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Experienced Wholeness - Integrating Insights from Gestalt Theory, Cognitive Neuroscience, and Predictive Processing (Hardcover)
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Experienced Wholeness - Integrating Insights from Gestalt Theory, Cognitive Neuroscience, and Predictive Processing (Hardcover)
Series: The MIT Press
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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. 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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