booktheque

Experienced Wholeness

Wanja Wiese · 2018

Where to buy

Amazon’s price changes daily, so the most accurate place to see it is there, at the moment you buy.

View on Amazon

This is an affiliate link: if you buy through it, the site earns a small commission at no extra cost to you, which helps us bring quality content and maintain the site. As an Amazon Associate we earn from qualifying purchases.

About the book

An interdisciplinary account of phenomenal unity, investigating how experiential wholes can be characterized and how such characterizations can be analyzed computationally.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.

Book details

Year
2018
Language
English
Publisher
The MIT Press
Editions
5
Pages
368
ISBN
9780262036993

Data source: Wikidata