{"dataset":{"id":"50767","dataset_id":"on002158","name":"Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging","description":"This multimodal neuroimaging dataset investigates the neural mechanisms of metacognition—the ability to assess decision confidence—by isolating postdecisional from decisional contributions. Healthy volunteers performed perceptual judgments and observed decisions while reporting confidence, with concurrent electroencephalography and functional magnetic resonance imaging recordings. The study reveals dissociable neural correlates of confidence in prefrontal regions and proposes a computational model explaining how decision commitment enhances metacognitive performance.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on002158","concept_doi":"10.82901/nemar.on002158","latest_version_doi":"10.82901/nemar.on002158.v1.0.0","created_at":"2026-06-21 02:30:54","updated_at":"2026-07-10 22:09:25","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging\",\n  \"description\": \"This multimodal neuroimaging dataset investigates the neural mechanisms of metacognition—the ability to assess decision confidence—by isolating postdecisional from decisional contributions. Healthy volunteers performed perceptual judgments and observed decisions while reporting confidence, with concurrent electroencephalography and functional magnetic resonance imaging recordings. The study reveals dissociable neural correlates of confidence in prefrontal regions and proposes a computational model explaining how decision commitment enhances metacognitive performance.\",\n  \"methods_description\": \"Participants performed speeded perceptual judgments (numerosity discrimination) in active and observation conditions while reporting confidence. Data were acquired using concurrent EEG and fMRI. EEG preprocessing included MR-gradient artifact removal, ballistocardiogram correction, bandpass filtering (1-10 Hz), independent component analysis, and dipolar source localization. fMRI analysis employed general linear modeling with parametric modulation by confidence ratings, response times, and perceptual evidence, with group-level results corrected for multiple comparisons using cluster-extent family-wise error correction.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Michael Pereira\": {\n      \"orcid\": \"0000-0003-0778-674X\",\n      \"affiliations\": [\n        {\n          \"name\": \"Center for Neuroprosthetics, ??cole Polytechnique F??d??rale de Lausanne, 1202 Geneva, Switzerland;\"\n        },\n        {\n          \"name\": \"Laboratory of Cognitive Neuroscience, Brain Mind Institute, Faculty of Life Sciences, ??cole Polytechnique F??d??rale de Lausanne, 1202 Geneva, Switzerland;\"\n        },\n        {\n          \"name\": \"Laboratoire de Psychologie et Neurocognition, CNRS UMR 5105, Universit?? Grenoble Alpes, 38400 Saint-Martin-d'H??res, France;\"\n        }\n      ]\n    },\n    \"Nathan Faivre\": {\n      \"orcid\": \"0000-0001-6011-4921\",\n      \"affiliations\": [\n        {\n          \"name\": \"Center for Neuroprosthetics, ??cole Polytechnique F??d??rale de Lausanne, 1202 Geneva, Switzerland;\"\n        },\n        {\n          \"name\": \"Laboratory of Cognitive Neuroscience, Brain Mind Institute, Faculty of Life Sciences, ??cole Polytechnique F??d??rale de Lausanne, 1202 Geneva, Switzerland;\"\n        },\n        {\n          \"name\": \"Laboratoire de Psychologie et Neurocognition, CNRS UMR 5105, Universit?? Grenoble Alpes, 38400 Saint-Martin-d'H??res, France;\"\n        }\n      ]\n    },\n    \"Inaki Iturrate\": {\n      \"orcid\": \"0000-0001-7781-7826\"\n    },\n    \"Marco Wirthlin\": {},\n    \"Luana Serafini\": {\n      \"orcid\": \"0000-0002-7039-5154\",\n      \"affiliations\": [\n        {\n          \"name\": \"Center for Neuroprosthetics, ??cole Polytechnique F??d??rale de Lausanne, 1202 Geneva, Switzerland;\"\n        },\n        {\n          \"name\": \"Laboratory of Cognitive Neuroscience, Brain Mind Institute, Faculty of Life Sciences, ??cole Polytechnique F??d??rale de Lausanne, 1202 Geneva, Switzerland;\"\n        },\n        {\n          \"name\": \"Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy;\"\n        }\n      ]\n    },\n    \"Stephanie Martin\": {\n      \"orcid\": \"0000-0001-8678-5862\"\n    },\n    \"Arnaud