
Siddharth Talwar- PhD Student at Catholic University of Louvain
Siddharth Talwar
- PhD Student at Catholic University of Louvain
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13
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Publications
Publications (13)
Brain oscillations from EEG and MEG shed light on neurophysiological mechanisms of human behavior. However, to extract information on cortical processing, researchers have to rely on source localization methods that can be very broadly classified into current density estimates such as exact low-resolution brain electromagnetic tomography (eLORETA),...
Perception necessitates interaction amongst neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Here, we propose a detailed neurobiologically realistic model that captures the neural mechanisms of inter‐individual variability observed in cross‐modal speech perception. From raw EEG s...
Voices are arguably among the most relevant sounds in humans' everyday life, and several studies have suggested the existence of voice-selective regions in the human brain. Despite two decades of research, defining the human brain regions supporting voice recognition remains challenging. Moreover, whether neural selectivity to voices is merely driv...
Elicited upon violation of regularity in stimulus presentation, mismatch negativity (MMN) reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection whereas P300 is associated with cognitive processes such as updating of the working memory. To date,...
Voices are the most relevant social sounds for humans and therefore have crucial adaptive value in development. Neuroimaging studies in adults have demonstrated the existence of regions in the superior temporal sulcus that respond preferentially to voices. Yet, whether voices represent a functionally specific category in the young infant’s mind is...
Seamlessly extracting emotional information from voices is crucial for efficient interpersonal communication. However, it remains unclear how the brain categorizes vocal expressions of emotion beyond the processing of their acoustic features. In our study, we developed a new approach combining electroencephalographic recordings (EEG) in humans with...
Seamlessly extracting emotional information from voices is crucial for efficient interpersonal communication. However, it remains unclear how the brain categorizes vocal expressions of emotion beyond the processing of their acoustic features. In our study, we developed a new approach combining electroencephalographic recordings (EEG) in humans with...
Prediction errors in the brain are indexed by two event-related potentials- MMN and P300, which are elicited upon violation of regularity in the occurrence of repetitive stimuli. While MMN reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection,...
Voices are arguably among the most relevant sounds in humans’ everyday life, and several studies have suggested the existence of voice-selective regions in the human brain. Despite two decades of research, defining the human brain regions supporting voice recognition remains challenging. Moreover, whether neural selectivity to voices is merely driv...
Perception necessitates interaction amongst neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Likewise, the variability in perception can be explained to emerge from the modulation of interactions within the underlying neural dynamical system. Employing this framework, we explain...
Oscillatory brain electromagnetic activity is an established tool to study neurophysiological mechanisms of human behavior using electro-encephalogram (EEG) and magneto-encephalogram (MEG) techniques. Often, to extract source level information in the cortex, researchers have to rely on inverse techniques that generate probabilistic estimation of th...
A widely used experimental design in multisensory integration is the McGurk paradigm that entail illusory (cross-modal) perception of speech sounds when presented with incongruent audio-visual (AV) stimuli. However, the distribution of responses across trials and individuals is heterogeneous and not necessarily everyone in a given group of individu...