| { |
| "format_version": 1, |
| "repository_type": "fitted_nsd_encoding_and_variance_partitioning_models", |
| "artifact_name": "VEDB and Reference SimCLR ResNet-18 — NSD Encoding and Variance-Partitioning Models", |
| "framework": { |
| "feature_extractor": "pytorch", |
| "encoding_model_fitting": "pytorch", |
| "artifact_serialization": "numpy" |
| }, |
| "paper": { |
| "title": "Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field", |
| "authors": [ |
| "Dylan M. Diaz", |
| "Margaret M. Henderson" |
| ], |
| "year": 2026, |
| "venue": "Proceedings of the 9th Conference on Cognitive Computational Neuroscience", |
| "doi": "10.32470/0416gfsq", |
| "arxiv": "2607.19316" |
| }, |
| "upstream_models": { |
| "architecture": "resnet18", |
| "pretraining_objective": "simclr", |
| "vedb_models": [ |
| { |
| "name": "Baseline", |
| "pretraining_dataset": "Visual Experience Dataset (VEDB)", |
| "repository": "DM-Diaz/VEDB-SimCLR-ResNet18-Baseline", |
| "encoding_analysis_identifier": "resnet18-Baseline" |
| }, |
| { |
| "name": "Fovea-Gaze", |
| "pretraining_dataset": "Visual Experience Dataset (VEDB)", |
| "repository": "DM-Diaz/VEDB-SimCLR-ResNet18-Fovea-Gaze", |
| "encoding_analysis_identifier": "resnet18-FoveaGaze" |
| }, |
| { |
| "name": "Periph", |
| "pretraining_dataset": "Visual Experience Dataset (VEDB)", |
| "repository": "DM-Diaz/VEDB-SimCLR-ResNet18-Periph", |
| "encoding_analysis_identifier": "resnet18-PeriphNonTTM" |
| }, |
| { |
| "name": "Periph-NF", |
| "pretraining_dataset": "Visual Experience Dataset (VEDB)", |
| "repository": "DM-Diaz/VEDB-SimCLR-ResNet18-Periph-NF", |
| "encoding_analysis_identifier": "resnet18-PeriphTTM" |
| } |
| ], |
| "reference_models": [ |
| { |
| "name": "STL-10", |
| "pretraining_dataset": "STL-10", |
| "source": "Spijkervet/SimCLR", |
| "source_url": "https://github.com/Spijkervet/SimCLR", |
| "externally_provided_checkpoint": true, |
| "redistributed_by_project": false, |
| "encoding_analysis_identifier": "resnet18-pretrained-simclr" |
| }, |
| { |
| "name": "ImageNet-100", |
| "pretraining_dataset": "ImageNet-100", |
| "dataset_source": "clane9/imagenet-100", |
| "repository": "DM-Diaz/SimCLR-ResNet18-ImageNet100", |
| "encoding_analysis_identifier": "resnet18-simclr-imgnet100" |
| }, |
| { |
| "name": "ImageNet-1K", |
| "pretraining_dataset": "ImageNet-1K", |
| "dataset_source": "evanarlian/imagenet_1k_resized_256", |
| "repository": "DM-Diaz/SimCLR-ResNet18-ImageNet1K", |
| "encoding_analysis_identifier": "resnet18-simclr-imgnet1k" |
| } |
| ], |
| "vedb_collection": "DM-Diaz/eccentricity-constrained-simclr-models-vedb" |
| }, |
| "neural_dataset": { |
| "name": "Natural Scenes Dataset (NSD)", |
| "modality": "7T fMRI", |
| "subjects": [ |
| "S1", |
| "S2", |
| "S3", |
| "S4", |
| "S5", |
| "S6", |
| "S7", |
| "S8" |
| ], |
| "held_out_evaluation_images": 1000, |
| "held_out_images_description": "NSD images shared across participants", |
| "raw_data_redistributed": false |
| }, |
| "input_preprocessing": { |
| "input_resolution": [ |
| 224, |
| 224 |
| ], |
| "nsd_visual_field_transform_reapplied": false, |
| "rescale": "uint8 / 255.0", |
| "normalization": { |
| "name": "ImageNet", |
| "mean": [ |
| 0.485, |
| 0.456, |
| 0.406 |
| ], |
| "std": [ |
| 0.229, |
| 0.224, |
| 0.225 |
| ] |
| } |
| }, |
| "feature_extraction": { |
| "layers": [ |
| "conv1", |
| "layer1.1", |
| "layer2.1", |
| "layer3.1", |
| "layer4.1", |
| "avgpool" |
| ], |
| "convolutional_spatial_reduction": { |
| "method": "adaptive_average_pooling", |
| "target_pre_pca_features_per_layer": 5000 |
| }, |
| "pca": { |
| "components_per_layer": 200, |
| "fit_separately_by_subject": true, |
| "fit_separately_by_model_condition": true, |
| "fit_separately_by_layer": true, |
| "fit_scope": "full subject-specific feature matrix before encoding-model train/holdout partitioning" |
| }, |
| "layer_features_concatenated": true |
| }, |
| "encoding_model": { |
| "type": "voxelwise_ridge_regression", |
| "regularization": "L2", |
| "candidate_lambda_count": 20, |
| "lambda_selection": "nested_holdout", |
| "selection_scope": "independently_per_voxel", |
