Individual differences at 20 months: analytic and holistic strategies in language acquisition

1983 ◽  
Vol 10 (2) ◽  
pp. 293-320 ◽  
Author(s):  
Inge Bretherton ◽  
Sandra McNew ◽  
Lynn Snyder ◽  
Elizabeth Bates
Keyword(s):  
Individual Differences ◽  
Language Acquisition ◽  
Holistic Processing ◽  
Data Sets ◽  
Partial Support ◽  
Language Abilities ◽  
Processing Strategies ◽  
Acquisition Strategies ◽  
Highly Correlated ◽  
Using Data

ABSTRACTThe study focuses on the language abilities of 30 20-month-old children, using data from two sources: a detailed maternal interview and 90 minutes of videotaped observation. Observed language was coded into the categories used for the interview. Production and comprehension at 28 months (MLU, PPVT and morphology comprehension) were also assessed. Observation and interview data at 20 months were highly intercorrelated. Cluster analyses of both data sets yielded referential, grammatical morpheme and dialogue clusters, providing partial support for the nominal/pronominal and referential/expressive acquisition styles reported in the literature. However, the referential and grammatical morpheme clusters were highly correlated, suggesting that two acquisition strategies are developing in parallel. Only for those children who heavily emphasize one strategy can one speak of a distinctive style. All interview and observation clusters predicted 28-months MLU, but the grammatical morpheme clusters did not predict later performance on a Grammatical Morpheme Test. It is tentatively suggested that holistic processing strategies underlie the pronominal/expressive style.

scholarly journals Correlation Versus Interchangeability: The Limited Robustness of Empirical Findings on Democracy Using Highly Correlated Data Sets

2003 ◽  
Vol 11 (2) ◽  
pp. 196-203 ◽  
Author(s):  
Gretchen Casper ◽  
Claudiu Tufis
Keyword(s):  
Comparative Politics ◽  
Correlated Data ◽  
Statistical Analyses ◽  
Data Sets ◽  
Time Periods ◽  
Highly Correlated ◽  
Using Data

This article shows that highly correlated measures can produce different results. We identify a democratization model from the literature and test it in more than 120 countries from 1951 to 1992. Then, we check whether the results are robust regarding measures of democracy, time periods, and levels of development. The findings show that measures do matter: Whereas some of the findings are robust, most of them are not. This explains, in part, why the debates on democracy have continued rather than been resolved. More important, it underscores the need for more careful use of measures and further testing to increase confidence in the findings. Scholars in comparative politics are drawn increasingly to large-N statistical analyses, often using data sets collected by others. As in any field, we show how they must be careful in choosing the most appropriate measures for their studies, without assuming that any correlated measure will do.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Studying individual differences in the social environment to better understand language learning and processing

2016 ◽  
Vol 2 (s1) ◽  
Author(s):  
Shiri Lev-Ari
Keyword(s):  
Second Language ◽  
Individual Differences ◽  
Second Language Acquisition ◽  
Language Acquisition ◽  
Language Learning ◽  
Social Environment ◽  
Native Speakers ◽  
Linguistic Performance ◽  
The Social

AbstractPeople learn language from their social environment. Therefore, individual differences in the input that their social environment provides could influence their linguistic performance. Nevertheless, investigation of the role of individual differences in input on performance has been mostly restricted to first and second language acquisition. In this paper I argue that individual differences in input can influence linguistic performance even in adult native speakers. Specifically, differences in input can affect performance by influencing people’s knowledgebase, by modulating their processing manner, and by shaping expectations. Therefore, studying the role that individual differences in input play can improve our understanding of how language is learned, processed and represented.