Desvachez\": {},\n    \"Olaf Blanke\": {},\n    \"Dimitri Van de Ville\": {\n      \"orcid\": \"0000-0002-2879-3861\",\n      \"affiliations\": [\n        {\n          \"name\": \"Center for Neuroprosthetics, ??cole Polytechnique F??d??rale de Lausanne, 1202 Geneva, Switzerland;\"\n        },\n        {\n          \"name\": \"Medical Image Processing Lab, Institute of Bioengineering, ??cole Polytechnique F??d??rale de Lausanne, 1202 Geneva, Switzerland;\"\n        },\n        {\n          \"name\": \"Department of Radiology and Medical Informatics, University of Geneva, 1205 Geneva, Switzerland;\"\n        }\n      ]\n    },\n    \"Jose del R. Millan\": {\n      \"orcid\": \"0000-0001-5819-1522\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"fMRI\"\n    },\n    {\n      \"term\": \"metacognition\"\n    },\n    {\n      \"term\": \"confidence judgment\"\n    },\n    {\n      \"term\": \"decision making\"\n    },\n    {\n      \"term\": \"prefrontal cortex\"\n    },\n    {\n      \"term\": \"multimodal imaging\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1073/pnas.1918335117\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on002158\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on002158\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1101/496877\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds002158.v1.0.2\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Bertarelli Foundation\"\n    },\n    {\n      \"funder_name\": \"Swiss National Science Foundation\"\n    },\n    {\n      \"funder_name\": \"European Research Council\",\n      \"award_number\": \"803122\"\n    }\n  ],\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\",\n    \"func\",\n    \"anat\",\n    \"fmap\"\n  ],\n  \"sizes\": [\n    \"453.8 GB (848 files)\"\n  ],\n  \"formats\": [\n    \".Rhistory\",\n    \".eeg\",\n    \".fdt\",\n    \".gitattributes\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".nii\",\n    \".set\",\n    \".tsv\",\n    \".txt\",\n    \".vhdr\",\n    \".vmrk\",\n    \".yml\"\n  ],\n  \"source_hash\": \"ea00da5d112f2e9618940d1a899e41da1df63e66f1c16451dc472eb58ad5b23a\"\n}","last_activity_at":"2026-06-21 02:30:54","source":"openneuro","source_id":"ds002158","subject_count":20,"modalities":"anat,eeg,fmap,func","age_min":20,"age_max":32,"file_size":460190036961,"total_files":1359,"tasks":"main,rest","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Michael Pereira, Nathan Faivre, Inaki Iturrate, Marco Wirthlin, Luana Serafini, Stephanie Martin, Arnaud Desvachez, Olaf Blanke, Dimitri Van de Ville, Jose del R. Millan","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on002158-blue)](https://doi.org/10.82901/nemar.on002158)\n\nThis dataset contains the data in  \n\nPereira, M., Faivre, N., Iturrate, I., Wirthlin, M., Serafini, L., Martin, S., Desvachez, A., Blanke, O., Van De Ville, D., Millan, JdR. (2020). Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging. Proceedings of the National Academy of Science, 117 (15) pp. 8382-8390\nhttps://doi.org/10.1073/pnas.1918335117\n\nPreprint: https://www.biorxiv.org/content/10.1101/496877v1\n\nABSTRACT\nThe human capacity to compute the likelihood that a decision is correct—known as metacognition—has proven difficult to study in isolation as it usually cooccurs with decision making. Here, we isolated postdecisional from decisional contributions to metacognition by analyzing neural correlates of confidence with multimodal imaging. Healthy volunteers reported their confidence in the accuracy of decisions they made or decisions they observed. We found better metacognitive performance for committed vs. observed decisions, indicating that committing to a decision may improve confidence. Relying on concurrent electroencephalography and hemodynamic recordings, we found a common correlate of confidence following committed and observed decisions in the inferior frontal gyrus and a dissociation in the anterior prefrontal cortex and anterior insula. We discuss these results in light of decisional and postdecisional accounts of confidence and propose a computational model of confidence in which metacognitive performance naturally improves when evidence accumulation is constrained upon committing a decision.