Fabrice Luyckx

This study shows that both humans and recurrent neural networks develop aligned neural number lines to generalize magnitude concepts across contexts.

The paper tests a selective integration model of decision-making using EEG, finding that neural signals encode locally preferred attributes with higher gain.

The paper shows that human neural representations of novel visual stimuli align with magnitude-based mental number line representations during reward-guided learning.