KongFatt Wong‐Lin

The paper identifies a supramodal brain network involved in perceptual decision formation and links it to the EEG centroparietal positivity (CPP) signal using fMRI and EEG data.

The paper identifies robust low-dimensional neural latent representations of implicit perceptual decision confidence using brain-wide iEEG data without explicit confidence reports.

The paper introduces a decision confidence neuron for ESNs to continuously monitor classification confidence in EEG motor imagery tasks, showing it correlates with decision accuracy and discriminates correct from error decisions.

This study shows that variational Laplace-based DCM struggles to accurately estimate connectivity parameters in multistable neural systems with complex dynamics.

This study shows how neural oscillations modulate decision confidence, speed, and accuracy by altering synaptic time scales and oscillatory phase, challenging the speed-accuracy trade-off.

This paper introduces a minimal mean-field neural circuit model that explains flexible perceptual decisions by integrating sensory evidence and action selection, replicating parietal cortical activity and decision dynamics.

This study shows that non-negative connections in neural networks lead to bow-tie architecture, which is efficient, robust, and generalizable.

The paper uses fMRI-informed EEG and DCM to show that decision confidence and speed are encoded by distinct neural networks with nonlinear dynamics across cortical layers.

This paper computationally models neuromodulator circuits to show that multiple stable architectures can produce similar network activity, demonstrating degeneracy in dopamine and serotonin systems.

This study compares neural responses to auditory stimuli between musicians and non-musicians using gamma-band entrainment, finding musicians show greater top-down control and non-musicians benefit from entrained gamma responses.

The paper demonstrates that changes-of-mind can occur without new post-decision evidence and links them to prolonged response times and high decision uncertainty.

This paper introduces a cluster-permutation statistical method for high-dimensional MEG-based functional connectivity analysis, identifying hypersynchronization in MCI patients.

This paper proposes a neuronal circuit model that computes decision uncertainty and explains its role in change-of-mind using behavioural and neural data.

This paper develops a computerized decision support system for predicting Alzheimer’s disease severity using cognitive and clinical measures, showing improved accuracy over biomarker-based models.