<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1645-0086</journal-id>
<journal-title><![CDATA[Psicologia, Saúde & Doenças]]></journal-title>
<abbrev-journal-title><![CDATA[Psic., Saúde & Doenças]]></abbrev-journal-title>
<issn>1645-0086</issn>
<publisher>
<publisher-name><![CDATA[Sociedade Portuguesa de Psicologia da Saúde]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1645-00862023000301059</article-id>
<article-id pub-id-type="doi">10.15309/23psd240322</article-id>
<title-group>
<article-title xml:lang="pt"><![CDATA[A importância do aprendizado de máquina nos estudos dos afetos]]></article-title>
<article-title xml:lang="en"><![CDATA[The importance of machine learning in affect studies]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Almeida]]></surname>
<given-names><![CDATA[Saulo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silva]]></surname>
<given-names><![CDATA[Paulo Caetano da]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade Salvador  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>30</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>30</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>24</volume>
<numero>3</numero>
<fpage>1059</fpage>
<lpage>1072</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1645-00862023000301059&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1645-00862023000301059&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1645-00862023000301059&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Aprendizado de máquina é um campo da Inteligência Artificial no qual um sistema pode modificar um comportamento, de forma autônoma, baseando-se na sua própria experiência, com o uso de regras lógicas que modificam o desempenho de uma tarefa ou tomam a decisão mais apropriada para o contexto. Um dos seus principais desafios é pensar como uma máquina pode aprender a lidar com afetos ou seus correlatos na interação com seres humanos ou outros agentes. Para isso, este trabalho busca compreender o que se tem produzido em uma associação entre as áreas dos afetos e do aprendizado de máquina, através de uma revisão sistemática de literatura. Após as filtragens aplicadas, discute-se 22 artigos que tratam dos temas de pesquisa. Em geral, os trabalhos lidam diretamente com a afetividade humana ou esta aparece como elemento ligado aos conceitos gerais, como é no caso dos estudos acerca do engajamento. Os trabalhos discutidos se destacam pelo uso de aprendizado de máquina na leitura e análise de dados retirados de resultados de exames diagnósticos ou de redes léxicas, associados a equipamentos de análise da atividade cerebral, destacando o eletroencefalograma (EEG), ou mesmo, o uso de robôs, chatbots ou jogos eletrônicos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Machine learning is a field of Artificial Intelligence in which a system can modify a behavior, autonomously, based on its own experience, using logical rules that modify the performance of a task or make the most appropriate decision. for the context. One of its main challenges is to think about how a machine can learn to deal with affects or their correlates in the interaction with human beings or other agents. For this, this work seeks to understand what has been produced in an association between the areas of affection and machine learning, through a systematic literature review. After the filters applied, 22 articles dealing with the research topics are discussed. In general, the works deal directly with human affectivity or it appears as an element linked to general concepts, as is the case of studies on engagement. The works discussed stand out for the use of machine learning in the reading and analysis of data taken from the results of diagnostic tests or lexical networks, associated with equipment for analyzing brain activity, highlighting the electroencephalogram (EEG), or even the use of robots, chatbots or electronic games.]]></p></abstract>
<kwd-group>
<kwd lng="pt"><![CDATA[Aprendizado de máquina]]></kwd>
<kwd lng="pt"><![CDATA[Afetos]]></kwd>
<kwd lng="pt"><![CDATA[Inteligência artificial]]></kwd>
<kwd lng="pt"><![CDATA[Avaliação psicológica]]></kwd>
<kwd lng="pt"><![CDATA[Psicologia]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[Affections]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Psychological assessment]]></kwd>
<kwd lng="en"><![CDATA[Psychology]]></kwd>
</kwd-group>
</article-meta>
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