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Doctoral Thesis
DOI
https://doi.org/10.11606/T.42.2011.tde-04102011-163948
Document
Author
Full name
Carolina Feher da Silva
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2011
Supervisor
Committee
Baldo, Marcus Vinicius Chrysostomo (President)
Alfonso, Nestor Felipe Caticha
Claessens, Peter Maurice Erna
Ranvaud, Ronald Dennis Paul Kenneth Clive
Xavier, Gilberto Fernando
Title in Portuguese
Abordagem computacional e psicofísica da alocação atencional e tomada de decisão.
Keywords in Portuguese
Algorítmos genéticos
Atenção
Cadeias de Markov
Problemas relacionados à evolução
Redes neurais
Tomada de decisão
Abstract in Portuguese
O processo evolutivo deixa vieses no sistema nervoso de forma a optimizar nossas capacidades cognitivas para o ambiente em que evoluímos. Nosso objetivo é criar modelos de vida artificial nos quais a atenção seletiva, a tomada de decisão em sequências binárias e o tempo de reação ao aparecimento abrupto de um alvo precedido por pista emerjam como consequência da evolução. Em nossos experimentos, a atenção seletiva enviesava o processamento de estímulos de forma a dar prioridade aos mais relevantes quando eles tinham relevâncias diferentes. Nossos experimentos de tomada de decisão apóiam a teoria de que o pareamento de probabilidades, estratégia adotada por seres humanos neste tipo de experimento, é consequência da busca de padrões, que decorre da importância que isto teve durante a evolução humana. No estudo do tempo de reação, o comportamento observado em seres humanos só pôde ser modelado em populações de animais artificiais quando existia ruído e eles tinham que selecionar uma ação apropriada entre duas possíveis.
Title in English
Computational and psychophysical approach to attentional allocation and decision making.
Keywords in English
Attention
Decision making
Genetic algorithms
Markov chains
Neural networks
Problems related to the evolution
Abstract in English
The evolutionary process leaves biases in the nervous system so as to optimize our cognitive capacities to the environment where we evolved. Our objective is to create artificial life models wherein selective attention, decision making in binary sequences and reaction time to the abrupt appearance of a target preceded by a cue emerge as a consequence of evolution. In our experiments, selective attention biased stimuli processing so as to give priority to the most relevant stimuli when they had different relevances. Our decision making experiments support the theory that probability matching, the strategy adopted by humans in this kind of experiment, is a consequence of a search for patterns, which results from the importance that finding regularities in our environment had during human evolution. In the study of reaction time, the behavior observed in humans could only be modeled in populations of artificial animal when there was noise and they had to select an appropriate action between two possible ones.
 
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Publishing Date
2011-11-10
 
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