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      Metajudgment: Metatheories and Beliefs About Good Judgment Across Societies

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          Abstract

          We introduce the concept of “metajudgment” to provide a framework for understanding folk standards people use to navigate everyday decisions. Defined as a set of metatheories and beliefs about different types of judgment, metajudgment serves as the guiding principle behind the selection and application of reasoning strategies in various contexts. We review emerging studies on metajudgment to identify common dimensions, such as intuition versus deliberative reasoning and rationality versus reasonableness. These dimensions are examined across multiple societies. The reviewed findings illuminate an apparent paradox: Universal adaptive challenges produce largely consistent folk standards of judgment across cultures, whereas situational demands drive systematic within-person variability. Metajudgment offers a comprehensive framework for understanding diverse reasoning patterns in individual and cross-cultural contexts, calling for greater attention to the ecologically sensitive study of within-person judgmental variability.

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          Resistance to Medical Artificial Intelligence

          Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers’ unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) that only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.
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            Task-Dependent Algorithm Aversion

            Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task’s perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms’ perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.
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              The adaptive decision maker

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                Author and article information

                Journal
                Curr Dir Psychol Sci
                Curr Dir Psychol Sci
                CDP
                spcdp
                Current Directions in Psychological Science
                SAGE Publications (Sage CA: Los Angeles, CA )
                0963-7214
                1467-8721
                8 August 2024
                August 2024
                : 33
                : 4
                : 261-269
                Affiliations
                [1-09637214241262335]Department of Psychology, University of Waterloo
                Author notes
                [*]Igor Grossmann, Department of Psychology, University of Waterloo Email: igrossma@ 123456uwaterloo.ca
                Author information
                https://orcid.org/0000-0003-2681-3600
                Article
                10.1177_09637214241262335
                10.1177/09637214241262335
                11357895
                39219628
                fb673927-52f1-4240-8d31-f3cd1f259c5e
                © The Author(s) 2024

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Funding
                Funded by: John Templeton Foundation, FundRef https://doi.org/10.13039/100000925;
                Award ID: 62260
                Funded by: Social Sciences and Humanities Research Council of Canada, FundRef https://doi.org/10.13039/501100000155;
                Award ID: Insight Grant 435-2014-0685
                Categories
                Article
                Custom metadata
                ts1

                Clinical Psychology & Psychiatry
                judgment,folk theories,norms,ecological decision-making,practical wisdom

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