| --- |
| pretty_name: "Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study" |
| license: cc0-1.0 |
| tags: |
| - eeg |
| - neuroscience |
| - eegdash |
| - brain-computer-interface |
| - pytorch |
| - auditory |
| - decision-making |
| - harmn400 |
| size_categories: |
| - n<1K |
| task_categories: |
| - other |
| authors: |
| - "Flora Schwartz" |
| - "Radouane El-Yagoubi" |
| - "Julie Cayron" |
| - "Pierre-Vincent Paubel" |
| - "Bastien Tremoliere" |
| --- |
| |
| # Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study |
|
|
| **Dataset ID:** `ds004860` |
|
|
| _Schwartz2023_ |
|
|
| > **At a glance:** EEG · Auditory decision-making · healthy · 31 subjects · 31 recordings · CC0 |
|
|
| ## Load this dataset |
|
|
| This repo is a **pointer**. The raw EEG data lives at its canonical source |
| (OpenNeuro / NEMAR); [EEGDash](https://github.com/eegdash/EEGDash) streams it |
| on demand and returns a PyTorch / braindecode dataset. |
|
|
| ```python |
| # pip install eegdash |
| from eegdash import EEGDashDataset |
| |
| ds = EEGDashDataset(dataset="ds004860", cache_dir="./cache") |
| print(len(ds), "recordings") |
| ``` |
|
|
| If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout, |
| you can also pull it directly: |
|
|
| ```python |
| from braindecode.datasets import BaseConcatDataset |
| ds = BaseConcatDataset.pull_from_hub("EEGDash/ds004860") |
| ``` |
|
|
|
|
| ## Dataset metadata |
|
|
| | | | |
| |---|---| |
| | **Subjects** | 31 | |
| | **Age range** | 19–45 yrs, mean 22.3 | |
| | **Recordings** | 31 | |
| | **Tasks (count)** | 1 | |
| | **Channels** | 36 (×31) | |
| | **Sampling rate (Hz)** | 512 (×30), 2048 (×1) | |
| | **Total duration (h)** | 16.4 | |
| | **Size on disk** | 3.8 GB | |
| | **Recording type** | EEG | |
| | **Experimental modality** | Auditory | |
| | **Paradigm type** | Decision-making | |
| | **Population** | Healthy | |
| | **BIDS version** | 1.8.0 | |
| | **Source** | openneuro | |
| | **License** | CC0 | |
| | **NEMAR citations** | 1 | |
|
|
| ## Tasks |
|
|
| - `HarmN400` |
|
|
|
|
| ## Upstream README |
|
|
| _Verbatim from the dataset's authors — the canonical description._ |
|
|
| EEG data collected from 31 participants as part of a research program on moral judgment. |
| The experiment consists of a third-party moral judgment task integrated into a semantic judgment task (N400). Participants listened to moral scenarios featuring either intentional or accidental harm transgressions. The last word of the scenario appeared as text (target) and participants had to respond whether the target was congruent with the scenario they just heard by pressing a response button. The target was congruent half of the time. The agent's intention and semantic congruency were manipulated orthogonally, leading to 4 within-subject conditions. For 20% of the moral scenarios, a moral judgment question (punishment) was presented immediately after the congruency judgment and participants indicated how much punishment the agent responsible for the moral transgression deserved using a joystick. |
|
|
|
|
| ## People |
|
|
| ### Authors |
| - Flora Schwartz |
| - Radouane El-Yagoubi |
| - Julie Cayron |
| - Pierre-Vincent Paubel |
| - Bastien Tremoliere _(senior)_ |
|
|
| ### Contact |
| - Flora Schwartz |
|
|
| ## Links |
|
|
| - **DOI:** [10.18112/openneuro.ds004860.v1.0.0](https://doi.org/10.18112/openneuro.ds004860.v1.0.0) |
| - **OpenNeuro:** [ds004860](https://openneuro.org/datasets/ds004860) |
| - **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/EEGDash/catalog) |
| - **Docs:** <https://eegdash.org> |
| - **Code:** <https://github.com/eegdash/EEGDash> |
|
|
| ## Provenance |
|
|
| - **Backend:** `s3` — `s3://openneuro.org/ds004860` |
| - **Exact size:** 4,065,631,774 bytes (3.8 GB) |
| - **Ingested:** 2026-04-06 |
| - **Stats computed:** 2026-04-04 |
|
|
| --- |
|
|
| _Auto-generated from [dataset_summary.csv](https://github.com/eegdash/EEGDash/blob/main/eegdash/dataset/dataset_summary.csv) and the [EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/ds004860). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._ |
|
|
