Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance

Aurelio Cortese, Kaoru Amano, Ai Koizumi, Mitsuo Kawato () and Hakwan Lau ()
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Aurelio Cortese: ATR Computational Neuroscience Laboratories
Kaoru Amano: Center for Information and Neural Networks (CiNet), NICT
Ai Koizumi: ATR Computational Neuroscience Laboratories
Mitsuo Kawato: ATR Computational Neuroscience Laboratories
Hakwan Lau: UCLA

Nature Communications, 2016, vol. 7, issue 1, 1-18

Abstract: 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.

Date: 2016
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DOI: 10.1038/ncomms13669

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