Estimation of Construction Waste Generation and Management in Taiwan

2011 ◽  
Vol 243-249 ◽  
pp. 6292-6295 ◽  
Author(s):  
Rong Yau Huang ◽  
Li Hsu Yeh ◽  
Hao Hsien Chen ◽  
Jyh Dong Lin ◽  
Ping Fu Chen ◽  
...  
Keyword(s):  
Decision Tree ◽  
Cross Correlation ◽  
Expert Interviews ◽  
Waste Generation ◽  
Valid Conclusion ◽  
Construction Waste ◽  
Composition Ratio ◽  
The Past ◽  
Highly Correlated ◽  
Using Data

This study examines construction waste generation and management in Taiwan. We verify the factors probable affecting the output of construction wastes by using data for the output of declared construction wastes produced from demolition projects in Taiwan in the last year, expert interviews, and research achievements in the past, and find “ on-site separation” is the factor with effects on the output of construction wastes via cross-correlation by algorithms such as K-Means and Decision Tree C5.0. It can be seen that the output (0.092(t/M3) with on-site separation or 0.329(t/M3) without on-site separation is highly correlated with the composition ratio of construction wastes and referred to as a valid conclusion.

POSTPARTUM AMENORRHOEA IN RURAL EASTERN UTTAR PRADESH, INDIA

1998 ◽  
Vol 30 (2) ◽  
pp. 227-243
Author(s):  
K. N. S. YADAVA ◽  
S. K. JAIN
Keyword(s):  
Higher Education ◽  
Uttar Pradesh ◽  
Current Status Data ◽  
North India ◽  
Current Status ◽  
Data Sets ◽  
Survival Status ◽  
The Mean ◽  
The Difference ◽  
Using Data

This paper calculates the mean duration of the postpartum amenorrhoea (PPA) and examines its demographic, and socioeconomic correlates in rural north India, using data collected through 'retrospective' (last but one child) as well as 'current status' (last child) reporting of the duration of PPA.The mean duration of PPA was higher in the current status than in the retrospective data;n the difference being statistically significant. However, for the same mothers who gave PPA information in both the data sets, the difference in mean duration of PPA was not statistically significant. The correlates were identical in both the data sets. The current status data were more complete in terms of the coverage, and perhaps less distorted by reporting errors caused by recall lapse.A positive relationship of the mean duration of PPA was found with longer breast-feeding, higher parity and age of mother at the birth of the child, and the survival status of the child. An inverse relationship was found with higher education of a woman, higher education of her husband and higher socioeconomic status of her household, these variables possibly acting as proxies for women's better nutritional status.

Pronoun reversals: who, when, and why?

1993 ◽  
Vol 20 (3) ◽  
pp. 573-589 ◽  
Author(s):  
Philip S. Dale ◽  
Catherine Crain-Thoreson
Keyword(s):  
Individual Differences ◽  
Language Acquisition ◽  
Risk Taking ◽  
Contextual Factors ◽  
Noun Phrases ◽  
Second Person ◽  
Pronoun Use ◽  
Processing Resources ◽  
Unstructured Play

ABSTRACTSeventeen of a sample of 30 precocious talkers aged 1;8 produced at least one pronoun reversal (I/you) during unstructured play. This finding led to an examination of the role of cognitive and linguistic individual differences as well as contextual factors and processing complexity as determinants of pronoun reversal. Contrary to predictions derived from previous hypotheses, there were few differences between reversers and non-reversers, other than higher use of second person forms by reversers. Reversals were more likely to occur in certain contexts: semantically reversible predicates with two noun phrases, and in imitations (though the rate of imitation was lower overall in reversers). We propose that pronoun reversals commonly result from a failure to perform a deictic shift, which is especially likely when children's psycholinguistic processing resources are taxed. Children who did not produce any pronoun reversals tended to avoid pronoun use, especially second person forms. Overt reversal may thus reflect a risk-taking approach to language acquisition, which may be particularly characteristic of precocious children.

scholarly journals Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

2018 ◽  
Vol 7 (2.28) ◽  
pp. 312
Author(s):  
Manu Kohli
Keyword(s):  
Data Extraction ◽  
Indirect Costs ◽  
True Positive Rate ◽  
Spare Parts ◽  
Machine Learning Algorithms ◽  
Machine Language ◽  
Support Vector ◽  
Data Sets ◽  
Equipment Failure ◽  
Using Data

Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations. 

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