{"dataset":{"id":"45263","dataset_id":"on007688","name":"The Temporal Sequence of Party Leader Incongruence","description":"This dataset comprises high-density EEG recordings from 44 participants in a preregistered study investigating partisan processing of political incongruence. Using a multitask design with 'Just Faces' and 'Statements & Faces' tasks, the study disentangles neural responses to party leader identity and message content incongruence, testing the 'Hot Cognition' hypothesis. Behavioral ratings and event annotations accompany the raw EEG data collected from Dutch political party supporters.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on007688","concept_doi":"10.82901/nemar.on007688","latest_version_doi":"10.82901/nemar.on007688.v1.0.0","created_at":"2026-06-19 02:31:44","updated_at":"2026-07-10 23:17:33","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"The Temporal Sequence of Party Leader Incongruence\",\n  \"description\": \"This dataset comprises high-density EEG recordings from 44 participants in a preregistered study investigating partisan processing of political incongruence. Using a multitask design with 'Just Faces' and 'Statements & Faces' tasks, the study disentangles neural responses to party leader identity and message content incongruence, testing the 'Hot Cognition' hypothesis. Behavioral ratings and event annotations accompany the raw EEG data collected from Dutch political party supporters.\",\n  \"methods_description\": \"EEG data were acquired using a 64-channel BioSemi ActiveTwo system with International 10-20 electrode layout, including EOG, ECG, and mastoid leads. Stimuli were presented using PsychoPy. The 'Just Faces' task presented four political leaders and two control faces for 700ms across 260 trials. The 'Statements & Faces' task employed a 2×2 factorial design (In-party/Out-party × Pro-attitudinal/Counter-attitudinal) with 240 trials, where statements (1830–2880ms) preceded politician faces (1200–1400ms). Data were band-pass filtered offline (0.5–40 Hz) and re-referenced to average reference during preprocessing.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Gustavo Couto de Jesus\": {},\n    \"Bert N. Bakker\": {},\n    \"Gijs Schumacher\": {},\n    \"Joe Bathelt\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"event-related potentials\"\n    },\n    {\n      \"term\": \"political cognition\"\n    },\n    {\n      \"term\": \"social identity\"\n    },\n    {\n      \"term\": \"partisan bias\"\n    },\n    {\n      \"term\": \"face processing\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on007688\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on007688\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds007688.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"European Union\",\n      \"award_number\": \"101072992\",\n      \"award_title\": \"IP-PAD: Horizon Europe MSCA Doctoral Networks\"\n    },\n    {\n      \"funder_name\": \"European Research Council\",\n      \"award_number\": \"759079\",\n      \"award_title\": \"POLEMIC\"\n    },\n    {\n      \"funder_name\": \"NWO\",\n      \"award_number\": \"VI.Vidi.211.055\",\n      \"award_title\": \"Talent Programme VIDI\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"14.3 GB (1230 files)\"\n  ],\n  \"formats\": [\n    \".csv\",\n    \".edf\",\n    \".gz\",\n    \".html\",\n    \".jpg\",\n    \".json\",\n    \".md\",\n    \".png\",\n    \".py\",\n    \".tsv\",\n    \".txt\",\n    \".xlsx\",\n    \".yml\"\n  ],\n  \"source_hash\": \"e89e26575ee00ac28de707e379bd162dfd66148c2b6ea63ca1525d705dd6421b\"\n}","last_activity_at":"2026-06-19 02:31:44","source":"openneuro","source_id":"ds007688","subject_count":44,"modalities":"eeg","age_min":19,"age_max":38,"file_size":14432237149,"total_files":1603,"tasks":"JustFaces,StatementsFaces","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Gustavo Couto de Jesus, Bert N. Bakker, Gijs Schumacher, Joe Bathelt","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on007688-blue)](https://doi.org/10.82901/nemar.on007688)\n\n# The Temporal Sequence of Party Leader Incongruence: A multitask EEG dataset\n\n## Introduction\nThis dataset contains high-density EEG (and linked behavioral data) from a preregistered study investigating how partisans process political incongruence. The study tests the **'Hot Cognition'** hypothesis by disentangling the temporal sequence of neural responses to party leaders (identity incongruence) and their messages (source-content incongruence). \n\nData was collected in the Netherlands, using supporters of the progressive **GreenLeft/Labour (GL/PvdA)** party as the in-party group and the far-right **Party for Freedom (PVV)** as the out-party group.\n\n## Dataset Content\nThe dataset includes data from 44 healthy adult participants. It is organized according to BIDS v1.8.0.\nThe release includes:\n1. **Raw EEG recordings** in EDF format with BIDS-compliant JSON sidecars. Find original downsampled fif. files in \\sourcedata.\n2. **Behavioral data**: Subjective ratings of agreement, surprise, and upset (Likert scales) and reaction times. You can find it in https://osf.io/xrtcy/overview\n3. **Event Annotations**: Trial-by-trial logs including stimulus onsets, durations, trial types (congruent/incongruent), and specific stimulus file references.