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      Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance

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          Abstract

          A central controversy in metacognition studies concerns whether subjective confidence directly reflects the reliability of perceptual or cognitive processes, as suggested by normative models based on the assumption that neural computations are generally optimal. This view enjoys popularity in the computational and animal literatures, but it has also been suggested that confidence may depend on a late-stage estimation dissociable from perceptual processes. Yet, at least in humans, experimental tools have lacked the power to resolve these issues convincingly. Here, we overcome this difficulty by using the recently developed method of decoded neurofeedback (DecNef) to systematically manipulate multivoxel correlates of confidence in a frontoparietal network. Here we report that bi-directional changes in confidence do not affect perceptual accuracy. Further psychophysical analyses rule out accounts based on simple shifts in reporting strategy. Our results provide clear neuroscientific evidence for the systematic dissociation between confidence and perceptual performance, and thereby challenge current theoretical thinking.

          Abstract

          Confidence associated with perceptual judgements is generally seen as directly reflecting the reliability of perceptual processes. Here the authors use fMRI-based decoded neurofeedback to manipulate confidence and show that it does not affect perceptual performance.

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          Most cited references45

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          Neural correlates of consciousness: progress and problems.

          There have been a number of advances in the search for the neural correlates of consciousness--the minimum neural mechanisms sufficient for any one specific conscious percept. In this Review, we describe recent findings showing that the anatomical neural correlates of consciousness are primarily localized to a posterior cortical hot zone that includes sensory areas, rather than to a fronto-parietal network involved in task monitoring and reporting. We also discuss some candidate neurophysiological markers of consciousness that have proved illusory, and measures of differentiation and integration of neural activity that offer more promising quantitative indices of consciousness.
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            A framework for consciousness.

            Here we summarize our present approach to the problem of consciousness. After an introduction outlining our general strategy, we describe what is meant by the term 'framework' and set it out under ten headings. This framework offers a coherent scheme for explaining the neural correlates of (visual) consciousness in terms of competing cellular assemblies. Most of the ideas we favor have been suggested before, but their combination is original. We also outline some general experimental approaches to the problem and, finally, acknowledge some relevant aspects of the brain that have been left out of the proposed framework.
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              Neural correlates, computation and behavioural impact of decision confidence.

              Humans and other animals must often make decisions on the basis of imperfect evidence. Statisticians use measures such as P values to assign degrees of confidence to propositions, but little is known about how the brain computes confidence estimates about decisions. We explored this issue using behavioural analysis and neural recordings in rats in combination with computational modelling. Subjects were trained to perform an odour categorization task that allowed decision confidence to be manipulated by varying the distance of the test stimulus to the category boundary. To understand how confidence could be computed along with the choice itself, using standard models of decision-making, we defined a simple measure that quantified the quality of the evidence contributing to a particular decision. Here we show that the firing rates of many single neurons in the orbitofrontal cortex match closely to the predictions of confidence models and cannot be readily explained by alternative mechanisms, such as learning stimulus-outcome associations. Moreover, when tested using a delayed reward version of the task, we found that rats' willingness to wait for rewards increased with confidence, as predicted by the theoretical model. These results indicate that confidence estimates, previously suggested to require 'metacognition' and conscious awareness are available even in the rodent brain, can be computed with relatively simple operations, and can drive adaptive behaviour. We suggest that confidence estimation may be a fundamental and ubiquitous component of decision-making.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                15 December 2016
                2016
                : 7
                : 13669
                Affiliations
                [1 ]Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories , 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
                [2 ]Faculty of Information Science, Nara Institute of Science and Technology , 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
                [3 ]Center for Information and Neural Networks (CiNet), NICT , 1-4 Yamadaoka, Suita City, Osaka 565-0871, Japan
                [4 ]Department of Psychology, UCLA , Franz Hall, 502 Portola Plaza, Los Angeles, California 90095, USA
                [5 ]Brain Research Institute, UCLA , 695 Charles E Young Dr S, Los Angeles, California 90095, USA
                Author notes
                [*]

                These authors contributed equally to this work

                Author information
                http://orcid.org/0000-0003-4567-0924
                Article
                ncomms13669
                10.1038/ncomms13669
                5171844
                27976739
                c7134696-fb12-4539-82e3-abe0aea27941
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 07 April 2016
                : 21 October 2016
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