Sebastian Bobadilla-Suarez

The paper shows that brain regions and deep learning models stretch representations along task-relevant dimensions to optimize performance through error minimization.

The paper shows that subjective value and decision entropy are encoded by aligned gradients across the human brain, with value areas more strongly signaling decision entropy than value itself.

The paper shows that different analysis workflows by 70 teams on the same fMRI dataset led to varied results, but meta-analysis found consensus in activated regions.

The paper evaluates neural similarity measures across fMRI datasets, finding that alternatives to Pearson correlation outperform it in capturing similarity across tasks.