access stringclasses 2
values | agent_results stringclasses 2
values | collection stringclasses 1
value | data_category stringclasses 6
values | metric_direction stringclasses 3
values | primary_metric stringclasses 9
values | protocol_url stringlengths 121 160 | public_bytes int64 0 10.6B | public_file_count int64 0 257 | source_url stringlengths 35 160 | summary stringlengths 87 492 | task_id stringlengths 9 41 | task_url stringlengths 106 145 | task_version stringclasses 10
values | title stringlengths 20 66 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
hosted_public | historical_v0_only | v1 | calcium-imaging | See task protocol | See task protocol | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/calcium-imaging/neuro-rsc-place-cell/README.md | 203,632,523 | 1 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/calcium-imaging/neuro-rsc-place-cell/README.md | The agent performs five analyses of RSC place-cell dynamics using the staged recording. | neuro-rsc-place-cell | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/calcium-imaging/neuro-rsc-place-cell/README.md | 0.2 | neuro-rsc-place-cell |
hosted_public | not_yet_available | v1 | connectomics | maximize | score | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/connectomics/celegans-structure-function-divergence/README.md | 1,063,606 | 3 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/connectomics/celegans-structure-function-divergence/README.md | How closely does anatomical wiring match measured signal propagation in C. elegans? The agent quantifies differences between a connectome and a signaling atlas using a fixed analysis protocol. It compares connections, communities, hubs, and pharyngeal organization. The evaluator runs the submitted analyzer on additiona... | celegans-structure-function-divergence | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/connectomics/celegans-structure-function-divergence/README.md | 0.6 | Anatomical Connectivity and Signal Propagation in C. elegans |
hosted_public | historical_v0_only | v1 | eeg | See task protocol | See task protocol | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/bciciv-2a/README.md | 202,714,474 | 1 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/bciciv-2a/README.md | The agent designs a PyTorch nn.Module MIAgentModel that classifies a (B, 22, 512) tensor of cue-relative, 4–38 Hz motor-imagery EEG (128 Hz, microvolts) into 4 classes (left hand / right hand / feet / tongue). | bciciv-2a | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/bciciv-2a/README.md | 0.2 | bciciv-2a — 4-class Motor Imagery decoding (BCI Competition IV 2a) |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-cvep-thielen2015/README.md | 119,737,938 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-cvep-thielen2015/README.md | This task asks an agent to classify one-second cVEP EEG windows into 36 native classes. The source protocol keeps train and validation subjects separate. | eeg-cvep-thielen2015 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-cvep-thielen2015/README.md | 0.1-public-validation | Thielen2015 cVEP decoding |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-dementia-dice/README.md | 510,942,124 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-dementia-dice/README.md | The DICE research task classifies five-second EEG windows from source-labeled Alzheimer's, dementia, and healthy groups. Train and validation subjects do not overlap. This is a research classification task, not clinical diagnostic validation. | eeg-dementia-dice | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-dementia-dice/README.md | 0.1-public-validation | DICE EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-erp-ern/README.md | 185,087,647 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-erp-ern/README.md | This ERN task distinguishes one-second EEG windows labeled Target and NonTarget in the source. The validation subjects do not appear in the training split. | eeg-erp-ern | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-erp-ern/README.md | 0.1-public-validation | ERN EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-erp-lrp/README.md | 185,087,647 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-erp-lrp/README.md | This LRP task classifies one-second EEG windows as source-labeled Target or NonTarget. The native split tests the method on subjects held out from training. | eeg-erp-lrp | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-erp-lrp/README.md | 0.1-public-validation | LRP EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-erp-mmn/README.md | 460,888,332 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-erp-mmn/README.md | This mismatch-negativity task distinguishes source-labeled Target and NonTarget EEG windows. Each window lasts one second, and train and validation use disjoint subject groups. | eeg-erp-mmn | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-erp-mmn/README.md | 0.1-public-validation | Mismatch-negativity EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-erp-n170/README.md | 148,243,682 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-erp-n170/README.md | This N170 task distinguishes one-second EEG windows labeled Target and NonTarget in the source. Its validation split contains subjects absent from training. | eeg-erp-n170 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-erp-n170/README.md | 0.1-public-validation | N170 EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-erp-n2pc/README.md | 148,345,021 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-erp-n2pc/README.md | This N2pc task classifies one-second EEG windows as source-labeled Target or NonTarget. The task uses a subject-disjoint public validation split. | eeg-erp-n2pc | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-erp-n2pc/README.md | 0.1-public-validation | N2pc EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-erp-n400/README.md | 55,590,922 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-erp-n400/README.md | This N400 task distinguishes one-second EEG windows labeled Target and NonTarget in the source. The source protocol separates training and validation subjects. | eeg-erp-n400 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-erp-n400/README.md | 0.1-public-validation | N400 EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-erp-p3/README.md | 92,652,002 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-erp-p3/README.md | This P3 task uses one-second EEG windows from the Kappenman2021 source case and two native classes. Its public validation subjects are separate from the training subjects. | eeg-erp-p3 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-erp-p3/README.md | 0.1-public-validation | Kappenman2021 P3 EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-me-highgamma2017/README.md | 2,041,458,389 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-me-highgamma2017/README.md | This task classifies three-second EEG windows from the High-Gamma motor-execution protocol into four native classes. The native split separates subjects between training and validation. | eeg-me-highgamma2017 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-me-highgamma2017/README.md | 0.1-public-validation | High-Gamma motor-execution classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-me-ofner2017/README.md | 443,100,882 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-me-ofner2017/README.md | This Ofner2017 task asks an agent to classify three-second motor-execution EEG windows into seven native