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Effective connectivity predicts distributed neural coding of perceptual decision confidence, uncertainty and speed

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Abstract

Decision-making is often accompanied by a level of confidence regarding the accuracy of one's decision. Previous studies have indicated neural activity associated with perceptual decision confidence during sensory stimulus presentation. Choice-based reaction time (RT) has been suggested as an indirect but more objective measure of decision confidence – generally faster RT for higher confidence. However, it is unclear whether choice confidence and RT are mediated by distinct neural pathways, and whether their neural correlates are encoded nonlinearly. Within a perceptual decision-making task, we applied fMRI-informed EEG-based effective connectivity analysis via dynamic causal modelling (DCM) on event-related potentials and found the frontoparietal network for fast-vs-slow RT condition to be different from that of high-vs-low confidence rating condition. Furthermore, trial-by-trial DCM analysis predicted cortical layer-based, distributed and nonlinear coding of RT, confidence or uncertainty. Collectively, our study suggests that decision confidence and speed are instantiated by different dynamical networks distributed across cortical layers.
Original languageEnglish
Article numberimag_a_00441
Pages (from-to)1-18
Number of pages18
JournalImaging Neuroscience
Volume3
Early online date2 Jan 2025
DOIs
Publication statusPublished (in print/issue) - 21 Jan 2025

Bibliographical note

Publisher Copyright:
© 2025 The Author. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Data Availability Statement

Source codes on subset of processed data (for demonstrative purposes) used in our analysis are available at https://github.com/asadpouretal/DCM-confidence. Original data are available in the open dataset (Gherman & Philiastides, 2020).

Funding

We thank Brendan Lenfesty for providing constructive feedback on the manuscript, and Saugat Bhattachayya for helpful discussion on the regression method. We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/T022175/1. A.A. and K.W.-L. were supported by HSC R&D (STL/5540/19) and MRC (MC_OC_20020).

FundersFunder number
HSC R&DSTL/5540/19
UK MRCMC_OC_20020
Medical Research CouncilMC_OC_20020
Engineering and Physical Sciences Research CouncilEP/T022175/1

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being
    2. SDG 9 - Industry, Innovation, and Infrastructure
      SDG 9 Industry, Innovation, and Infrastructure

    Keywords

    • perceptual decision confidence
    • reaction time
    • dynamic causal modelling DCM
    • Bayesian model selection
    • event-related potentials ERP

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