Clark, A;
Friston, K;
Wilkinson, S;
(2019)
Bayesing Qualia Consciousness as Inference, Not Raw Datum.
Journal of Consciousness Studies
, 26
(9-10)
pp. 19-33.
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Abstract
The meta-problem of consciousness (Chalmers, 2018) is the problem of explaining the behaviours and verbal reports that we associate with the so-called 'hard problem of consciousness'. These may include reports of puzzlement, of the attractiveness of dualism, of explanatory gaps, and the like. We present and defend a solution to the meta-problem. Our solution takes as its starting point the emerging picture of the brain as a hierarchical inference engine. We show why such a device, operating under familiar forms of adaptive pressure, may come to represent some of its mid-level inferences as especially certain. These mid-level states confidently re-code raw sensory stimulation in ways that (they are able to realize) fall short of fully determining how properties and states of affairs are arranged in the distal world. This drives a wedge between experience and the world. Advanced agents then represent these mid-level inferences as irreducibly special, becoming increasingly puzzled as a result.
Type: | Article |
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Title: | Bayesing Qualia Consciousness as Inference, Not Raw Datum |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://www.ingentaconnect.com/contentone/imp/jcs/... |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience |
URI: | https://discovery.ucl.ac.uk/id/eprint/10085952 |
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