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"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."
}
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