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      When people are defeated by artificial intelligence in a competition task requiring logical thinking, how do they make causal attribution?

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          Abstract

          Given that artificial intelligence (AI) has been predicted to eventually take on human tasks demanding logical thinking, it makes sense that we should examine psychological responses of humans when their performance is inferior to AI. Research has demonstrated that after people fail a task, whether they reorient their behavior towards success depends on what they attribute the failure to. This study investigated the causal attributions people made in a competition task requiring such thinking. We also recorded whether they wanted to re-challenge the games after they were defeated by AI. Experiments 1 ( N = 74) and 2 ( N = 788) recruited Japanese participants, while Experiment 3 ( N = 500) comprised American participants. There were two conditions: in the first, participants competed against an AI opponent and in the other, they believed they were competing against a human. The results of the three experiments showed that participants attributed the loss to their own and their opponent’s abilities more than any other factor, irrespective of the opponent type. The number of participants choosing to re-challenge the game did not differ significantly between the AI and human conditions in Experiments 1 and 3, although the number was lower in the AI condition than in the human condition in Experiment 2. Besides providing fresh insight on how people make causal attributions when competing against AI, our findings also predict how people will respond after their jobs are replaced by AI.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s12144-021-02559-w.

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            High-performance medicine: the convergence of human and artificial intelligence

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            The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
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              The associative basis of the creative process.

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                Author and article information

                Contributors
                cykd1004@mail2.doshisha.ac.jp
                Journal
                Curr Psychol
                Curr Psychol
                Current Psychology (New Brunswick, N.j.)
                Springer US (New York )
                1046-1310
                1936-4733
                14 January 2022
                : 1-16
                Affiliations
                [1 ]GRID grid.255178.c, ISNI 0000 0001 2185 2753, Faculty of Psychology, Graduate School of Psychology, , Doshisha University, ; 1-3, TataraMiyakodani, Kyotanabe-shi, Kyoto, 610-0394 Japan
                [2 ]GRID grid.54432.34, ISNI 0000 0004 0614 710X, Japan Society for the Promotion of Science Research Fellow, ; Tokyo, Japan
                Author information
                http://orcid.org/0000-0002-4149-4524
                https://orcid.org/0000-0002-5073-100X
                Article
                2559
                10.1007/s12144-021-02559-w
                8758208
                83a23a97-b9ae-4e97-956e-9bf0933d7e7a
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 21 November 2021
                Categories
                Article

                Clinical Psychology & Psychiatry
                artificial intelligence,causal attribution,behavioral response,competition game,self-effacing bias

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