DOI: 10.6084/m9.figshare.30753239.v1
title: Derivatives for <i>"Convergent Transformations of Visual Representation in Brains and Models"</i>

description: This dataset contains all derivative files generated for the analyses presented in the manuscript <i>Convergent Transformations of Visual Representation in Brains and Models (arXiv:</i><i>2507.13941</i><i>)</i>. These derivatives include representational similarity analyses (RSA), centered kernel alignment (CKA), model-feature extractions, stimulus metadata, and additional materials produced during the peer-review revision process.The derivatives allow full reproduction of the figures and analyses presented in the paper without requiring reprocessing of the raw fMRI datasets (NSD, BOLD5000, THINGS-fMRI, Mosaic-fMRI). All analysis code, together with instructions for generating the derivatives from raw data, is provided in the companion repository:<br><b>https://github.com/memory-formation/convergent-transformations</b><br>A full description of each file is available in <code>derivatives/README.md</code> within the repository.The dataset is organized into the following folders:<b>1. </b><code><strong>metadata/</strong></code>Global metadata used across analyses:HCP-MMP1 atlas definitionsModel metadata and parameter counts<b>2. </b><code><strong>captions/</strong></code>Stimulus descriptions used for language-model feature extraction:Human-curated MS-COCO captionsPixtral-12B generated captions for NSD, BOLD5000, and THINGS-fMRI<b>3. </b><code><strong>nsd/</strong></code>Representational alignment derivatives for the Natural Scenes Dataset:Inter-subject RSA and CKA (multiple shifts and hemisphere configurations)Subject–model RSA for all vision and language modelsPartial RSA controlling for confoundsPermutation distributions for significance testingkMCCA projections and semantic dimensionsScene/object annotations<b>4. </b><code><strong>bold5000/</strong></code>Derivatives for the BOLD5000 dataset:Inter-subject RSA across all images and dataset subsetsSubject–model RSA across visual and language models<b>5. </b><code><strong>things/</strong></code>THINGS-fMRI derivatives:Inter-subject RSASubject–model RSA<b>6. </b><code><strong>datasets/</strong></code>Preprocessed metadata supporting all alignment analyses:NSD stimulus indices, ROI masks, and image filesBOLD5000 stimulus indices, captions, and trial mappingsTHINGS-fMRI categories, captions, and stimulus indices<b>7. </b><code><strong>revision/</strong></code>Additional files generated during the peer-review revision process:ROI voxel countsReliability analyses (stepwise subset analysis, half-split, repetition-based)MPNet (sentence-transformers) representations following Doerig et al. 2025<b>7.1 </b><code><strong>revision/connectivity-surface-mosaic/</strong></code>Surface-based replication of the main IS-RSA connectivity analyses using MOSAIC-fMRI:Connectivity matrices for NSD, BOLD5000, and THINGSCorrelation–correlation and cosine–correlation RSAFull matrices and diagonal-only variants<b>How to use this dataset:</b><br>Place these derivatives inside a <code>derivatives/</code> directory at the root of the GitHub repository. All notebooks and scripts will run without recomputing intermediate files.<br><b>Code availability:</b><br>All code and instructions are provided in the public repository:<br>https://github.com/memory-formation/convergent-transformations<br><b>Contact:</b><br>For any questions or additional materials, contact the corresponding author (Pablo Marcos-Manchón; pmarcos@ub.edu).