Literature DB >> 28916521

Behavioral, Modeling, and Electrophysiological Evidence for Supramodality in Human Metacognition.

Nathan Faivre1,2,3, Elisa Filevich4,5,6, Guillermo Solovey7,8, Simone Kühn4,9, Olaf Blanke10,2,11.   

Abstract

Human metacognition, or the capacity to introspect on one's own mental states, has been mostly characterized through confidence reports in visual tasks. A pressing question is to what extent results from visual studies generalize to other domains. Answering this question allows determining whether metacognition operates through shared, supramodal mechanisms or through idiosyncratic, modality-specific mechanisms. Here, we report three new lines of evidence for decisional and postdecisional mechanisms arguing for the supramodality of metacognition. First, metacognitive efficiency correlated among auditory, tactile, visual, and audiovisual tasks. Second, confidence in an audiovisual task was best modeled using supramodal formats based on integrated representations of auditory and visual signals. Third, confidence in correct responses involved similar electrophysiological markers for visual and audiovisual tasks that are associated with motor preparation preceding the perceptual judgment. We conclude that the supramodality of metacognition relies on supramodal confidence estimates and decisional signals that are shared across sensory modalities.SIGNIFICANCE STATEMENT Metacognitive monitoring is the capacity to access, report, and regulate one's own mental states. In perception, this allows rating our confidence in what we have seen, heard, or touched. Although metacognitive monitoring can operate on different cognitive domains, we ignore whether it involves a single supramodal mechanism common to multiple cognitive domains or modality-specific mechanisms idiosyncratic to each domain. Here, we bring evidence in favor of the supramodality hypothesis by showing that participants with high metacognitive performance in one modality are likely to perform well in other modalities. Based on computational modeling and electrophysiology, we propose that supramodality can be explained by the existence of supramodal confidence estimates and by the influence of decisional cues on confidence estimates.
Copyright © 2018 the authors 0270-6474/18/380263-15$15.00/0.

Entities:  

Keywords:  EEG; audiovisual; confidence; metacognition; signal detection theory; supramodality

Mesh:

Year:  2017        PMID: 28916521      PMCID: PMC6596112          DOI: 10.1523/JNEUROSCI.0322-17.2017

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  21 in total

1.  I know that "Kiki" is angular: The metacognition underlying sound-shape correspondences.

Authors:  Yi-Chuan Chen; Pi-Chun Huang; Andy Woods; Charles Spence
Journal:  Psychon Bull Rev       Date:  2019-02

2.  Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging.

Authors:  Michael Pereira; Nathan Faivre; Iñaki Iturrate; Marco Wirthlin; Luana Serafini; Stéphanie Martin; Arnaud Desvachez; Olaf Blanke; Dimitri Van De Ville; José Del R Millán
Journal:  Proc Natl Acad Sci U S A       Date:  2020-04-01       Impact factor: 11.205

3.  Response-Related Signals Increase Confidence But Not Metacognitive Performance.

Authors:  Elisa Filevich; Christina Koß; Nathan Faivre
Journal:  eNeuro       Date:  2020-05-20

4.  Causal Evidence for Mnemonic Metacognition in Human Precuneus.

Authors:  Qun Ye; Futing Zou; Hakwan Lau; Yi Hu; Sze Chai Kwok
Journal:  J Neurosci       Date:  2018-06-19       Impact factor: 6.167

Review 5.  Visual metacognition: Measures, models, and neural correlates.

Authors:  Dobromir Rahnev
Journal:  Am Psychol       Date:  2021-12

6.  Task-Specific Neural Representations of Generalizable Metacognitive Control Signals in the Human Dorsal Anterior Cingulate Cortex.

Authors:  Jie Su; Wenbin Jia; Xiaohong Wan
Journal:  J Neurosci       Date:  2021-12-14       Impact factor: 6.709

7.  Performance monitoring for sensorimotor confidence: A visuomotor tracking study.

Authors:  Shannon M Locke; Pascal Mamassian; Michael S Landy
Journal:  Cognition       Date:  2020-08-05

Review 8.  Sources of Metacognitive Inefficiency.

Authors:  Medha Shekhar; Dobromir Rahnev
Journal:  Trends Cogn Sci       Date:  2020-11-16       Impact factor: 20.229

9.  Domain-General and Domain-Specific Patterns of Activity Supporting Metacognition in Human Prefrontal Cortex.

Authors:  Jorge Morales; Hakwan Lau; Stephen M Fleming
Journal:  J Neurosci       Date:  2018-03-08       Impact factor: 6.167

10.  Is there a G factor for metacognition? Correlations in retrospective metacognitive sensitivity across tasks.

Authors:  Audrey Mazancieux; Stephen M Fleming; Céline Souchay; Chris J A Moulin
Journal:  J Exp Psychol Gen       Date:  2020-03-19
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