Bati Yilmaz

RSA is a framework to relate representations across different measurement and signal spaces, such as those defined by different brain imaging modalities (EEG and fMRI) or computational models [43,65]. The idea is to abstract from incommensurate measurement spaces into a common similarity space where representations can be directly compared. For each masking condition, the analysis proceeded in two steps.

Abstract Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results.