Thesis. 1976. Ph.D.--Massachusetts Institute of Technology. Dept. of Philosophy. / Microfiche copy available in Archives and Humanities. / Bibliography: leaves 165-170. / Ph.D.

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The process of acquiring language requires children to learn grammatical categories and apply these categories to new words. Researchers have proposed various explanations of this process in the form of algorithms and computational modeling. Recently, adaptation and selection models have been tested and applied as a possible explanation to the process of acquiring grammatical categories. These studies have proven promising, however, the external validity of this approach has not been examined by grammatically coding samples outside the training corpus. The current thesis applies an adaptation and selection model, which pauses the evolution of dictionaries after every thousand cycles to allow the tagging of 30 outside samples, which are then checked for tagging accuracy. The accuracy across the five training corpora by the six thousandth cycle averaged 76.75%. Additional research is needed to explore the effects of altering the parameters in the model.

The purpose of this research was to study grammatical categories in the Tagalog mental lexicon using lexical decision tasks. Some linguists question whether words in Tagalog can be classified as nouns and verbs (Foley, 1998; Kaufman, 2011) because most root words can be inflected for any grammatical function and because verbs cannot be used in their uninflected form. Previous studies with English and German (Kauschke and Stenneken 2008) have shown that participants respond differently to nouns and verbs in lexical decision tasks. These studies have also shown that participants respond differently to transitive and intransitive verbs in lexical decision tasks. It was assumed that if nouns and verbs exist in Tagalog, response times to Tagalog lexical decision tasks will show similar patterns to those performed in English and German. Two experiments were performed to examine whether words are classified as nouns and verbs in the Tagalog mental lexicon and whether other factors affected that classification. For the experiments, native speakers of Tagalog participated in lexical decision tasks and response times were measured. The first experiment tested the classification of root nouns and verbs. Contrary to findings in other languages, there was no significant difference between response times to nouns and verbs. However, there were differences in response times to nouns from different semantic categories and to verbs with different morphosyntactic structures. The second experiment examined the classification of inflected nouns and verbs. Again, the results showed no difference between response times to nouns and verbs. There was also no difference between transitive and intransitive verbs. However, there was a slight difference between verbs of different voice inflections. The results of the experiments suggest the while the grammatical classes of nouns and verbs may not be the most important features of words in the Tagalog mental lexicon, they may still play a role since different features, semantics or morphosyntactics, did affect the responses to words from the different categories.

By the later preschool years, most children have a knowledge of the grammatical categories of their native language and are capable of expanding this knowledge to novel words. To model this accomplishment, researchers have created a variety of explicit, testable models or algorithms. These have had partial but promising success in extracting grammatical word categories from transcriptions of caregiver input to young children. Additional insight into children's learning of the grammatical categories of words might be gained from evolutionary computing algorithms, which apply principles of evolutionary biology such as variation, adaptive change, self-regulation, and inheritance to computational models. The current thesis applied such a model to the language addressed to five children, whose ages ranged from 1;1 to 5;1 (years;months). The model evolved dictionaries linking words to their grammatical tags and was run for 4000 cycles; four different rates of mutation of offspring dictionaries were assessed. The accuracy for coding the words in the corpora of language addressed to the children averaged 92.74%. Directions for further development and evaluation of the model are proposed.

Most children who follow a typical developmental timeline learn the grammatical categories of words in their native language by the time they enter school. Researchers have worked to provide a number of explicit, testable models or algorithms in an attempt to model this language development. These models or algorithms have met with some varying success in terms of determining grammatical word categories from the transcripts of adult input to children. A new model of grammatical category acquisition involving an application of evolutionary computing algorithms may provide further understanding in this area. This model implements aspects of evolutionary biology, such as variation, adaptive change, self-regulation, and inheritance. The current thesis applies this model to six English language corpora. The model created dictionaries based on the words in each corpus and matched the words with their grammatical tags. The dictionaries evolved over 5,000 generations. Four different mutation rates were used in creating offspring dictionaries. The accuracy achieved by the model in correctly matching words with tags reached 90%. Considering this success, further research involving an evolutionary model appears warranted.

Most typically developing children have achieved a knowledge of the grammatical categories of the words in their native language by school age. To model this achievement, researchers have developed a variety of explicit, testable models or algorithms which have had partial but promising success in extracting the grammatical word categories from the transcriptions of caregiver input to children. Additional insight into children's learning of the grammatical categories of words might be obtained from an application of evolutionary computing algorithms, which simulate principles of evolutionary biology such as variation, adaptive change, self-regulation, and inheritance. Thus far, however, this approach has only been applied to English language corpora. The current thesis applied such a model to corpora of language addressed to five Spanish-speaking children, whose ages ranged from 0;11 to 4;8 (years; months). The model evolved dictionaries which linked words to their grammatical tags and was run for 5000 cycles; four different rates of mutation of offspring dictionaries were assessed. The accuracy for coding the words in the corpora of language addressed to the children peaked at about 85%. Directions for further development and evaluation of the model and its application to Spanish language corpora are suggested.

The purpose of this study is to make it visible how children in need of special support are described in action plans, as well as what normative ideas we have about children's behavior and needs at preschool. The aim of the study has been answered with the support of the following questions: How are children in need of special support described? Which power structures or norms become visible in the design of the action plans? Which value-laden words are often used? Our study is a qualitative method where we analyze pre-written documents that are made for children in need of special support at preschool. To support our study, we have chosen to analyze our empirical material from an intersectional perspective, which is an analytical perspective that pays attention to the power structures that exist in our society and what they create for normative ideas about the human being. Based on our analysis, we obtained a result that shows that children are categorized as children in need of special support, when their behavior confuses and worries the staff at the preschool. Our results show how the power structures that exist in our society contribute to normative ideas about how children should be, what they should be able to do, and if children cannot live up to that, it is considered as something deviant. Our results show that preschool staff tend to have normative ideas about children's play, gender, age and ethnicity. Keywords: Documentation, expectations, power structures, norms, categories.