| "feature_normalization": { |
| "method": "z_score", |
| "statistics_fit_on": "training_plus_nested_holdout", |
| "final_held_out_evaluation_excluded": true |
| }, |
| "intercept": { |
| "included": true, |
| "implementation": "column_of_ones_appended_to_feature_matrix", |
| "saved_weight_location": "final_row_of_weights" |
| }, |
| "evaluation_metrics": [ |
| "r2", |
| "corr" |
| ] |
| }, |
| "encoding_model_release": { |
| "model_count": 7, |
| "subjects_per_model": 8, |
| "vedb_model_count": 4, |
| "reference_model_count": 3, |
| "vedb_encoding_fit_count": 32, |
| "reference_encoding_fit_count": 24, |
| "total_encoding_fit_count": 56, |
| "variance_partitioning_fit_count": 16, |
| "total_npy_artifact_count": 72 |
| }, |
| "variance_partitioning": { |
| "method": "voxelwise_encoding_model_variance_partitioning", |
| "reported_comparisons": [ |
| { |
| "model1": "Fovea-Gaze", |
| "model2": "Periph", |
| "paper_figure": "Figure 4C", |
| "repository_path": "variance-partitioning/fovea-gaze-vs-periph" |
| }, |
| { |
| "model1": "Periph", |
| "model2": "Periph-NF", |
| "paper_figure": "Figure 4D", |
| "repository_path": "variance-partitioning/periph-vs-periph-nf" |
| } |
| ], |
| "subjects_per_comparison": 8, |
| "released_artifact_count": 16, |
| "fits_per_comparison": [ |
| "model1_only", |
| "model2_only", |
| "combined" |
| ], |
| "combined_feature_space": "concatenation_of_model1_and_model2_feature_spaces", |
| "feature_dimensions": { |
| "single_model_without_intercept": 1200, |
| "single_model_with_intercept": 1201, |
| "combined_without_intercept": 2400, |
| "combined_with_intercept": 2401 |
| }, |
| "regularization": { |
| "type": "L2_ridge_regression", |
| "candidate_lambda_count": 20, |
| "lambda_selection": "nested_holdout", |
| "selection_scope": "independently_per_voxel" |
| }, |
| "evaluation_metrics": [ |
| "r2", |
| "corr" |
| ], |
| "purpose": "estimate variance uniquely and jointly explained by paired representation spaces" |
| }, |
| "encoding_model_artifact": { |
| "file_format": ".npy", |
| "serialization": "numpy_saved_python_dictionary", |
| "load_with_allow_pickle": true, |
| "fit_fields": [ |
| "subject", |
| "model", |
| "features_file_list", |
| "lambdas", |
| "voxel_mask", |
| "voxel_index", |
| "voxel_nc", |
| "brain_nii_shape", |
| "weights", |
| "r2", |
| "corr", |
| "best_lambda_inds" |
| ], |
| "contains_fitted_voxelwise_weights": true, |
| "contains_held_out_metrics": true, |
| "contains_raw_nsd_stimuli": false, |
| "contains_raw_fmri_data": false, |
| "contains_fitted_pca_transforms": false, |
| "contains_feature_normalization_statistics": false, |
| "turnkey_new_image_to_voxel_prediction": false |
| }, |
| "variance_partitioning_artifact": { |
| "file_format": ".npy", |
| "serialization": "numpy_saved_python_dictionary", |
| "load_with_allow_pickle": true, |
| "fit_fields": [ |
| "subject", |
| "model1", |
| "model2", |
| "features_file_list1", |
| "features_file_list2", |
| "lambdas", |
| "voxel_mask", |
| "voxel_index", |
| "voxel_nc", |
| "brain_nii_shape", |
| "weights_varpart", |
| "r2_varpart", |
| "corr_varpart", |
| "best_lambda_inds_varpart" |
| ], |
| "weights_varpart_entries": [ |
| "model1-only", |
| "model2-only", |
| "combined" |
| ], |
| "contains_fitted_voxelwise_weights": true, |
| "contains_held_out_metrics": true, |
| "contains_raw_nsd_stimuli": false, |
| "contains_raw_fmri_data": false, |
| "contains_fitted_pca_transforms": false, |
| "contains_feature_normalization_statistics": false, |
| "turnkey_new_image_to_voxel_prediction": false |
| }, |
| "artifact_scope_note": "The repository contains 56 subject-specific fitted voxelwise encoding models derived from seven pretrained visual models (four VEDB-pretrained models and three non-egocentric reference models), plus 16 variance-partitioning fits reported in the associated study, for a total of 72 fitted .npy artifacts. Reproducing predictions for new images or refitting the analyses additionally requires the corresponding pretrained ResNet-18 checkpoints and the original feature-extraction, spatial-pooling, PCA, concatenation, normalization, and model-fitting procedures." |
| } |
|
|