\n4. **Stimuli**: A root-level `/stimuli` folder containing the facial cut-outs (JPG) of the political leaders used.\n5. **Code**: Companion preprocessing and analysis scripts.\n6. **Derivatives**: Preprocessed data\n6. **Metadata**: Additional information\n\n## Experimental Design\nThe study utilized two primary tasks to isolate different stages of political information processing:\n\n### 1. Just Faces Task (Identity Incongruence)\n* **Goal**: To measure automatic neural responses to political social identity.\n* **Stimuli**: Four political leaders (Frans Timmermans & Jesse Klaver for GL/PvdA; Geert Wilders & Fleur Agema for PVV) and two unfamiliar control faces.\n* **Procedure**: 260 trials. Faces were presented for 700ms.\n* **Task**: Participants pressed a key only for unfamiliar faces to ensure attention during Just Faces task.\n\n### 2. Statements & Faces Task (Source-Content Incongruence)\n* **Goal**: To investigate how source identity of a political message is processed given source-content congruent and incongruent messages\n* **Design**: 2x2 factorial design (Face: In-party vs. Out-party; Statement: Pro-attitudinal vs. Counter-attitudinal).\n* **Stimuli**: 240 trials. A statement (1830–2880ms) was followed by a politician's face (1200–1400ms).\n* **Issues**: Immigration, climate change, EU expansion, gender equality, vegetarianism, and cultural integration.\n* **Task**: In ~18% of trials, participants rated their agreement, surprise, or upset.\n\n## Data Acquisition\n* **EEG System**: 64-channel BioSemi ActiveTwo system.\n* **Electrodes**: International 10-20 layout, including EOG, ECG, and mastoid leads.\n* **Reference**: Recorded with CMS/DRL; re-referenced to average reference during preprocessing.\n* **Filtering**: Data were band-pass filtered offline (0.5–40 Hz).\n* **Software**: Stimuli were presented and analysed using PsychoPy and MNE-Python.\n\n## Technical Validation\n* **Behavioral**: Analysis confirmed strong in-party favoritism. Participants agreed more with in-party leaders and felt greater surprise/upset when in-party leaders were paired with counter-attitudinal statements.\n* **Neural (ERP)**: Cluster-based permutation tests revealed an enhanced early posterior positivity (Visual P2) for out-party stimuli emerging as early as 148ms, suggesting rapid, pre-conscious detection of political opponents.\n\n## Data Quality Notes\n* **Participant Exclusions**: \n    * Just Faces (H1): n=37 included after excluding for EEG noise/bad channels.\n    * Statements & Faces (H3): n=36 included due to technical issues in trial recording for some participants.\n* **Bad Channels**: Frequently interpolated channels included AF7, F7, FT8, FC5, and C2.\n* **Demographics**: The sample consists primarily of young, progressive-leaning university students (Mean age = 21.4).\n\n## Usage Recommendations\n* **Citing this dataset**: Please search associated the preprint or paper with the following name:\n  > Couto de Jesus, G., Bakker, B. N., Schumacher, G., & Bathelt, J. (2026). The Temporal Sequence of Party Leader Incongruence: a data-driven ERP study.\n* **Running its code**\n  > The python scripts stored in \\code were built to analyse downsampled fif. files (see \\sourcedata). \n\n## Acknowledgements\nFunding was provided by:\n* **IP-PAD**: European Union’s Horizon Europe MSCA Doctoral Networks (Grant No. 101072992).\n* **POLEMIC**: European Research Council (ERC) (Grant No. 759079).\n* **NWO**: Talent Programme VIDI (Project No. VI.Vidi.211.055).\n\nSpecial thanks to Anna Mae van Dooren, Lois van Petegem, and Rayna Akil for data collection assistance.\n","bids_version":"1.8.0","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-06 02:51:28","zarr_store_count":88,"zarr_index_etag":"faee79e7ce6bd32aaf9cbc8dec59fced","zarr_source_commit":"8f78cdfeebd36f4e72d7768067113cb65b7315df","archive_status":"ready","archive_size":12810242233,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":0,"n_channels":65,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":14326669276,"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":81905,"recording_duration_min":256,"recording_duration_max":1766,"recording_count":88,"recordings_unavailable":0,"recordings_measured":88,"channel_count_min":73,"channel_count_max":73,"sampling_frequency":250,"power_line_frequency":50,"eeg_reference":"placed on Cz","placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-19 02:43:05\",\"metadata_updated_at\":\"2026-06-19 02:43:09\",\"archive_checked_at\":\"2026-06-19 02:53:43\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-19 02:47:46\",\"citations_updated_at\":null,\"channel_montage_checked_at\":\"2026-06-28 23:58:36\",\"hed_checked_at\":\"2026-06-30 05:45:00\",\"data_checked_at\":\"2026-07-25 03:00:24\",\"availability_report_at\":\"2026-07-23 01:33:32\",\"signal_defaults_at\":\"2026-09-02 12:57:39\",\"recording_stats_at\":\"2026-09-06 03:01:12\"}","participants":44,"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":"13.44 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on007688/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}