Jona Sassenhagen

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.

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.

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.