Fosca Al Roumi

Humans compress order-based structure into short mental programs

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

Long-Horizon associative learning as a unifying framework for statistical learning across scales

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.

A geometric shape regularity effect in the human brain

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.

Long-horizon associative learning explains human sensitivity to statistical and network structures in auditory sequences

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.

Brain-imaging evidence for compression of binary sound sequences in human memory

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.

Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure

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.

Mental compression of spatial sequences in human working memory using numerical and geometrical primitives

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.