Fosca Al Roumi

The paper shows that humans encode sequences with order-based structure as mental programs, improving recall accuracy and predicting motor production patterns.

This paper introduces a unifying framework for statistical learning across temporal scales, showing a single mechanism can capture both local and higher-order patterns in data.

This study investigates the neural mechanisms of geometric shape perception in humans using fMRI and MEG, finding distinct brain activation patterns and suggesting a symbolic mode of visual processing.

The paper investigates how humans process auditory sequences with network structures using MEG, finding rapid encoding of network structure and a long-horizon associative learning mechanism.

The paper uses brain imaging to show that human memory compresses binary sound sequences using recursive structures, with brain activity reflecting sequence complexity and prediction difficulty.

The paper shows humans use compressed network models to represent auditory sequences, pruning and completing relationships to efficiently capture both local and high-order structures.

The paper investigates how the human brain stores spatial sequences in working memory, finding that it compresses sequences using numerical and geometrical regularities rather than storing each item separately.