Vincent Michel

TL;DR: The findings demonstrate partial format invariance of individual number codes that is compatible with more numerous but more broadly tuned populations for nonsymbolic than for symbolic numbers, as postulated by recent computational models.

TL;DR: In this paper, the l1 norm of the image gradient is used as a regularization method for brain decoding, which can be applied to fMRI data for brain mapping and brain decoding.

TL;DR: In this article, the authors used multivariate pattern recognition on high-resolution functional imaging data to decode the information content of fine-scale signals evoked by different individual numbers, and demonstrated partial format invariance of individual number codes that is compatible with more numerous but more broadly tuned populations for nonsymbolic than for symbolic numbers, as postulated by recent computational models.

TL;DR: A method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging to predict the subject's behavior during a scanning session yields higher prediction accuracy than standard voxel-based approaches and infers an explicit weighting of the regions involved in the regression or classification task.

TL;DR: A sparse hierarchical structured regularization that encodes the spatial structure of the data at different scales into the regularization, which makes the overall prediction procedure more robust to inter-subject variability.