Affective experience in the predictive mind: a review and new integrative account.
This paper aims to offer an account of affective experiences within Predictive Processing, a novel framework that considers the brain to be a dynamical, hierarchical, Bayesian hypothesis-testing mechanism. We begin by outlining a set of common features of affective experiences (or feelings) that a P...
| Publicado en: | Synthese Vol. 198; no. 11; pp. 10847 - 10883 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Nov2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=152624456&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 152624456 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Nov2021 vid: 198 iid: 11 pid: 237 pub: Springer Nature artinfo: ui: 152624456 10.1007/s11229-020-02755-4 ppf: 10847 ppct: 36 formats: fmt: @attributes: type: P size: 456KB tig: atl: Affective experience in the predictive mind: a review and new integrative account. aug: au: Fernandez Velasco, Pablo Loev, Slawa affil: Institut Jean Nicod, Département d'Études Cognitives (DEC), ENS, EHESS, CNRS, PSL University, 29 rue d'Ulm, 75005, Paris, France Munich Center for Neuroscience, Ludwig Maximilian University, Munich, Germany Faculty of Philosophy, Ludwig Maximilian University, Munich, Germany su: Affect (Psychology) Error rates Error analysis in mathematics Prediction models Emotions sug: subj: Affect (Psychology) Error rates Error analysis in mathematics Prediction models Emotions keyword: Affective experience Affordances Emotion Feelings Predictive processing Valence ab: This paper aims to offer an account of affective experiences within Predictive Processing, a novel framework that considers the brain to be a dynamical, hierarchical, Bayesian hypothesis-testing mechanism. We begin by outlining a set of common features of affective experiences (or feelings) that a PP-theory should aim to explain: feelings are conscious, they have valence, they motivate behaviour, and they are intentional states with particular and formal objects. We then review existing theories of affective experiences within Predictive Processing and delineate two families of theories: Interoceptive Inference Theories (which state that feelings are determined by interoceptive predictions) and Error Dynamics Theories (which state that feelings are determined by properties of error dynamics). We highlight the strengths and shortcomings of each family of theories and develop a synthesis: the Affective Inference Theory. Affective Inference Theory claims that valence corresponds to the expected rate of prediction error reduction. In turn, the particular object of a feeling is the object predicted to be the most likely cause of expected changes in prediction error rate, and the formal object of a feeling is a predictive model of the expected changes in prediction error rate caused by a given particular object. Finally, our theory shows how affective experiences bias action selection, directing the organism towards allostasis and towards optimal levels of uncertainty in order to minimise prediction error over time. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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