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Bibliography: p. 185-196. Electronic publication; Full text available in PDF format; abstract in HTML format. Electronic reproduction.[Australia] :Australian Digital Theses Program,2001.

Two connectionist models are developed that predict humans' categorization of facial expressions of emotion and their judgements of similarity between two facial expressions. For each stimulus, the models predict the subjects' judgement, the entropy of the response, and the mean response time (RT). Both models involve a connectionist component which predicts the response probabilities and a response generator which predicts the mean RT. The input to the categorization model is a preprocessed picture of a facial expression, while the hidden unit representations generated by the first model for two facial expressions constitute the input of the similarity model. The data collected on 45 subjects in a single-session experiment involving a categorization and a similarity task provided the target outputs to train both models. Two response generators are tested. The first, called the threshold model , is a linear integrator with threshold inspired from Lacouture and Marley's (1991) model. The second, called the channel model, constitutes a new approach which assumes a linear relationship between entropy of the response and mean RT. It is inspired by Lachman's (1973) interpretation of Shannon's (1948) entropy equation. The categorization model explains 50% of the variance of mean RT for the training set. It yields an almost perfect categorization of the pure emotional stimuli of the training set and is about 70% correct on the generalization set. A two-dimensional representation of emotions in the hidden unit space reproduces most of the properties of emotional spaces found by multidimensional scaling in this study as well as in other studies (e.g., Alvarado, 1996). The similarity model explains 53% of the variance of mean similarity judgements; it provides a good account of subjects' mean RT; and it even predicts an interesting bow effect that was found in subjects' data.

This study examines the use of the neural net paradigm as a modeling tool to represent word meanings. The neural net paradigm, also called "connectionism" and "parallel distributed processing," provides a new metaphor and vocabulary for representing the structure of the mental lexicon. As a research method applied to the componential analysis of word meanings, the neural net approach has one primary advantage over the traditional introspective method: freedom from the investigator's personal biases.The connectionist method is illustrated in this thesis with an extensive examination of the meanings of the words "cup" and "mug." These words have been studied previously by Labov (1973), Wierzbicka (1985), Andersen (1975), and Kempton (1978), using very different methods.The neural net models developed in this study are based on empirical data acquired through interviews with nine informants who classified 37 objects, 37 photographs, and 37 line drawings as "cups," "mugs," or "neither." These responses were combined with a data file representing the coded attributes of each object, to construct neural net models which reflect each informant's classification process.In the neural net models, the "cup" and "mug" features are interconnected with positive and negative weights that represent the association strengths of the features. When the connection weights are set so that they reflect the informants' responses, the neural net models can account for the extreme discrepancies in object-naming among informants, and the models can also account for the inconsistent classifications of each individual informant with respect to the mode of presentation (drawing, photograph, or actual object). Further, the neural net modelscan predict classifications for novel objects with an accuracy varying from 82% to 100%.By examining the connection weight patterns within the neural net model, it is possible to discover the "cup" and "mug" features which are most salient for each informant, and for the informants collectively. This analysis shows that each informant has acquired internal meanings for the words "cup" and "mug" which are unique to the individual, although there is considerable overlap with respect to the most salient features. / Department of English

Thesis (M.S.)--Oregon Graduate Center, 1988.

Thesis (Ph.D.)--University of Adelaide, Dept. of Philosophy, 1998? / Bibliography: p. 185-196. Also available in print form.

Thesis (M.A.) -- University of Adelaide, School of Humanities, Discipline of Philosophy, 20045. / Includes bibliographical references. Also available in print.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 158-160).

