Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.04.21.537831

Two distinct stimulus-locked EEG signatures reliably encode domain-general confidence during decision formation

Kopcanova, M.·Ince, R. A.·Benwell, C. S.
View original source ↗Export metadata ↓

Abstract

Decision confidence, an internal estimate of how accurate our choices are, is essential for metacognitive self-evaluation and guides behaviour. However, it can be suboptimal and hence understanding the underlying neurocomputational mechanisms is crucial. To do so, it is essential to establish the extent to which both behavioural and neurophysiological measures of metacognition are reliable over time and shared across cognitive domains. The evidence regarding domain-generality of metacognition has been mixed, while the test-retest reliability of the most widely used metacognitive measures has not been reported. Here, in human participants of both sexes, we examined behavioural and electroencephalographic (EEG) measures of metacognition across two tasks that engage distinct cognitive domains - visual perception and semantic memory. The test-retest reliability of all measures was additionally tested across two experimental sessions. The results revealed a dissociation between metacognitive bias and efficiency, whereby only metacognitive bias showed strong test-retest reliability and domain-generality whilst metacognitive efficiency (measured by M-ratio) was neither reliable nor domain-general. Hence, overall confidence calibration (i.e., metacognitive bias) is a stable trait-like characteristic underpinned by domain-general mechanisms whilst metacognitive efficiency may rely on more domain-specific computations. Additionally, we found two distinct stimulus-locked EEG signatures related to the trial-by-trial fluctuations in confidence ratings during decision formation. A late event-related potential was reliably linked to confidence across cognitive domains, while evoked spectral power predicted confidence most reliably in the semantic knowledge domain. Establishing the reliability and domain-generality of neural predictors of confidence represents an important step in advancing our understanding of the mechanisms underlying self-evaluation. Significance StatementUnderstanding the mechanisms underlying metacognition is essential for addressing deficits in self-evaluation. Open questions exist regarding the domain-generality and reliability of both behavioural and neural measures of metacognition. We show that metacognitive bias is reliable across cognitive domains and time, whereas the most adopted measure of metacognitive efficiency is domain-specific and shows poor test-retest reliability. Hence, more reliable measures of metacognition, tailored to specific domains, are needed. We further show that decision confidence is linked to two EEG signatures: late event-related potentials and evoked alpha/beta spectral power. While the former predicts confidence in both perception and semantic knowledge domains, the latter is only reliably linked to knowledge confidence. These findings provide crucial insights into the computations underlying metacognition across domains.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kopcanova, M., Ince, R. A., Benwell, C. S.. 2023-04-22. Two distinct stimulus-locked EEG signatures reliably encode domain-general confidence during decision formation. https://doi.org/10.1101/2023.04.21.537831

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