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Dolega, Krzysztof; Dewhurst, Joe E. (2020): Fame in the predictive brain: a deflationary approach to explaining consciousness in the prediction error minimization framework. In: Synthese, Vol. 198, No. 8: pp. 7781-7806
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The proposal that probabilistic inference and unconscious hypothesis testing are central to information processing in the brain has been steadily gaining ground in cognitive neuroscience and associated fields. One popular version of this proposal is the new theoretical framework of predictive processing or prediction error minimization (PEM), which couples unconscious hypothesis testing with the idea of 'active inference' and claims to offer a unified account of perception and action. Here we will consider one outstanding issue that still looms large at the core of the PEM framework: the lack of a clear criterion for distinguishing conscious states from unconscious ones. In order to fulfill the promise of becoming a unifying framework for describing and modeling cognition, PEM needs to be able to differentiate between conscious and unconscious mental states or processes. We will argue that one currently popular view, that the contents of conscious experience are determined by the 'winning hypothesis' (i.e. the one with the highest posterior probability, which determines the behavior of the system), falls short of fully accounting for conscious experience. It ignores the possibility that some states of a system can control that system's behavior even though they are apparently not conscious (as evidenced by e.g. blindsight or subliminal priming). What follows from this is that the 'winning hypothesis' view does not provide a complete account of the difference between conscious and unconscious states in the probabilistic brain. We show how this problem (and some other related problems) for the received view can be resolved by augmenting PEM with Daniel Dennett's multiple drafts model of consciousness. This move is warranted by the similar roles that attention and internal competition play in both the PEM framework and the multiple drafts model.