Em competição com um roba´ humana³ide, os humanos atrasam suas decisaµes quando o roba´ olha para eles
Na maioria das situaa§aµes da vida cotidiana, o cérebro humano precisa se envolver não apenas na tomada de decisaµes, mas também em antecipar e prever o comportamento dos outros.
![](data:image/jpeg;base64,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)
Ilustração de um humano e um roba´ humana³ide envolvidos em um jogo competitivo, conforme relatado em Belkaid et al. “O olhar maºtuo com um roba´ afeta a atividade neural humana e atrasa os processos de tomada de decisão†( Science Robotics ). O humano estãojogando contra o roba´ enquanto sua atividade cerebral émedida com eletroencefalograma (EEG). Crédito: IIT-Istituto Italiano di Tecnologia
O olhar éum sinal extremamente poderoso e importante durante a comunicação e interação humano-humano, transmitindo intenções e informando sobre as decisaµes dos outros. O que acontece quando um roba´ e um humano interagem olhando um para o outro? Pesquisadores do IIT-Istituto Italiano di Tecnologia (Instituto Italiano de Tecnologia) investigaram se o olhar de um roba´ humana³ide influencia a maneira como as pessoas raciocinam em um contexto de tomada de decisão social. O que eles descobriram éque um olhar maºtuo com um roba´ afeta a atividade neural humana, influenciando os processos de tomada de decisão, em particular atrasando-os. Assim, o olhar de um roba´ leva os humanos a percebaª-lo como um sinal social. Essas descobertas tem fortes implicações para contextos onde humana³ides podem encontrar aplicações, como colegas de trabalho, suporte clanico ou auxiliares domanãsticos.
Â
O projeto, denominado InStance, aborda a questãode quando e em que condições as pessoas tratam os robôs como seres intencionais. Ou seja, se, para explicar e interpretar o comportamento do roba´ , as pessoas se referem a estados mentais como crena§as ou desejos.
Os autores do artigo de pesquisa são Marwen Belkaid, Kyveli Kompatsiari, Davide de Tommaso, Ingrid Zablith e Agnieszka Wykowska.
Na maioria das situações da vida cotidiana, o cérebro humano precisa se envolver não apenas na tomada de decisaµes, mas também em antecipar e prever o comportamento dos outros. Em tais contextos, o olhar pode ser altamente informativo sobre as intenções, objetivos e decisaµes futuras dos outros. Os humanos prestam atenção aos olhos dos outros, e o cérebro reage fortemente quando alguém olha para eles ou direciona o olhar para um determinado evento ou local no ambiente. Os pesquisadores investigaram esse tipo de interação com um roba´.
Os autores do artigo de pesquisa são Marwen Belkaid, Kyveli Kompatsiari, Davide de
Tommaso, Ingrid Zablith e Agnieszka Wykowska (lider da equipe) do laboratório do IIT
“Social Cognition in Human-Robot Interactionâ€. Crédito:
IIT-Istituto Italiano di Tecnologia
“Os robôs estara£o cada vez mais presentes em nossa vida cotidianaâ€, diz Agnieszka Wykowska, pesquisadora principal do IIT e autora saªnior do artigo. "a‰ por isso que éimportante entender não apenas os aspectos tecnola³gicos do design do roba´, mas também o lado humano da interação homem-roba´. Especificamente, éimportante entender como o cérebro humano processa os sinais comportamentais transmitidos pelos robôs."
Wykowska e seu grupo de pesquisa pediram a um grupo de 40 participantes para jogar um jogo estratanãgico - o jogo do frango - com o roba´ iCub enquanto mediam o comportamento e a atividade neural dos participantes, esta última por meio de eletroencefalografia (EEG). O jogo éestratanãgico, representando uma situação em que dois motoristas de carros simulados se movem um em direção ao outro em rota de colisão e o resultado depende se os jogadores cedem ou seguem em frente.
Os pesquisadores descobriram que os participantes demoraram mais para responder quando o iCub estabeleceu o olhar maºtuo durante a tomada de decisão, em relação ao olhar desviado. As respostas atrasadas podem sugerir que o olhar maºtuo acarretou um maior esfora§o cognitivo, por exemplo, ao extrair mais raciocanio sobre as escolhas do iCub ou maior grau de supressão do estamulo do olhar potencialmente distrativo, o que era irrelevante para a tarefa.
"Pense em jogar pa´quer com um roba´. Se o roba´ olhar para vocêno momento em que vocêprecisa tomar uma decisão sobre o pra³ximo movimento, vocêtera¡ mais dificuldade em tomar uma decisão, em relação a uma situação em que o roba´ desvia o olhar Seu cérebro também precisara¡ empregar processos difaceis e caros para tentar "ignorar" aquele olhar do roba´ ", diz Wykowska.
Â
Esses resultados sugerem que o olhar do roba´ "sequestra" os mecanismos "sociocognitivos" do cérebro humano - fazendo o cérebro responder ao roba´ como se ele fosse um agente social. Nesse sentido, “ser social†para um roba´ nem sempre pode ser benanãfico para o ser humano, interferindo em seu desempenho e velocidade de tomada de decisão, mesmo que sua interação recaproca seja agrada¡vel e envolvente.
Wykowska e seu grupo de pesquisa esperam que essas descobertas ajudem os roboticistas a projetar robôs que exibam o comportamento mais apropriado para um contexto especafico de aplicação. Humana³ides com comportamento social podem ser aºteis no auxalio no cuidado ao idoso ou na puericultura, como no caso do roba´ iCub, fazendo parte da terapia experimental no tratamento do autismo. Por outro lado, quando o foco na tarefa énecessa¡rio, como nas configurações de fa¡brica ou no controle de tra¡fego aanãreo, a presença de um roba´ com sinais sociais pode ser uma distração.