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KittyCat: a cognitive model of structure-form discovery

Submitted by Andréia Brandão Daltro Sodré (andreia.bdsodre@gmail.com) on 2014-11-12T19:16:58Z
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Previous issue date: 2014-10-09 / Cognition is a core subject to understand how humans think and behave. In that sense, it is clear that Cognition is a great ally to Management, as the later deals with people and is very interested in how they behave, think, and make decisions. However, even though Cognition shows great promise as a field, there are still many topics to be explored and learned in this fairly new area. Kemp & Tenembaum (2008) tried to a model graph-structure problem in which, given a dataset, the best underlying structure and form would emerge from said dataset by using bayesian probabilistic inferences. This work is very interesting because it addresses a key cognition problem: learning. According to the authors, analogous insights and discoveries, understanding the relationships of elements and how they are organized, play a very important part in cognitive development. That is, this are very basic phenomena that allow learning. Human beings minds do not function as computer that uses bayesian probabilistic inferences. People seem to think differently. Thus, we present a cognitively inspired method, KittyCat, based on FARG computer models (like Copycat and Numbo), to solve the proposed problem of discovery the underlying structural-form of a dataset.

Identiferoai:union.ndltd.org:IBICT/oai:bibliotecadigital.fgv.br:10438/12442
Date09 October 2014
CreatorsSodré, Andréia Brandão Daltro
ContributorsSobral, Filipe, Koiller, Jair, Escolas::EBAPE, Linhares, Alexandre
Source SetsIBICT Brazilian ETDs
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis
Sourcereponame:Repositório Institucional do FGV, instname:Fundação Getulio Vargas, instacron:FGV
Rightsinfo:eu-repo/semantics/openAccess

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