An Automatic Domain-General Error Signal Is Shared across Tasks and Predicts Confidence in Different Sensory Modalities

Understanding the ability to self-evaluate decisions is an active area of research. This research has primarily focused on the neural correlates of self-evaluation during visual tasks and whether neural correlates before or after the primary decision contribute to self-reported confidence. This focus has been useful, yet the reliance on subjective confidence reports may confound our understanding of key everyday features of metacognitive self-evaluation: that decisions must be rapidly evaluated without explicit feedback and unfold in a multisensory world. These considerations led us to hypothesize that an automatic domain-general metacognitive signal may be shared between sensory modalities, which we tested in the present study with multivariate decoding of electroencephalographic (EEG) data. Participants (<i>N</i> = 21, 12 female) first performed a visual task with no request for self-evaluations of performance, prior to an auditory task that included rating decision confidence on each trial. A multivariate classifier trained to predict errors in the speeded visual task generalized to distinguish correct and error trials in the subsequent nonspeeded auditory discrimination. This generalization did not occur for classifiers trained on the visual stimulus-locked data and further predicted subjective confidence on the subsequent auditory task. This evidence of overlapping post-response neural activity provides evidence for automatic encoding of confidence independent of any explicit request for metacognitive reports and a shared basis for metacognitive evaluations across sensory modalities.

Authors

Institutions

Publication Details

Journal
eNeuro
Published
2025-05-22
DOI
https://doi.org/10.1523/eneuro.0124-25.2025
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Automatic Domain-General Error Signal Is Shared across Tasks and Predicts Confidence in Different Sensory Modalities

Nick Yeung, Sriraj Aiyer, Matthew Davidson
eNeuro
Neural dynamics and brain function
article

An Automatic Domain-General Error Signal Is Shared across Tasks and Predicts Confidence in Different Sensory Modalities

Nick Yeung, Sriraj Aiyer, Matthew Davidson
article en

Abstract

Understanding the ability to self-evaluate decisions is an active area of research. This research has primarily focused on the neural correlates of self-evaluation during visual tasks and whether neural correlates before or after the primary decision contribute to self-reported confidence. This focus has been useful, yet the reliance on subjective confidence reports may confound our understanding of key everyday features of metacognitive self-evaluation: that decisions must be rapidly evaluated without explicit feedback and unfold in a multisensory world. These considerations led us to hypothesize that an automatic domain-general metacognitive signal may be shared between sensory modalities, which we tested in the present study with multivariate decoding of electroencephalographic (EEG) data. Participants (<i>N</i> = 21, 12 female) first performed a visual task with no request for self-evaluations of performance, prior to an auditory task that included rating decision confidence on each trial. A multivariate classifier trained to predict errors in the speeded visual task generalized to distinguish correct and error trials in the subsequent nonspeeded auditory discrimination. This generalization did not occur for classifiers trained on the visual stimulus-locked data and further predicted subjective confidence on the subsequent auditory task. This evidence of overlapping post-response neural activity provides evidence for automatic encoding of confidence independent of any explicit request for metacognitive reports and a shared basis for metacognitive evaluations across sensory modalities.

eNeuroVol. 12(6)
University of Technology Sydney (AU), University of Oxford (GB)
Openalex Percentile: Top 89%
Neural dynamics and brain function
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

An Automatic Domain-General Error Signal Is Shared across Tasks and Predicts Confidence in Different Sensory Modalities — Nick Yeung, Sriraj Aiyer, et al. · eNeuro (2025) | TGRS Research Map | TGRS