Orientador: Edson Françozo / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Estudos da Linguagem / Made available in DSpace on 2018-08-07T04:10:15Z (GMT). No. of bitstreams: 1 Garcia_RicardoBasso_M.pdf: 578837 bytes, checksum: c5ae075736e4e6e2077a3d8f82d9be89 (MD5) Previous issue date: 2006 / Resumo: Esta dissertação começa com uma reflexão sobre o caráter interdisciplinar da psicolingüística, que é tomada como o campo de pesquisa que investiga, entre outros, os processos cognitivos subjacentes ao comportamento lingüístico. Essa investigação emprega diferentes métodos e técnicas, dada a complexidade do estudo de processos que são internos ao organismo e, portanto, não diretamente observáveis. Nesse quadro, introduzimos a metodologia adotada nesta dissertação - a modelagem. O uso de modelos é tratado em termos gerais, para então ser apresentado no contexto do estudo dos processos cognitivos. Neste ponto, é crucial notar que modelar esses processos implica em fazer suposições sobre como eles são. A hipótese geral que fundamenta os modelos de nosso interesse é a de que cognição é computação, ou seja, processos cognitivos são computacionais. Essa hipótese deu origem a dois paradigmas de modelagem distintos - um baseado nos fundamentos da computação digital e outro nos princípios da neurocomputação. Este segundo é apresentado em detalhes, depois de uma exposição das principais diferenças entre esses paradigmas. Estabelecidas as bases teórico-metodológicas dessa abordagem, replicamos o experimento de Elman (1990) em que uma rede neural recorrente simples é treinada para processar palavras dispostas em seqüências que compõem sentenças simples. É interessante notar que essa disposição reflete informações gramaticais, que podem ser descritas em tennos sintáticos (como sujeito, verbo e objeto) e semânticos (como agente, paciente e tema). Nosso intuito foi investigar se e como informações desse tipo são aprendidas pelo sistema. A análise dessa rede mostrou que seu processamento opera principalmente por distinções lexicais, isto é, não há o uso efetivo de informações lingüísticas do nível de sentenças. O passo seguinte foi modificar esse experimento - treinamos a rede com o mesmo conjunto de sentenças, desta vez marcando a fronteira entre elas. A análise dessa rede mostrou que, nesse novo experimento, o processamento opera principalmente por distinções sintáticas, havendo também a presença de distinções semânticas de papel temático. A partir desses resultados, elaboramos um novo experimento - treinamos uma rede com um conjunto de sentenças nas quais a disposição das palavras reflete principalmente informações semânticas de papel temático, havendo também a presença de informações sintáticas e da fronteira entre as sentenças. O objetivo foi controlar a informação lingüística presente nas sentenças, de modo a permitir maior confiabilidade nos resultados. A análise dessa rede mostrou-se coerente com a do experimento anterior, ou seja, o processamento está, de fato, operando segundo conhecimentos sintático-semânticos do nível de sentenças. A consistência dos resultados obtidos permite afirmar, com um grau de confiabilidade satisfatório, que há a presença de conhecimento sintático-semântico, durante o processamento de sentenças, ao mesmo tempo em que mostra como essas informações estão presentes. Esses resultados são importantes porque estendem a já conhecida capacidade da rede recorrente simples em codificar informação sintática, mostrando também sua capacidade em captar informação semântica / Abstract: This dissertation begins with a retlection about the interdisciplinary roots of psycholinguistics, which is taken as the research field concemed with the investigation of the cognitive processes subjacent to the verbal behavior. This investigation employs different methods and techniques, given the complexity inherent to the study of processes that take place inside the organism, and therefore are not directly observable. In view of this, we introduce the methodology adopted in this dissertation - modeling. The usage of models is initially dealt with in general terms, and then is analysed in the context of the study of cognitive processes. At this point, it is crucial to notice that modeling such processes implies making assumptions about how they are. The general hypothesis underlying the models we are interested in is that cognition is computation, that is, cognitive processes are computational. This hypothesis has given birth to two different paradigms of modeling - one based on the fundamentals of digital computing, and other based on the principIes of neurocomputing. Afier a brief discussion of their main differences, the later is presented in details. Once the theoretical-methodological basis of the neurocomputing framework is established, we replicate Elman's (1990) experiment in which a simple recurrent neural network is trained to process words in sequences, forming simple sentences. It should be noted that the sequences reflect grammatical information that can be described in syntactic (subjects, verbs and objects) and semantic (agents, patients and themes) terms. Our intent was to investigate whether and how such information is leamed by the system. Analyses of this experiment have shown that the processing is guided by lexical information, that is, linguistic information at the sentence leveI is not used. In the next step we have modified this experiment by training the network with the saroe set of sentences, just introducing an end-of-period mark between them. The analyses have shown that, in this new experiment, the processing is guided by both syntactic and semantic information, such as thematic roles. Taking these results as starting point, we devised a new experiment - a similar network was trained with a set of sentences in which word sequences were main1y led by semantic information like thematic role; the end-of-period mark between sentences was used. The intent was to controllinguistic information so as to increase our confidence in the results. Results consistent with the fonner experiments were obtained, that is, the processing is indeed guided by both syntactic and semantic infonnation at the sentence level. The consistency of the results achieved a1lows us to state that there are both syntactic and semantic knowledge operating during sentence processing. This result is important because it extends the already known simple recurrent network ability in coding syntactic information, showing that SRNs also are capable of handling semantic infonnation like thematic roles / Mestrado / Mestre em Linguística

This dissertation defends extended cognitivism: a recently emerging view in the philosophy of mind and cognitive science that claims that an individual's cognitive processes or states sometimes extend beyond the boundaries of their brain or their skin to include states and processes in the world. I begin the defence of this thesis through a background discussion of several foundational issues in cognitive science: the general character of cognitive behaviour and cognitive processes, as well as the nature and role of representation as it is standardly taken to figure in cognition. I argue in favour of the widely held view that cognition is best characterised as involving information processing, and that carriers of information (i.e., representations) are ineliminable components of the most distinctively human and powerful forms of cognition. Against this background the dissertation argues in stages for successively stronger claims regarding the explanatory role of the external world in cognition. First to be defended is the claim that cognition is often embedded in one's environment. I develop this claim in terms of what I call 'parainformation': roughly, information that shapes how we tackle a cognitive task by enabling the extraction of task-relevant information. Proceeding then to the defence of extended cognitivism, I draw most significantly on the work of Andy Clark. In outline, and in general following Clark, it is argued that states and processes occurring beyond the skin of the cognitive agent sometimes play the same explanatory role as internal processes that unquestionably count as cognitive. I develop this claim in two versions of differing strength: firstly, in a general way without commitment to the representational character of extended cognition, and secondly in a specifically representational version with special attention to intentional explanation. Against each of these versions of extended cognitivism are ranged a number of criticisms and objections, many of which stem from the work of Fred Adams and Ken Aizawa. The dissertation examines these objections and rejects each of them in turn. / Arts, Faculty of / Philosophy, Department of / Graduate