PhD Candidates of the Max Planck School of Cognition

Research interests: • mathematical modelling of learning and decision making • representations of knowledge in the brain

Research interests: • deep learning and its interpretability • natural language processing • computational methods in sciences • knowledge acquisition and sampling-based decision-marking • data visualisation

Research interests: • human cognitive neuroscience • functional connectivity and representational geometry • functional neuroimaging • reproducibility, transparency, and scientific best practice

Research interests: • auditory pattern learning • audiovisual attentional processing (especially involuntary mechanisms) • EEG and eytracking

Research interests: • philosophy of cognitive science • rationality and decision making • animal cognition • moral philosophy and moral psychology • philosophical methodology

Research interests: • embodied emotion • emotion perception • emotion regulation/mental training • interoception/interoceptive awareness • virtual reality

Research interests: • challenging the mechanistic account of explanation as the only adequate account of explanation in neuroscience • emergentism • causation in the mind/brain system • cognitive ontology • human cognitive and moral enhancement

Research interests: • neurophysiological basis of conscious experience • neural and cognitive mechanisms of voluntary action • multivariate neuroimaging and brain-computer interfacing • open science

Research interests: • metacognition, or how we monitor and asses our cognitive performance and abilities • mechanisms behind failures of metacognition, such as illusions of knowledge or depth of understanding

Research interests: • analysis of large-scale brain networks using fMRI and EEG • organization of structure knowledge for adaptive behaviour, statistical learning • hippocampal pattern completion, pattern separation, and replay • sensory integration and time perception • multivariate pattern analysis of neuroimaging data, brain decoding methods

Research interests: • biologically plausible learning and plasticity in artificial neural networks • early neural development and self-organising neural circuits • computational psychiatry and neurology: neuro-cognitive models and reinforcement learning

Research interests: • neural substrates of (object) recognition and categorisation • organization of cortical knowledge representations (“cognitive maps”) • “Mind-reading”: Decoding of neural signals • disturbances of human object recognition and categorisation (e.g. in dementia) • functional brain imaging • diverse methodological approaches to data analysis