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  1. Article ; Online: Deciding to be authentic: Intuition is favored over deliberation when authenticity matters.

    Oktar, Kerem / Lombrozo, Tania

    Cognition

    2022  Volume 223, Page(s) 105021

    Abstract: Deliberative analysis enables us to weigh features, simulate futures, and arrive at good, tractable decisions. So why do we so often eschew deliberation, and instead rely on more intuitive, gut responses? We propose that intuition might be prescribed for ...

    Abstract Deliberative analysis enables us to weigh features, simulate futures, and arrive at good, tractable decisions. So why do we so often eschew deliberation, and instead rely on more intuitive, gut responses? We propose that intuition might be prescribed for some decisions because people's folk theory of decision-making accords a special role to authenticity, which is associated with intuitive choice. Five pre-registered experiments find evidence in favor of this claim. In Experiment 1 (N = 654), we show that participants prescribe intuition and deliberation as a basis for decisions differentially across domains, and that these prescriptions predict reported choice. In Experiment 2 (N = 555), we find that choosing intuitively vs. deliberately leads to different inferences concerning the decision-maker's commitment and authenticity-with only inferences about the decision-maker's authenticity showing variation across domains that matches that observed for the prescription of intuition in Experiment 1. In Experiment 3 (N = 631), we replicate our prior results and rule out plausible confounds. Finally, in Experiment 4 (N = 177) and Experiment 5 (N = 526), we find that an experimental manipulation of the importance of authenticity affects the prescribed role for intuition as well as the endorsement of expert human or algorithmic advice. These effects hold beyond previously recognized influences on intuitive vs. deliberative choice, such as computational costs, presumed reliability, objectivity, complexity, and expertise.
    MeSH term(s) Decision Making ; Goals ; Humans ; Intuition/physiology ; Reproducibility of Results
    Language English
    Publishing date 2022-02-26
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1499940-7
    ISSN 1873-7838 ; 0010-0277
    ISSN (online) 1873-7838
    ISSN 0010-0277
    DOI 10.1016/j.cognition.2022.105021
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Philosophy instruction changes views on moral controversies by decreasing reliance on intuition.

    Oktar, Kerem / Lerner, Adam / Malaviya, Maya / Lombrozo, Tania

    Cognition

    2023  Volume 236, Page(s) 105434

    Abstract: What changes people's judgments on moral issues, such as the ethics of abortion or eating meat? On some views, moral judgments result from deliberation, such that reasons and reasoning should be primary drivers of moral change. On other views, moral ... ...

    Abstract What changes people's judgments on moral issues, such as the ethics of abortion or eating meat? On some views, moral judgments result from deliberation, such that reasons and reasoning should be primary drivers of moral change. On other views, moral judgments reflect intuition, with reasons offered as post-hoc rationalizations. We test predictions of these accounts by investigating whether exposure to a moral philosophy course (vs. control courses) changes moral judgments, and if so, via what mechanism(s). In line with deliberative accounts of morality, we find that exposure to moral philosophy changes moral views. In line with intuitionist accounts, we find that the mechanism of change is reduced reliance on intuition, not increased reliance on deliberation; in fact, deliberation is related to increased confidence in judgments, not change. These findings suggest a new way to reconcile deliberative and intuitionist accounts: Exposure to reasons and evidence can change moral views, but primarily by discounting intuitions.
    MeSH term(s) Humans ; Intuition ; Morals ; Philosophy ; Problem Solving ; Judgment
    Language English
    Publishing date 2023-03-22
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1499940-7
    ISSN 1873-7838 ; 0010-0277
    ISSN (online) 1873-7838
    ISSN 0010-0277
    DOI 10.1016/j.cognition.2023.105434
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Getting aligned on representational alignment

    Sucholutsky, Ilia / Muttenthaler, Lukas / Weller, Adrian / Peng, Andi / Bobu, Andreea / Kim, Been / Love, Bradley C. / Grant, Erin / Groen, Iris / Achterberg, Jascha / Tenenbaum, Joshua B. / Collins, Katherine M. / Hermann, Katherine L. / Oktar, Kerem / Greff, Klaus / Hebart, Martin N. / Jacoby, Nori / Zhang, Qiuyi / Marjieh, Raja /
    Geirhos, Robert / Chen, Sherol / Kornblith, Simon / Rane, Sunayana / Konkle, Talia / O'Connell, Thomas P. / Unterthiner, Thomas / Lampinen, Andrew K. / Müller, Klaus-Robert / Toneva, Mariya / Griffiths, Thomas L.

    2023  

    Abstract: Biological and artificial information processing systems form representations that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the extent to which the representations formed by these diverse systems agree? ... ...

    Abstract Biological and artificial information processing systems form representations that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the extent to which the representations formed by these diverse systems agree? Do similarities in representations then translate into similar behavior? How can a system's representations be modified to better match those of another system? These questions pertaining to the study of representational alignment are at the heart of some of the most active research areas in cognitive science, neuroscience, and machine learning. For example, cognitive scientists measure the representational alignment of multiple individuals to identify shared cognitive priors, neuroscientists align fMRI responses from multiple individuals into a shared representational space for group-level analyses, and ML researchers distill knowledge from teacher models into student models by increasing their alignment. Unfortunately, there is limited knowledge transfer between research communities interested in representational alignment, so progress in one field often ends up being rediscovered independently in another. Thus, greater cross-field communication would be advantageous. To improve communication between these fields, we propose a unifying framework that can serve as a common language between researchers studying representational alignment. We survey the literature from all three fields and demonstrate how prior work fits into this framework. Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields. We hope that our work can catalyze cross-disciplinary collaboration and accelerate progress for all communities studying and developing information processing systems. We note that this is a working paper and encourage readers to reach out with their suggestions for future revisions.

    Comment: Working paper, changes to be made in upcoming revisions
    Keywords Quantitative Biology - Neurons and Cognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Computer Science - Neural and Evolutionary Computing
    Subject code 501
    Publishing date 2023-10-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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