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Experienced Wholeness:Integrating Insights from Gestalt Theory, Cognitive Neuroscience, and Predictive Processing '18

Wiese, Wanja  著

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価格 \12,746(税込)         

発行年月 2018年01月
出版社/提供元
出版国 アメリカ合衆国
言語 英語
媒体 冊子
装丁 hardcover
ページ数/巻数 368 p., 21 FIGURES
ジャンル 洋書/人文科学/認知科学/認知哲学
ISBN 9780262036993
商品コード 1024306387
本の性格 学術書
新刊案内掲載月 2017年11月
商品URL
参照
https://kw.maruzen.co.jp/ims/itemDetail.html?itmCd=1024306387

内容

How can we account for phenomenal unity? That is, how can we characterize and explain our experience of objects and groups of objects, bodily experiences, successions of events, and the attentional structure of consciousness as wholes? In this book, Wanja Wiese develops an interdisciplinary account of phenomenal unity, investigating how experiential wholes can be characterized and how such characterization can be analyzed conceptually as well as computationally. Wiese first addresses how the unity of consciousness can be characterized phenomenologically, discussing what it is like to experience wholes and what is the experiential contribution of phenomenal unity. Considering the associated conceptual and empirical issues, he draws connections to phenomenological accounts and research on Gestalt theory. The results show how the attentional structure of experience, the experience of temporal flow, and different types of experiential wholes contribute to our sense of phenomenal unity. Moreover, characterizing phenomenal unity in terms of the existence of a single global phenomenal state is neither necessary nor sufficient to adequately address the problem of phenomenal unity. Wiese then suggests that the concepts and ideas of predictive processing can be used to analyze phenomenal unity computationally. The result is both a conceptual framework and an interdisciplinary account: the regularity account of phenomenal unity. According to this account, experienced wholes correspond to a hierarchy of connecting regularities. The brain tracks these regularities by hierarchical prediction error minimization, which approximates hierarchical Bayesian inference.

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