classes. Training and validation use different subjects. | eeg-me-ofner2017 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-me-ofner2017/README.md | 0.1-public-validation | Ofner2017 motor-execution classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-mental-arithmetic-zyma2019/README.md | 63,605,750 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-mental-arithmetic-zyma2019/README.md | This Zyma2019 task classifies five-second mental-arithmetic EEG windows into two native classes. Its public validation split is subject-disjoint. | eeg-mental-arithmetic-zyma2019 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-mental-arithmetic-zyma2019/README.md | 0.1-public-validation | Zyma2019 mental-arithmetic classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-mi-barachant2012/README.md | 11,825,808 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-mi-barachant2012/README.md | This AlexMI task asks an agent to classify four-second motor-imagery EEG windows into three native classes. The split separates trigger indices and excludes overlapping windows on the same timeline. | eeg-mi-barachant2012 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-mi-barachant2012/README.md | 0.1-public-validation | AlexMI motor-imagery classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-mi-bnci2014/README.md | 170,344,916 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-mi-bnci2014/README.md | This task classifies four-second BCI-IV-2a EEG windows into four source-defined motor-imagery classes. It retains the native subject grouping from the frozen AgentEEGBench protocol. It is not the legacy bciciv-2a task, which fits session T and scores session E per subject. | eeg-mi-bnci2014 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-mi-bnci2014/README.md | 0.1-public-validation | BCI-IV-2a native motor-imagery classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-mi-cho2017/README.md | 1,023,001,442 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-mi-cho2017/README.md | This Cho2017 task classifies four-second motor-imagery EEG windows into two native classes. The source protocol reserves separate subjects for validation. | eeg-mi-cho2017 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-mi-cho2017/README.md | 0.1-public-validation | Cho2017 motor-imagery classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-mi-eegmmidb/README.md | 929,308,208 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-mi-eegmmidb/README.md | This EEGMMIDB task uses four-second motor-imagery EEG windows and four native classes. Training and validation subjects do not overlap. | eeg-mi-eegmmidb | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-mi-eegmmidb/README.md | 0.1-public-validation | EEGMMIDB motor-imagery classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-mi-openbmi2019/README.md | 1,024,405,402 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-mi-openbmi2019/README.md | This OpenBMI motor-imagery task asks an agent to classify four-second EEG windows into two native classes. Its native split keeps validation subjects out of training. | eeg-mi-openbmi2019 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-mi-openbmi2019/README.md | 0.1-public-validation | OpenBMI motor-imagery classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-mi-zhou2016/README.md | 32,355,620 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-mi-zhou2016/README.md | This Zhou2016 task classifies four-second motor-imagery EEG windows into three native classes. The source protocol places training and validation in separate sessions. | eeg-mi-zhou2016 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-mi-zhou2016/README.md | 0.1-public-validation | Zhou2016 motor-imagery classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-p3-openbmi2019/README.md | 10,623,665,898 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-p3-openbmi2019/README.md | This OpenBMI P3 task uses one-second EEG windows and two native classes. Training and validation use disjoint subjects under the source protocol. | eeg-p3-openbmi2019 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-p3-openbmi2019/README.md | 0.1-public-validation | OpenBMI P3 EEG classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-sleep-stage-sleepedf/README.md | 4,453,124,284 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-sleep-stage-sleepedf/README.md | This task classifies 30-second Sleep-EDF cassette EEG windows into N1, N2, N3, R, and W. It uses a full native export from 78 subjects. The native pipeline crops wake time to 30 minutes and uses a subject-disjoint split. | eeg-sleep-stage-sleepedf | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-sleep-stage-sleepedf/README.md | 0.1-public-validation | Sleep-EDF cassette sleep-stage classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-ssvep-openbmi2019/README.md | 1,024,405,402 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-ssvep-openbmi2019/README.md | This OpenBMI SSVEP task asks an agent to classify four-second EEG windows into four native classes. The public validation subjects are separate from training subjects. | eeg-ssvep-openbmi2019 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-ssvep-openbmi2019/README.md | 0.1-public-validation | OpenBMI SSVEP classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-ssvep-wang2017/README.md | 796,760,562 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-ssvep-wang2017/README.md | This Wang2017 SSVEP task classifies four-second EEG windows into 40 native classes. The native split evaluates the chosen method on different subjects. | eeg-ssvep-wang2017 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-ssvep-wang2017/README.md | 0.1-public-validation | Wang2017 SSVEP classification |
hosted_public | not_yet_available | v1 | eeg | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/eeg-typing-spanishbcbl/README.md | 3,539,526,604 | 4 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/eeg-typing-spanishbcbl/README.md | This task classifies one-second typing-related EEG windows into 29 source-defined output classes. The mapping includes characters, space, a number group, and a special-symbol group. The native SimilaritySplit separates train and validation by sentence-similarity clusters. | eeg-typing-spanishbcbl | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/eeg-typing-spanishbcbl/README.md | 0.1-public-validation | SpanishBCBL EEG typing classification |
hosted_public | historical_v0_only | v1 | eeg | See task protocol | See task protocol | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/eeg/sleep-edf/README.md | 1,225,526,664 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/eeg/sleep-edf/README.md | The agent designs a PyTorch nn.Module SleepAgentModel that classifies a (B, 1, 3000) tensor of preprocessed Fpz-Cz sleep EEG (30 s epoch at 100 Hz, microvolts) into 5 sleep stages (Wake / N1 / N2 / N3 / REM). | sleep-edf | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/eeg/sleep-edf/README.md | 0.2 | sleep-edf — 5-class Sleep Staging (Sleep-EDF Cassette) |
hosted_public | not_yet_available | v1 | invasive | minimize | normalized_spectrogram_MSE | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/intracortical/falcon-b1-birdsong/README.md | 170,230,855 | 11 | https://dandiarchive.org/dandiset/001046/draft | A Neuropixels probe recorded 85 threshold-crossing channels from a singing zebra finch. The task predicts one vocalization spectrogram for each complete song motif. It is not a human speech task. | falcon-b1-birdsong | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/invasive/falcon-b1-birdsong/README.md | 0.1-public-validation-motif | B1: birdsong spectrogram |
