6. Lastly, we found reliable results across two different populations from the US and Mexico. We also computed our data across different distance metrics, correlation methods, and statistical methods. so we are looking forward to feedback! To learn more, check out our preprint!😊

5. Finally, we trained CNNs and algorithmic models on our image dataset (with 40 categories) on object-based properties, and we found that only supervised and models with high performance correlate significantly with object-based and neural spaces.

4. When looking at their 2nd-level bimodal correlations, we found that only object-based properties correlate significantly with neural regions (being more strongly correlated with visual areas than associative areas). Our behavioral and bimodal results were supported by MDS.

3. We computed traditional noise ceilings across brain regions, but also added analyses to compute noise ceilings per ROI, which we believe may be a more informative way of contrasting explainable variance across models to show how it relates to bimodal correlations :)

2. Neurally, we evaluated 11 ROIs between visual and pre-frontal regions to compare the granularity of their representational spaces, and show lower across-subjects variance in visual regions compared to prefrontal areas, which is an important factor for bimodal correlations ;)

1. By collecting similarity jugments for multiple object properties (e.g. category, shape), subjective feelings (scariness and preference), and more abstract ratings (general similarity), we identified what we call an "object-based" space and a "subject-based" space.

Happy to share my first-ever pre-print !!!! 🚨 on the representational geometry across similarity properties using behavioral judgments, fMRI, and AI/CNNs models 🚨 !! 1/n🧵 preprint: osf.io/preprints/ps...

Thanks to the @cnsmtg.bsky.social for a wonderful #CNS2025 full of great research and people. So glad to see old friends and meet up with new ones 🙌🙌 !!!