scholarly journals The Emergence of an Abstract Grammatical Category in Children’s Early Speech

2017 ◽  
Vol 28 (2) ◽  
pp. 181-192 ◽  
Author(s):  
Stephan C. Meylan ◽  
Michael C. Frank ◽  
Brandon C. Roy ◽  
Roger Levy

How do children begin to use language to say things they have never heard before? The origins of linguistic productivity have been a subject of heated debate: Whereas generativist accounts posit that children’s early language reflects the presence of syntactic abstractions, constructivist approaches instead emphasize gradual generalization derived from frequently heard forms. In the present research, we developed a Bayesian statistical model that measures the degree of abstraction implicit in children’s early use of the determiners “a” and “the.” Our work revealed that many previously used corpora are too small to allow researchers to judge between these theoretical positions. However, several data sets, including the Speechome corpus—a new ultra-dense data set for one child—showed evidence of low initial levels of productivity and higher levels later in development. These findings are consistent with the hypothesis that children lack rich grammatical knowledge at the outset of language learning but rapidly begin to generalize on the basis of structural regularities in their input.

2019 ◽  
Author(s):  
Roger Philip Levy ◽  
Stephan Meylan ◽  
Michael C. Frank ◽  
Brandon Roy

How do children begin to use language to say things they have never heard before? The origins of linguistic productivity have been a subject of heated debate: While generativist accounts posit that children’s early language reflects the presence of syntactic abstractions, constructivist approaches instead emphasize gradual generalization over frequently-heard forms. Here we develop a Bayesian statistical model that measures the degree of abstraction implicit in children’s early use of the determiners “a” and “the.” Our work reveals that many previously-used corpora are too small to adjudicate between these theoretical positions. Several datasets, including the Speechome Corpus—a new ultra-dense dataset for one child—show evidence of low initial levels of productivity and higher levels later in development, however. These findings are consistent with the hypothesis that children lack rich grammatical knowledge at the outset of language learning, but rapidly begin to generalize on the basis of structural regularities in their input.


2020 ◽  
Vol 32 (11) ◽  
pp. 2187-2211
Author(s):  
Yu Terada ◽  
Tomoyuki Obuchi ◽  
Takuya Isomura ◽  
Yoshiyuki Kabashima

Recent remarkable advances in experimental techniques have provided a background for inferring neuronal couplings from point process data that include a great number of neurons. Here, we propose a systematic procedure for pre- and postprocessing generic point process data in an objective manner to handle data in the framework of a binary simple statistical model, the Ising or generalized McCulloch–Pitts model. The procedure has two steps: (1) determining time bin size for transforming the point process data into discrete-time binary data and (2) screening relevant couplings from the estimated couplings. For the first step, we decide the optimal time bin size by introducing the null hypothesis that all neurons would fire independently, then choosing a time bin size so that the null hypothesis is rejected with the strict criteria. The likelihood associated with the null hypothesis is analytically evaluated and used for the rejection process. For the second postprocessing step, after a certain estimator of coupling is obtained based on the preprocessed data set (any estimator can be used with the proposed procedure), the estimate is compared with many other estimates derived from data sets obtained by randomizing the original data set in the time direction. We accept the original estimate as relevant only if its absolute value is sufficiently larger than those of randomized data sets. These manipulations suppress false positive couplings induced by statistical noise. We apply this inference procedure to spiking data from synthetic and in vitro neuronal networks. The results show that the proposed procedure identifies the presence or absence of synaptic couplings fairly well, including their signs, for the synthetic and experimental data. In particular, the results support that we can infer the physical connections of underlying systems in favorable situations, even when using a simple statistical model.


2021 ◽  
pp. 096372142110578
Author(s):  
George Kachergis ◽  
Virginia A. Marchman ◽  
Michael C. Frank

A standard model is a theoretical framework that synthesizes observables into a quantitative consensus. Have researchers made progress toward this kind of synthesis for children’s early language learning? Many computational models of early vocabulary learning assume that individual words are learned through an accumulation of environmental input. This assumption is also implicit in empirical work that emphasizes links between language input and learning outcomes. However, models have typically focused on average performance, whereas empirical work has focused on variability. To model individual variability, we relate the tradition of research on accumulator models to item response theory models from psychometrics. This formal connection reveals that currently available data sets do not allow researchers to test the resulting models fully, illustrating a critical need for theory to contribute to shaping new data collection and creating and testing an eventual standard model.


ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 32-32
Author(s):  
Heidi Hanks

Leave your flashcards at home and try these five apps for early language learning.


2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.


2018 ◽  
Vol 28 (7) ◽  
pp. 2319-2324
Author(s):  
Rina Muka ◽  
Irida Hoti

The language acquired from the childhood is the language spoken in the family and in the place of living. This language is different from one pupil to another, because of their social, economical conditions. By starting the school the pupil faces first the ABC book and then in the second grade Albanian language learning through the Albanian language textbook. By learning Albanian language step by step focused on Reading, Writing, Speaking and Grammar the pupil is able to start learning the second language on the next years of schooling. So, the second language learning in Albanian schools is related to the first language learning (mother tongue), since the early years in primary school. In our schools, the second language (English, Italian) starts in the third grade of the elementary class. On the third grade isn’t taught grammar but the pupil is directed toward the correct usage of the language. The textbooks are structured in developing the pupil’s critical thinking. The textbooks are fully illustrated and with attractive and educative lessons adequate to the age of the pupils. This comparative study will reflect some important aspects of language learning in Albanian schools (focused on Albanian language - first language and English language - second language), grade 3-6. Our point of view in this paper will show not only the diversity of the themes, the lines and the sub-lines but also the level of language knowledge acquired at each level of education. First, the study will focus on some important issues in comparing Albanian and English language texts as well as those which make them different: chronology and topics retaken from one level of education to another, so by conception of linear and chronological order will be shown comparatively two learned languages (mother tongue and second language). By knowing and learning well mother tongue will be easier for the pupil the foreign language learning. The foreign language (as a learning curriculum) aims to provide students with the skills of using foreign language written and spoken to enable the literature to recognize the achievements of advanced world science and technology that are in the interest of developing our technique. Secondly, the study will be based on the extent of grammatical knowledge, their integration with 'Listening, Reading, Speaking and Writing' as well as the inclusion of language games and their role in language learning. The first and second language learning in Albanian schools (grade III-VI) is based on similar principles for the linearity and chronology of grammatical knowledge integrated with listening, reading, writing and speaking. The different structure of both books help the pupils integrate and use correctly both languages. In the end of the sixth grade, the pupils have good knowledge of mother tongue and the second language and are able to write and speak well both languages.


2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.


Author(s):  
Kyungkoo Jun

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.


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