source_only | not_yet_available | v1 | invasive | maximize | R2_variance_weighted | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/intracortical/falcon-h1-reach-grasp/README.md | 0 | 0 | https://dandiarchive.org/dandiset/000954/draft | A human participant watched a virtual arm and attempted arm and grasp movements. Utah arrays recorded threshold crossings in motor cortex. The task predicts seven commanded velocities, not measured arm movement. Read the FALCON dataset description and the native loader. | falcon-h1-reach-grasp | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/invasive/falcon-h1-reach-grasp/README.md | 0.1-public-validation-stream | H1: attempted reach and grasp |
hosted_public | not_yet_available | v1 | invasive | minimize | WER | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/intracortical/falcon-h2-handwriting/README.md | 1,110,017,522 | 86 | https://dandiarchive.org/dandiset/000950/0.241029.1403 | A BrainGate2 participant attempted to write prompted English sentences while intracortical arrays recorded spike counts. The task decodes one sentence after each complete trial. | falcon-h2-handwriting | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/invasive/falcon-h2-handwriting/README.md | 0.1-public-validation-stream | H2: attempted handwriting |
hosted_public | not_yet_available | v1 | invasive | maximize | R2_variance_weighted | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/intracortical/falcon-m1-emg/README.md | 134,424,422 | 18 | https://dandiarchive.org/dandiset/000941/0.241029.1405 | This task predicts 16 upper-limb muscle signals from 64 intracortical channels during macaque reach and grasp. | falcon-m1-emg | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/invasive/falcon-m1-emg/README.md | 0.1-public-validation-stream | M1-A: upper-limb EMG |
hosted_public | not_yet_available | v1 | invasive | maximize | R2_variance_weighted | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/intracortical/falcon-m2-finger-velocity/README.md | 13,234,392 | 30 | https://dandiarchive.org/dandiset/000953/draft | This task predicts two finger-group velocities from 96 macaque intracortical channels during finger control. | falcon-m2-finger-velocity | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/invasive/falcon-m2-finger-velocity/README.md | 0.1-public-validation-stream | M2: finger-group velocity |
hosted_public | not_yet_available | v1 | invasive | maximize | balanced_accuracy | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/ieeg/ieeg-film-speech-music/README.md | 922,341,200 | 257 | https://doi.org/10.18112/openneuro.ds003688.v1.0.7 | This task classifies speech and music intervals in a film from clinical ECoG and sEEG recordings. It tests held-out film blocks within each participant. | ieeg-film-speech-music | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/invasive/ieeg-film-speech-music/README.md | 0.2-complete-guarded-windows | Film speech versus music from clinical iEEG |
hosted_public | not_yet_available | v1 | invasive | maximize | pearson_r | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/ieeg/ieeg-finger-flexion-bciciv4/README.md | 324,773,839 | 14 | https://www.bbci.de/competition/iv/ | This task predicts continuous finger flexion from ECoG recordings in three participants. Each model returns five trajectories, while the score uses four fingers. | ieeg-finger-flexion-bciciv4 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/invasive/ieeg-finger-flexion-bciciv4/README.md | 0.1-public-validation-offline | BCI Competition IV Dataset 4: finger flexion |
hosted_public | not_yet_available | v1 | mri | minimize | test_mae | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/mri/brain-age-mri/README.md | 3,729,262,581 | 1 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/mri/brain-age-mri/README.md | The agent develops a model that predicts chronological age from T1-weighted brain MRI. Model development uses public images and age labels. Evaluation uses a separate set of held-out subjects. | brain-age-mri | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/mri/brain-age-mri/README.md | 0.1-candidate | Age Prediction from Structural MRI |
hosted_public | historical_v0_only | v1 | mri | See task protocol | See task protocol | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/mri/tops-fmri/README.md | 965,667,246 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/mri/tops-fmri/README.md | The agent trains a linear 279 ROI / 38781 edge functional-connectivity signature on the public Study3 fMRI dynamic-FC data (dfc_5bin_dat × CAPS/REST + pain ratings). | tops-fmri | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/mri/tops-fmri/README.md | 0.1 | tops-fmri — Tonic Pain Signature (linear FC, external validation) |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-badham2017deficits-exp1/README.md | 2,304,677 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-badham2017deficits-exp1/README.md | Participants classify geometric objects as E or K across four rule problems and receive feedback. The agent predicts participants' recorded responses from the information available before each response. | psych101-badham2017deficits-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-badham2017deficits-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Category Learning from Feedback |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-bahrami2020four-exp/README.md | 5,106,777 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-bahrami2020four-exp/README.md | Participants repeatedly choose among four labeled options and observe the points earned. The agent predicts participants' recorded responses from the information available before each response. | psych101-bahrami2020four-exp | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-bahrami2020four-exp/README.md | 0.2-byte-next-v1 | Predicting Human Choices Among Four Rewards |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-collsioo2023mcpl-exp1-775cd1984f/README.md | 3,027,152 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-collsioo2023mcpl-exp1-775cd1984f/README.md | Participants estimate a target hormone level from two verbally described hormone levels. The agent predicts participants' recorded responses from the information available before each response. | psych101-collsioo2023mcpl-exp1-775cd1984f | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-collsioo2023mcpl-exp1-775cd1984f/README.md | 0.2-byte-next-v1 | Human Hormone-Level Estimates from Verbal Cues |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-collsioo2023mcpl-exp2-6bd18ff5bf/README.md | 3,201,056 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-collsioo2023mcpl-exp2-6bd18ff5bf/README.md | Participants estimate a target hormone level from two numbered hormone levels. The agent predicts participants' recorded responses from the information available before each response. | psych101-collsioo2023mcpl-exp2-6bd18ff5bf | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-collsioo2023mcpl-exp2-6bd18ff5bf/README.md | 0.2-byte-next-v1 | Human Hormone-Level Estimates from Numeric Cues |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-collsioo2023mcpl-exp3-c372064286/README.md | 2,025,867 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-collsioo2023mcpl-exp3-c372064286/README.md | Participants estimate a target hormone level from two verbally described hormone