disorders/Developmental Dyslexia (DLD/DD); 2 = children diagnosed with ASD;</p> <p>Column D, Time_point: 1 = data related to the EEG recording performed at six months of age; 2 = data related to the EEG recording performed at twelve months of age;</p> <p>Columns from E to P: power values obtained in the frontal left (F_sx) and right (F_dx) clusters for each frequency band.</p> <p>All details about subjects, groups, data acquisition and preprocessing are available in the following manuscript: Piazza, C., Dondena, C., Riboldi, E. M., Riva, V., & Cantiani, C. (2023). Baseline EEG in the first year of life: Preliminary insights into the development of autism spectrum disorder and language impairments. <em>iScience</em>, <em>26</em>(7).</p> <p>Matlab .m files related to the preprocessing steps and the power value extraction are also available.</p>
Differences in Brain Processing of First and Second Languages (Korean and English)
ClinicalTrials.gov study NCT00016510. IPD Sharing: Not stated. Countries: 1. Publications: 3.
First, do no harm? Utilitarian choices in COVID-19 and non-COVID moral dilemmas depend on the language we use and type of dilemma, but not on the digital device we use
<p>Data set</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.