\n\npreregistration: https://osf.io/a5qmv/\n\nThe dataset contains raw fMRI scans, raw EEG in BrainVision format as well as anatomical scans (T1) and field mapping. We also included preprocessed EEG and fMRI data in derivatives/eegprep and derivatives/fmriprep.\n\nEEG PREPROCESSING\nMR-gradient artifacts were removed using sliding window average template subtraction. TP10 electrode on the right mastoid was used to detect heartbeats for ballistocardiogram artifact (BCG) removal using a semi-automatic procedure in BrainVision Analyzer 2. Data were then filtered using a Butterworth, 4th order zero-phase (two-pass) bandpass filter between 1 and 10 Hz, epoched [-0.2, 0.6 s] around the response onset (i.e. the button press in the active condition or the appearance of the virtual hand for in observation condition), re-referenced to a common average, and input to independent component analysis (ICA) to remove residual BCG and ocular artifacts. In order to ensure numerical stability when estimating the independent components, we retained 99% of the variance from the electrode space, leading to an average of 19 (SD = 6) components estimated for each participant and condition. Independent components (ICs) were then fitted with a dipolar source localization method (66). ICs whose dipole lied outside the brain, or resembled muscular or ocular artifacts were eliminated. A total of 8 (SD = 3) components were finally kept. All preprocessing steps were performed using EEGLAB and in-house scripts under Matlab (The MathWorks, Inc., Natick, Massachusetts, United States).\n \nFMRI PREPROCESSING\nWe modeled the BOLD signal using a general linear model (GLM) with two separate regressors (stick functions at stimulus onset) for the active and observation condition as well as their spatial and temporal derivatives. We then parametrically modulated the regressors with three behavioral variables: the confidence ratings, the response times, and the numerosity difference between the two arrays of dots (i.e., perceptual evidence). Empirical cross-correlation between regressors confirmed limited collinearity for the active (resp. observation) condition (max(abs(R)) = 0.26 ± 0.02 resp., max(abs(R)) = 0.25 ± 0.02). Bad trials as defined in the behavioral analysis section were modeled by two separate regressors (one for active and one for observation) and their spatial and temporal derivatives. We added six realignments parameters as regressors of no interest. All second-level (group-level) results are reported at a significance-level of p < 0.05 using cluster-extent family-wise error (FWE) correction with a voxel-height threshold of p < 0.001. We used the anatomical automatic labelling (AAL) atlas for brain parcellation (Tzourio-Mazoyer et al., 2002). \n","bids_version":"1.1.1","sessions_count":1,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"failed","zarr_converted_at":"2026-08-03 01:53:40","zarr_store_count":20,"zarr_index_etag":"3bf5cbcb89b49462bdec0e9fb9f2edf6","zarr_source_commit":"73b6296c7ef2803d5ec0bfdc127cdb711f5409b0","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 428.6 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":119,"zarr_failure_count":1,"zarr_deterministic":0,"zarr_failed_at":"2026-09-07 02:21:48","num_dataset_citations":0,"num_datapaper_citations":0,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":453848306954,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":21548,"recording_duration_min":520,"recording_duration_max":1140,"recording_count":139,"recordings_unavailable":119,"recordings_measured":20,"channel_count_min":63,"channel_count_max":64,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-21 02:44:44\",\"metadata_updated_at\":\"2026-06-21 02:44:47\",\"archive_checked_at\":\"2026-06-21 02:45:18\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-21 02:54:13\",\"citations_updated_at\":\"2026-09-09 03:00:19\",\"channel_montage_checked_at\":\"2026-06-28 23:06:41\",\"hed_checked_at\":\"2026-06-30 04:37:25\",\"data_checked_at\":\"2026-07-28 03:00:14\",\"availability_report_at\":\"2026-07-23 01:11:50\",\"recording_stats_at\":\"2026-09-02 11:32:16\",\"signal_defaults_at\":\"2026-09-02 11:55:04\"}","participants":20,"num_citations":0,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"429 GB","zarr_data_failures":{"count":1,"detail_ref":"zarr/index.json","pending":118,"discovered":119},"zarr_index_url":null,"attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}