levels. The agent predicts participants' recorded responses from the information available before each response. | psych101-collsioo2023mcpl-exp3-c372064286 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-collsioo2023mcpl-exp3-c372064286/README.md | 0.2-byte-next-v1 | Human Hormone-Level Estimates from Two Cues |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-cox2017information-exp1/README.md | 8,684,274 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-cox2017information-exp1/README.md | Participants study word pairs and judge whether a later pair appeared together in the study list. The agent predicts participants' recorded responses from the information available before each response. | psych101-cox2017information-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-cox2017information-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Recognition of Studied Word Pairs |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-enkavi2019adaptivenback-exp1/README.md | 8,466,948 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-enkavi2019adaptivenback-exp1/README.md | Participants compare each letter with the one shown N trials earlier and press a match or nonmatch key. The agent predicts participants' recorded responses from the information available before each response. | psych101-enkavi2019adaptivenback-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-enkavi2019adaptivenback-exp1/README.md | 0.2-byte-next-v1 | Predicting Human N-Back Letter Responses |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-enkavi2019digitspan-exp1/README.md | 5,692,403 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-enkavi2019digitspan-exp1/README.md | Participants view a digit sequence and type the digits in their remembered order. The agent predicts participants' recorded responses from the information available before each response. | psych101-enkavi2019digitspan-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-enkavi2019digitspan-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Digit-Sequence Recall |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-enkavi2019gonogo-exp1/README.md | 7,564,952 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-enkavi2019gonogo-exp1/README.md | Participants press a key for one color and withhold a response for another color. The agent predicts participants' recorded responses from the information available before each response. | psych101-enkavi2019gonogo-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-enkavi2019gonogo-exp1/README.md | 0.2-byte-next-v1 | Predicting Human Go/No-Go Keypresses |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-enkavi2019recentprobes-exp1/README.md | 3,678,382 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-enkavi2019recentprobes-exp1/README.md | Participants remember six letters and judge whether a later probe letter belonged to that list. The agent predicts participants' recorded responses from the information available before each response. | psych101-enkavi2019recentprobes-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-enkavi2019recentprobes-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Recognition of Recent Letters |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-feng2021dynamics-exp1/README.md | 3,114,689 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-feng2021dynamics-exp1/README.md | Participants choose between two slot machines over multiple games and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-feng2021dynamics-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-feng2021dynamics-exp1/README.md | 0.2-byte-next-v1 | Predicting Human Choices Between Changing Slot Machines |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-flesch2018comparing-exp1/README.md | 31,867,420 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-flesch2018comparing-exp1/README.md | Participants accept or reject trees that vary in leaves and branches across two gardens, with feedback during training. The agent predicts participants' recorded responses from the information available before each response. | psych101-flesch2018comparing-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-flesch2018comparing-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Tree-Category Learning Across Gardens |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-frey2017cct-exp1/README.md | 71,600,868 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-frey2017cct-exp1/README.md | Participants turn over gain or loss cards and can stop a round to claim its current payout. The agent predicts participants' recorded responses from the information available before each response. | psych101-frey2017cct-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-frey2017cct-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Stop-or-Continue Card Choices |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-frey2017risk-exp1/README.md | 12,928,138 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-frey2017risk-exp1/README.md | Participants pump a balloon for points or stop to collect the accumulated points. The agent predicts participants' recorded responses from the information available before each response. | psych101-frey2017risk-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-frey2017risk-exp1/README.md | 0.2-byte-next-v1 | Predicting Human Balloon Pumping and Stopping |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-garcia2023experiential-exp1/README.md | 2,951,357 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-garcia2023experiential-exp1/README.md | Participants choose between reward options across three phases and later estimate each option's chance of winning. The agent predicts participants' recorded responses from the information available before each response. | psych101-garcia2023experiential-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-garcia2023experiential-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Reward Choices and Odds Estimates — Experiment 1 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-garcia2023experiential-exp2/README.md | 2,951,505 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-garcia2023experiential-exp2/README.md | Participants choose between reward options across three phases and later estimate each option's chance of winning. The agent predicts participants' recorded responses from the information available before each response. | psych101-garcia2023experiential-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-garcia2023experiential-exp2/README.md | 0.2-byte-next-v1 | Modeling Human Reward Choices and Odds Estimates — Experiment 2 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-garcia2023experiential-exp3/README.md | 2,917,546 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-garcia2023experiential-exp3/README.md | Participants choose between reward options across three phases and later estimate each option's chance of winning. The agent predicts participants' recorded responses from the information available before each response. | psych101-garcia2023experiential-exp3 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-garcia2023experiential-exp3/README.md | 0.2-byte-next-v1 | Modeling Human Reward Choices and Odds Estimates — Experiment 3 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-garcia2023experiential-exp4/README.md | 2,292,628 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-garcia2023experiential-exp4/README.md | Participants choose between reward options across three phases and later estimate each option's chance of