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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
        9781003310945, 9781032317106, 9781032317113
        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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        • 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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