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    Chapter 1 Affective Forecasting and Substantial Self-Knowledge

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    Author(s)
    Tooming, Uku
    Miyazono, Kengo
    Language
    English
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    Abstract
    1. This chapter argues that our self-knowledge is often mediated by our affective self-knowledge. In other words, we often know about ourselves by knowing our own emotions. More precisely, what Cassam has called “substantial self-knowledge” (SSK), such as self-knowledge of one's character, one's values, or one's aptitudes, is mediated by affective forecasting, which is the process of predicting one's emotional responses to possible situations. For instance, a person comes to know that she is courageous by predicting her own emotional reactions to possible risks and dangers. This hypothesis explains why attaining substantive self-knowledge tends to be difficult. Attaining substantive self-knowledge is difficult because, first, SSK is mediated by affective forecasting and, second, we tend to be wrong about predicting the intensity and duration of our own emotional reactions. As a result, we can identify what is common to central cases of SSK: such cases require knowledge about complex dispositions whose manifestations involve affective responses that one is not sufficiently familiar with. One thus needs to resort to the highly fallible method of affective forecasting.
    Book
    Emotional Self-Knowledge
    URI
    https://library.oapen.org/handle/20.500.12657/62291
    Keywords
    Alba Montes Sánchez and Alessandro Salice
    DOI
    10.4324/9781003310945-3
    ISBN
    9781032317106, 9781032317113, 9781003310945
    Publisher
    Taylor & Francis
    Publisher website
    https://taylorandfrancis.com/
    Publication date and place
    2023
    Imprint
    Routledge
    Classification
    Philosophy of mind
    Pages
    23
    Public remark
    Funder name: University of Tartu.
    Rights
    https://creativecommons.org/licenses/by-nc-nd/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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