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Doctoral Thesis
DOI
10.11606/T.18.2017.tde-12052017-103355
Document
Author
Full name
Renato Ferrari Pacheco
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2004
Supervisor
Committee
Araújo, Aluizio Fausto Ribeiro (President)
França, Felipe Maia Galvão
Gonzalez, Maria Eunice Quilici
Oliveira, Vilma Alves de
Piqueira, José Roberto Castilho
Title in Portuguese
Módulos neurais para modelagem de falsas memórias
Keywords in Portuguese
Estruturas cerebrais
Falso reconhecimento
Ilusões de memória
Memórias
Redes neurais artificiais
Rejeição errada
Simulação computacional
Teoria do rastro difuso
Abstract in Portuguese
As falsas memórias são um tipo falha de memória, em que o indivíduo pode (a) reconhecer como tendo visto antes um objeto ou evento que não tenha ocorrido ou (b) não reconhecer algo previamente presenciado. Estes são o falso reconhecimento e a rejeição errada. Segundo a teoria do rastro difuso, dois processos distintos agem em paralelo durante a memorização e reconhecimento, um sobre as informações literais (verbatim) e o outro sobre a essência do significado da palavra (gist). Neste trabalho é proposto um sistema modular de redes neurais artificiais que considera estes dois processos, características funcionais das estruturas cerebrais envolvidas na memorização e fluxo de informação análogo ao ocorrido no cérebro. O modelo neural é validado através de treinamento para armazenar e recuperar listas de palavras semanticamente relacionadas. Na formulação do modelo e da representação foram considerados a representação fonológica e significado das palavras, de forma a simular as computações ocorridas e os resultados obtidos em experimentos efetuados com sujeitos humanos. Nestes experimentos, 12 listas de aproximadamente 15 palavras, cada lista semanticamente relacionadas a um tema são ouvidas e, em seguida, algumas destas palavras, a palavra tema e outras palavras não relacionadas são também ouvidas e os indivíduos respondem se cada palavra fora ouvida previamente. Os resultados obtidos computacionalmente aproximam-se bastante dos resultados obtidos com sujeitos humanos, e o modelo produzido serve como base para estudo das influências dos diversos processos atuantes durante a memorização e reconhecimento.
Title in English
Neural modules for false memories modelling
Keywords in English
Artificial neural networks
Brain structures
Computational simulation
False memory
False memory theory
Memories
Memory illusions
Wrong rejection
Abstract in English
False memories are a kind of memory failure, in which the subject may (a) recognize as known an never seen object or never happened fact or (b) don't recognize something that was already presented him. These are false memories and wrong rejections. According to false memory theory, two parallel processes act during memorization and recognition, one on verbatim information and other on gist information. In this work is proposed a artificial neural network model system that takes in account these two processes, functional issues about brain structures involved on memorization and the an information flow analog to the occurred in the brain. The neural model is validated by training to store in recover lists of semantically related words. In the model and representation scheme formulation, phonological and semantic informations were used intending to simulate brain computations and results of human subjects experiments. In such experiment, 12 lists of something about 15 semantically related words, are heard and, in the second step, in the sequence, many of these words, other related words and not related words are heard in a recognition test, when subjets say if that word was or was not heard during memorization steps. Results obtained from computer tests are very close of human results, and the produced model may be used as a tool for analysis of the influences of the many processes that take place during memorization and recognition.
 
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Publishing Date
2017-05-12
 
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