winning. The agent predicts participants' recorded responses from the information available before each response. | psych101-garcia2023experiential-exp4 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-garcia2023experiential-exp4/README.md | 0.2-byte-next-v1 | Modeling Human Reward Choices and Odds Estimates — Experiment 4 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-gershman2018deconstructing-exp1/README.md | 319,085 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-gershman2018deconstructing-exp1/README.md | Participants choose between a variable slot machine and one that always gives zero points. The agent predicts participants' recorded responses from the information available before each response. | psych101-gershman2018deconstructing-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-gershman2018deconstructing-exp1/README.md | 0.1-public-validation | Human Choices Against a Fixed-Zero Slot Machine |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-gershman2018deconstructing-exp2/README.md | 321,792 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-gershman2018deconstructing-exp2/README.md | Participants repeatedly choose between two variable slot machines, one of which is better on average. The agent predicts participants' recorded responses from the information available before each response. | psych101-gershman2018deconstructing-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-gershman2018deconstructing-exp2/README.md | 0.1-public-validation | Human Choices Between Unequal Slot Machines |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-gershman2020reward-exp1/README.md | 2,322,153 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-gershman2020reward-exp1/README.md | Participants learn which of three keys earns a reward for each presented stimulus. The agent predicts participants' recorded responses from the information available before each response. | psych101-gershman2020reward-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-gershman2020reward-exp1/README.md | 0.2-byte-next-v1 | Human Stimulus–Response Learning from Rewards |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-hebart2023things-exp1/README.md | 156,881,709 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-hebart2023things-exp1/README.md | Participants select the least similar object from each three-object group. The agent predicts participants' recorded responses from the information available before each response. | psych101-hebart2023things-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-hebart2023things-exp1/README.md | 0.2-byte-next-v1 | Predicting Human Odd-One-Out Judgments |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-hilbig2014generalized-exp1/README.md | 591,437 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-hilbig2014generalized-exp1/README.md | Participants compare two fictional products using four expert ratings and choose one product. The agent predicts participants' recorded responses from the information available before each response. | psych101-hilbig2014generalized-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-hilbig2014generalized-exp1/README.md | 0.1-public-validation | Human Product Choices from Expert Ratings |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-jansen2021dunningkruger-exp1/README.md | 31,736,579 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-jansen2021dunningkruger-exp1/README.md | Participants estimate how many questions they will answer correctly on a 20-question grammar quiz. The agent predicts participants' recorded responses from the information available before each response. | psych101-jansen2021dunningkruger-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-jansen2021dunningkruger-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Grammar Quiz Self-Estimates |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-kool2016when-exp1/README.md | 2,813,931 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-kool2016when-exp1/README.md | Participants choose spaceships to collect treasure while avoiding antimatter from changing alien mines. The agent predicts participants' recorded responses from the information available before each response. | psych101-kool2016when-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-kool2016when-exp1/README.md | 0.2-byte-next-v1 | Predicting Human Spaceship Choices amid Treasure and Antimatter |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-kool2016when-exp2/README.md | 4,096,825 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-kool2016when-exp2/README.md | Participants choose spaceships and alien mines to collect treasure as mine rewards change. The agent predicts participants' recorded responses from the information available before each response. | psych101-kool2016when-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-kool2016when-exp2/README.md | 0.2-byte-next-v1 | Predicting Human Spaceship and Alien Choices for Treasure |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-kool2017cost-exp1/README.md | 3,703,289 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-kool2017cost-exp1/README.md | Participants choose spaceships for changing treasure rewards after learning whether a reward multiplier applies. The agent predicts participants' recorded responses from the information available before each response. | psych101-kool2017cost-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-kool2017cost-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Spaceship Choices with Treasure Multipliers |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-kool2017cost-exp2/README.md | 3,618,680 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-kool2017cost-exp2/README.md | Participants choose spaceships and alien mines for treasure after learning whether a reward multiplier applies. The agent predicts participants' recorded responses from the information available before each response. | psych101-kool2017cost-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-kool2017cost-exp2/README.md | 0.2-byte-next-v1 | Modeling Human Spaceship and Alien Choices with Multipliers |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-krueger2022identifying-exp1/README.md | 28,635,461 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-krueger2022identifying-exp1/README.md | Participants choose one of six gambles, then receive a payoff linked to the chosen gamble and a drawn ball color. The agent predicts participants' recorded responses from the information available before each response. | psych101-krueger2022identifying-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-krueger2022identifying-exp1/README.md | 0.2-byte-next-v1 | Human Choices Among Six Gambles |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-kumar2023disentangling-exp1/README.md | 42,562,418 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-kumar2023disentangling-exp1/README.md | Participants select unrevealed locations on a 7×7 grid and try to reveal ones while avoiding zeros. The agent predicts participants' recorded responses from the information available before each response. | psych101-kumar2023disentangling-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-kumar2023disentangling-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Choices on a Hidden Binary Grid |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-lefebvre2017behavioural-exp1/README.md | 450,595 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-lefebvre2017behavioural-exp1/README.md | Participants visit four casinos and choose