biostudies-literatureSchutt HHDeutsche Forschungsgemeinschafte82566https://www.ebi.ac.uk/biostudies/studies/S-EPMC10446828biostudies-literatureUnknown12Neuroscience has recently made much progress, expanding the complexity of both neural activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison witheLifeStatistical inference on representational geometries.PMC10446828Forschungsstipendium SCHU 3351/1-1Kriegeskorte NSchutt HHDiedrichsen JKipnis ADfalseStatistical inference on representational geometries.Neuroscience has recently made much progress, expanding the complexity of both neural activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with2023-01-01T00:00:00Z2023 Aug2025-04-05T15:47:18.305Z2025-04-05T15:47:18.305ZS-EPMC104468283761030210.7554/eLife.8256610.7554/elife.82566