Christian J. Fiebach

This paper proposes optimal transport theory as a framework for modeling sub-letter orthographic processing, showing it aligns with early neural activity and captures geometric invariances better than existing methods.

This study uses a computational model to show that orthographic processing in humans, baboons, and pigeons varies with phylogenetic distance, with humans and baboons using more similar representations.

The paper shows that 16 time frames of high brain connectivity can predict intelligence, but network-defining states do not, and results replicate in an independent sample.

The paper proposes a computational model of left-ventral occipito-temporal cortex activation in reading, showing it predicts brain activity better than alternatives and links word-likeness and lexical categorization to reading efficiency.

The paper shows that eye movement sampling rates during reading align with speech production rates across languages, with variations linked to writing system complexity.

This study investigates the association between brain network dynamics and psychological resilience using resting-state fMRI, finding no substantial correlation between resilience and node flexibility.

The paper investigates how predictive contexts affect visual word recognition by examining pre-activation of orthographic and lexical-semantic representations using behavioral and neuroimaging data.

The paper demonstrates that prior visual-orthographic knowledge optimizes visual word representations, improving reading efficiency by focusing on relevant visual information.

This study investigates whether distributional word vectors can model brain activity during word processing without context, finding they predict neural patterns and outperform a taxonomic baseline.

The paper shows that P3 and P600 brain responses share neural patterns, challenging the view of P600 as language-specific and supporting a domain-general interpretation.

This paper investigates context-based facilitation in visual word recognition, finding facilitation at visual and lexical levels but not pre-lexical or top-down.

The paper examines associations between ADHD symptoms and brain network structure in healthy adults, finding significant links between symptoms and connectivity patterns in key brain networks.

The paper uses EEG to show that semantic word categories can be decoded from brain activity before the word is presented, supporting predictive coding during sentence processing.