between two slot machines to collect stochastic rewards. The agent predicts participants' recorded responses from the information available before each response. | psych101-lefebvre2017behavioural-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-lefebvre2017behavioural-exp1/README.md | 0.1-public-validation | Modeling Human Choice in a Casino Task |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-lefebvre2017behavioural-exp2/README.md | 339,457 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-lefebvre2017behavioural-exp2/README.md | Participants visit four casinos and choose between two machines that can return positive or negative points. The agent predicts participants' recorded responses from the information available before each response. | psych101-lefebvre2017behavioural-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-lefebvre2017behavioural-exp2/README.md | 0.1-public-validation | Modeling Human Casino Choices with Gains and Losses |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-levering2020revisiting-exp1/README.md | 1,229,159 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-levering2020revisiting-exp1/README.md | Participants classify objects by shape, size, and shading, then rate category typicality in a test block. The agent predicts participants' recorded responses from the information available before each response. | psych101-levering2020revisiting-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-levering2020revisiting-exp1/README.md | 0.2-byte-next-v1 | Human Object Classification and Typicality Ratings — Experiment 1 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-levering2020revisiting-exp2/README.md | 995,024 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-levering2020revisiting-exp2/README.md | Participants classify objects by shape, size, and shading, then rate category typicality in a test block. The agent predicts participants' recorded responses from the information available before each response. | psych101-levering2020revisiting-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-levering2020revisiting-exp2/README.md | 0.2-byte-next-v1 | Human Object Classification and Typicality Ratings — Experiment 2 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-ludwig2023human-exp0/README.md | 2,623,321 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-ludwig2023human-exp0/README.md | Participants move through a fruit market, collect fruits with two vitamins, and feed animals with different preferences. The agent predicts participants' recorded responses from the information available before each response. | psych101-ludwig2023human-exp0 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-ludwig2023human-exp0/README.md | 0.1-public-validation | Modeling Human Fruit-Market Navigation and Feeding — Experiment 0 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-ludwig2023human-exp1/README.md | 847,355 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-ludwig2023human-exp1/README.md | Participants move through a fruit market, collect fruits with two vitamins, and feed animals with different preferences. The agent predicts participants' recorded responses from the information available before each response. | psych101-ludwig2023human-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-ludwig2023human-exp1/README.md | 0.1-public-validation | Modeling Human Fruit-Market Navigation and Feeding — Experiment 1 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-ludwig2023human-exp2/README.md | 799,996 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-ludwig2023human-exp2/README.md | Participants move through a fruit market, collect fruits with two vitamins, and feed animals with different preferences. The agent predicts participants' recorded responses from the information available before each response. | psych101-ludwig2023human-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-ludwig2023human-exp2/README.md | 0.1-public-validation | Modeling Human Fruit-Market Navigation and Feeding — Experiment 2 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-peterson2021using-exp1/README.md | 185,996,551 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-peterson2021using-exp1/README.md | Participants choose between two gambles while some problems show obtained and missed outcomes and others suppress that feedback. The agent predicts participants' recorded responses from the information available before each response. | psych101-peterson2021using-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-peterson2021using-exp1/README.md | 0.2-byte-next-v1 | Human Gamble Choices under Varying Feedback |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-plonsky2018when-exp1/README.md | 14,186,060 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-plonsky2018when-exp1/README.md | Participants choose between two gambles over repeated encounters, first without feedback and then with both outcomes shown. The agent predicts participants' recorded responses from the information available before each response. | psych101-plonsky2018when-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-plonsky2018when-exp1/README.md | 0.2-byte-next-v1 | Human Gamble Choices before and after Feedback |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-popov2023intent-exp1/README.md | 897,131 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-popov2023intent-exp1/README.md | Participants judge word meanings, solve arithmetic problems, and later recall marked words from study lists. The agent predicts participants' recorded responses from the information available before each response. | psych101-popov2023intent-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-popov2023intent-exp1/README.md | 0.2-byte-next-v1 | Predicting Human Word Study and Recall — Experiment 1 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-popov2023intent-exp2/README.md | 722,615 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-popov2023intent-exp2/README.md | Participants judge paired word meanings, solve arithmetic problems, and later recall marked word pairs. The agent predicts participants' recorded responses from the information available before each response. | psych101-popov2023intent-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-popov2023intent-exp2/README.md | 0.2-byte-next-v1 | Predicting Human Word-Pair Study and Recall |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-popov2023intent-exp3/README.md | 1,081,021 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-popov2023intent-exp3/README.md | Participants judge word meanings, solve arithmetic problems, and later recall marked words from study lists. The agent predicts participants' recorded responses from the information available before each response. | psych101-popov2023intent-exp3 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-popov2023intent-exp3/README.md | 0.2-byte-next-v1 | Predicting Human Word Study and Recall — Experiment 3 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-ruggeri2022globalizability-exp1/README.md | 20,495,426 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-ruggeri2022globalizability-exp1/README.md | Participants state preferences between a payment now and a larger payment after a delay. The agent predicts participants' recorded responses from the information available before each response. | psych101-ruggeri2022globalizability-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-ruggeri2022globalizability-exp1/README.md | 0.2-byte-next-v1 | Human Preferences for Immediate