Robin A. A. Ince

The paper shows that thalamic and cortical signals work together to represent auditory prediction errors, with synergistic interactions that go beyond simple relay.

This study investigates how attention modulates prediction-error (PE) encoding in thalamic and cortical circuits during learning, revealing distinct informational dynamics and reorganization of PE representations.

The paper investigates the roles of broadband and narrowband gamma oscillations in visual information encoding using ECoG and LFP data from humans and macaques, finding broadband signals to be synergistic and narrowband gamma to be redundant.

The paper shows how task demands dynamically structure feature selection, routing, and integration in the human brain using dynamic faces and MEG data.

The paper shows that beta responses to naturalistic speech can be explained by a simple acoustic model, suggesting a domain-general temporal forecasting function rather than a linguistic one.

This paper shows that hippocampal theta oscillations can be detected and tracked non-invasively using MEG, validated across rodent and human datasets.

The paper identifies a functional social pathway in the brain that computes dynamic facial movements for emotion categorization using MEG data and representational interaction measures.

The paper investigates whether cortical interactions during prediction error processing are synergistic or redundant, finding that both ERPs and broadband signals show high synergy across the cortical hierarchy.

The paper examines how choice history influences perceptual and metacognitive decision-making, finding dissociations that challenge existing theories of confidence.

The paper uses eye-movement analysis and information theory to separate perception from report in bistable visual stimuli, showing distinct neural mechanisms for integration and differentiation.

The paper shows that the strength of predicted category features biases decision behavior by using classifiers to analyze reactivation of specific stimulus features.

The paper examines behavioural and EEG measures of metacognition across two cognitive domains, finding that metacognitive bias is reliable and domain-general, while efficiency is not, and identifying two EEG signatures linked to confidence.

This study shows that top-down predictions of visual features reverse bottom-up processing in the occipito-ventral pathway, improving categorization speed and visual representation.

The paper shows that top-down predictions from temporal to occipital cortex sharpen visual feature representations for faster categorization.

The paper investigates how delta and theta brain oscillations contribute to semantic processing during audiovisual speech perception, finding distinct roles for each band in integrating auditory and visual inputs.

The paper introduces a non-parametric permutation-based framework for group-level analysis of cognitive brain networks using neurophysiological data and presents an open-source Python toolbox called Frites.

The paper shows how four stages of task-specific computations over the same visual inputs achieve different behaviors in dynamic brain networks.

This paper introduces a Bayesian method to estimate population prevalence from individual participant data, offering a quantitative estimate with uncertainty instead of binary inferences.

Tensorpac provides an open-source Python toolbox for efficient and comprehensive phase-amplitude coupling analysis in electrophysiological brain signals.

The paper examines how speech perception in noise relates to reading strategies in children, finding distinct neural correlates for lexical and sublexical reading.

This study uses a reverse correlation paradigm to show that visual information processed at different times during a fixation affects neural responses and behavior, with some differences linked to the N170 ERP component.

The paper introduces a framework called Stimulus Information Representation (SIR) to understand how brain activity reflects information processing during tasks.

The paper shows that acoustic features can explain speech processing results previously attributed to linguistic features, suggesting syllables may be better intermediate representations than phonemes.

The paper investigates how ageing delays neural processing of face features, showing a 40 ms delay in occipital-temporal pathway activity that correlates with slower behavioural responses.

The paper uses an information theoretic framework to show how the brain reduces high-dimensional visual input into task-relevant representations in the right fusiform gyrus to support perceptual decisions.

This study investigates the neural correlates of an auditory pitch-visual size cross-modal association using EEG and finds early neural representations and congruency effects in sensory processing.

The paper identifies two distinct computational mechanisms in different brain regions that support audiovisual speech comprehension: redundancy in the left posterior superior temporal gyrus and synergy in the left motor cortex.

The paper investigates how local speech encoding and functional connectivity contribute to audio-visual speech integration using MEG data, finding that enhanced connectivity, not local encoding, predicts the benefit of visual speech cues.

The paper introduces a statistical framework for neuroimaging data analysis using mutual information estimated via a Gaussian copula, validated on EEG and MEG data with applications to various signal features.

The paper shows the N170 ERP reflects cross-hemispheric transfer of visual features, not just local processing.