and Delayed Payments |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-sadeghiyeh2020temporal-exp1/README.md | 2,735,477 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-sadeghiyeh2020temporal-exp1/README.md | Participants choose between two slot machines over multiple games and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-sadeghiyeh2020temporal-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-sadeghiyeh2020temporal-exp1/README.md | 0.2-byte-next-v1 | Human Slot-Machine Choices Across Games |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-schulz2020finding-exp1/README.md | 1,693,724 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-schulz2020finding-exp1/README.md | Participants choose among eight reward options over repeated rounds and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-schulz2020finding-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-schulz2020finding-exp1/README.md | 0.1-public-validation | Modeling Human Search Among Eight Reward Options — Experiment 1 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-schulz2020finding-exp2/README.md | 1,108,447 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-schulz2020finding-exp2/README.md | Participants choose among eight reward options over repeated rounds and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-schulz2020finding-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-schulz2020finding-exp2/README.md | 0.1-public-validation | Modeling Human Search Among Eight Reward Options — Experiment 2 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-schulz2020finding-exp3/README.md | 1,723,768 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-schulz2020finding-exp3/README.md | Participants choose among eight reward options over repeated rounds and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-schulz2020finding-exp3 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-schulz2020finding-exp3/README.md | 0.1-public-validation | Modeling Human Search Among Eight Reward Options — Experiment 3 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-schulz2020finding-exp4/README.md | 1,524,152 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-schulz2020finding-exp4/README.md | Participants choose among eight reward options over repeated rounds and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-schulz2020finding-exp4 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-schulz2020finding-exp4/README.md | 0.1-public-validation | Modeling Human Search Among Eight Reward Options — Experiment 4 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-schulz2020finding-exp5/README.md | 2,300,511 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-schulz2020finding-exp5/README.md | Participants choose among eight reward options over repeated rounds and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-schulz2020finding-exp5 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-schulz2020finding-exp5/README.md | 0.1-public-validation | Modeling Human Search Among Eight Reward Options — Experiment 5 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-somerville2017charting-exp1/README.md | 4,696,596 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-somerville2017charting-exp1/README.md | Participants choose between two slot machines over multiple games and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-somerville2017charting-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-somerville2017charting-exp1/README.md | 0.2-byte-next-v1 | Human Exploration of Two Slot Machines |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-speekenbrink2008learning-exp1/README.md | 497,343 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-speekenbrink2008learning-exp1/README.md | Participants judge whether combinations of one to three tarot cards predict rainy or fine weather. The agent predicts participants' recorded responses from the information available before each response. | psych101-speekenbrink2008learning-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-speekenbrink2008learning-exp1/README.md | 0.1-public-validation | Predicting Human Weather Judgments from Tarot Cards |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-steingroever2015data-exp1/README.md | 2,193,605 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-steingroever2015data-exp1/README.md | Participants draw cards from four decks and experience varying gains and occasional losses. The agent predicts participants' recorded responses from the information available before each response. | psych101-steingroever2015data-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-steingroever2015data-exp1/README.md | 0.1-public-validation | Human Choices from Four Card Decks — Experiment 1 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-steingroever2015data-exp2/README.md | 66,396 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-steingroever2015data-exp2/README.md | Participants draw cards from four decks and experience varying gains and occasional losses. The agent predicts participants' recorded responses from the information available before each response. | psych101-steingroever2015data-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-steingroever2015data-exp2/README.md | 0.1-public-validation | Human Choices from Four Card Decks — Experiment 2 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-steingroever2015data-exp3/README.md | 681,645 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-steingroever2015data-exp3/README.md | Participants draw cards from four decks and experience varying gains and occasional losses. The agent predicts participants' recorded responses from the information available before each response. | psych101-steingroever2015data-exp3 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-steingroever2015data-exp3/README.md | 0.1-public-validation | Human Choices from Four Card Decks — Experiment 3 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-tomov2020discovery-exp2/README.md | 12,159,394 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-tomov2020discovery-exp2/README.md | Participants plan routes from a start station to a goal through an unfamiliar subway network. The agent predicts participants' recorded responses from the information available before each response. | psych101-tomov2020discovery-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-tomov2020discovery-exp2/README.md | 0.2-byte-next-v1 | Predicting Human Subway Navigation — Experiment 2 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-tomov2020discovery-exp4/README.md | 3,021,550 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-tomov2020discovery-exp4/README.md | Participants plan routes from a start station to a goal through an unfamiliar subway network. The agent predicts participants' recorded responses from the information available before each response. | psych101-tomov2020discovery-exp4 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-tomov2020discovery-exp4/README.md | 0.2-byte-next-v1 | Predicting Human Subway Navigation — Experiment 4 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-tomov2020discovery-exp5/README.md | 12,369,221 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-tomov2020discovery-exp5/README.md | Participants plan routes from a start station to a goal through an unfamiliar subway network. The agent predicts participants' recorded responses from the information available before each response. | psych101-tomov2020discovery-exp5 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-tomov2020discovery-exp5/README.md | 0.2-byte-next-v1 | Predicting Human Subway Navigation — Experiment 5 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-tomov2020discovery-exp7/README.md | 8,601,475 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-tomov2020discovery-exp7/README.md | Participants plan routes from a start station to a goal through an unfamiliar subway network. The agent predicts participants' recorded responses from the information available before each response. | psych101-tomov2020discovery-exp7 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-tomov2020discovery-exp7/README.md | 0.2-byte-next-v1 | Predicting Human Subway Navigation — Experiment 7 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-tomov2021multitask-exp1/README.md | 5,475,976 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-tomov2021multitask-exp1/README.md | Participants choose doors between castle rooms to find wood, stone, and iron resources. The agent predicts participants' recorded responses from the information available before each response. | psych101-tomov2021multitask-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-tomov2021multitask-exp1/README.md | 0.2-byte-next-v1 | Modeling Human Castle-Room Exploration — Experiment 1 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-tomov2021multitask-exp3/README.md | 5,140,495 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-tomov2021multitask-exp3/README.md | Participants choose doors between castle rooms to find wood, stone, and iron resources. The agent predicts participants' recorded responses from the information available before each response. | psych101-tomov2021multitask-exp3 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-tomov2021multitask-exp3/README.md | 0.2-byte-next-v1 | Modeling Human Castle-Room Exploration — Experiment 3 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-waltz2020differential-exp1/README.md | 1,637,351 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-waltz2020differential-exp1/README.md | Participants choose between two slot machines over multiple games and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-waltz2020differential-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-waltz2020differential-exp1/README.md | 0.2-byte-next-v1 | Human Slot-Machine Choices Across Games |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-wilson2014humans-exp1/README.md | 3,234,483 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-wilson2014humans-exp1/README.md | Participants choose between two slot machines over multiple games and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-wilson2014humans-exp1 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-wilson2014humans-exp1/README.md | 0.1-public-validation | Human Two-Slot-Machine Choices — Experiment 1 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-wilson2014humans-exp2/README.md | 421,444 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-wilson2014humans-exp2/README.md | Participants choose between two slot machines over multiple games and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-wilson2014humans-exp2 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-wilson2014humans-exp2/README.md | 0.1-public-validation | Human Two-Slot-Machine Choices — Experiment 2 |
hosted_public | not_yet_available | v1 | psychology | minimize | mean_response_nll | https://github.com/NeuroAIHub/BrainPilotBench/blob/7306cc0b0e8958cc8f84918b68446310d8dbc662/tasks/psychology/psych101-wilson2014humans-exp3/README.md | 4,093,232 | 2 | https://github.com/NeuroAIHub/BrainPilotBench/blob/9d92b6738189d23607478ee1f71bdb6ff567d07b/tasks/psychology/psych101-wilson2014humans-exp3/README.md | Participants choose between two slot machines over multiple games and observe point outcomes. The agent predicts participants' recorded responses from the information available before each response. | psych101-wilson2014humans-exp3 | https://huggingface.co/datasets/BrainPilot-Bench/Tasks-Data-Public/blob/main/tasks/psychology/psych101-wilson2014humans-exp3/README.md | 0.1-public-validation | Human Two-Slot-Machine Choices — Experiment 3 |
BrainPilotBench v1 — task data
Public data for research tasks in brain science and human behavior. Agents use these data to develop neural decoders, predict human responses, and analyze relationships between brain measurements. The BrainPilotBench source repository provides task instructions, environments, submission formats, and scoring code.
112 tasks · 6 data categories · public data for 111 tasks
Agent benchmark results for the 108 new v1 tasks are not yet available. The four retained v0 tasks have separate historical results. Data preparation and fixed-reference checks are not Agent benchmark results.
Explore the tasks
| Data category | Tasks | Research problems |
|---|---|---|
| EEG | 25 | Classify motor imagery, evoked responses, sleep stages, and other scalp-EEG conditions. |
| Psychology | 76 | Model participants' recorded choices and responses from causal trial history. |
| MRI | 2 | Predict age from structural MRI and tonic pain from fMRI connectivity. |
| Connectomics | 1 | Compare anatomical connectivity with measured signal propagation in C. elegans. |
| Calcium imaging | 1 | Analyze RSC place-cell activity and decode behavior. |
| Invasive recordings | 7 | Decode movement, muscle activity, handwriting, film conditions, and birdsong from invasive neural recordings. |
Start with human casino choices, MRI age prediction, or ECoG finger-flexion decoding. Each task card explains the research question, available data, and evaluation, then links to its full guide.
The Dataset Viewer displays one row per task. It is a task directory, not a combined table of neural recordings or behavioral trials. The catalog and collection manifest provide machine-readable entries and counts.
Get data for one task
Each task has one stable location:
tasks/<data-category>/<task-id>/
README.md
manifest.json
public/<native-relative-path>
Read the task card and source terms before download.
Use its versioned GitHub guide to install the environment and prepare a workspace.
The task's data.lock pins exact HF commits, file sizes, and SHA-256 digests.
Native scientific formats remain unchanged.
Public files are hosted for 111 tasks, including BCI IV-4 and six invasive-recording tasks. H1 provides source download and local preparation instructions because its Data Use Agreement prohibits redistribution. The film task's public package supports Agent preparation. Block-first scoring still requires a trusted evaluator mapping that is not hosted here. Private evaluation files, owner mappings, and keys are excluded from this data repository.
Versions, source terms, and results
The v1 collection contains 108 new tasks and four retained v0 tasks.
Individual task versions define scientific protocols. HF commit IDs identify data snapshots.
Task cards link to versioned source guides. Directory navigation links use the current main tree.
The attribution guide records source-specific terms and citations. The root LICENSE does not grant an Apache license for all task data. BCI IV-4 retains its competition source terms. The film package excludes the copyrighted movie.
The unified access update is under review in PR #77. Use the linked source snapshot for its documented workflow until that update reaches GitHub main. The data preparation record separates delivery checks from Agent evaluation. Earlier immutable data URLs remain available in the publication history.
- Downloads last month
- 1,825