Discover datasets

This dataset comprises two reversal learning tasks designed to investigate differential neural processing of reward gain and punishment avoidance. Participants performed parallel tasks using distinct reinforcers: monetary reward (gain/non-gain outcomes) and primary threat avoidance (noise burst avoidance/application), with noise intensity individually titrated to match subjective value of monetary rewards. The dataset enables investigation of how the brain differentially encodes appetitive versus aversive learning contingencies.

Participants

26

Channels

66 (10-10)

Size

31.5 GB

Version

v1.0.0

Updated

Jul 10, 2026

…conversions for full BIDS compliance ### Behavioral Data (Audio) Vocal recordings are stored…

Participants

3

Channels

128 (other)

Size

49.6 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings from 21 participants learning to perform two distinct drumming patterns at three different tempos using keyboard responses. Participants received real-time visual feedback indicating response timing accuracy (early, on-time, or late) relative to target durations. The study investigates motor learning and timing control during sequential pattern acquisition, with detailed event markers capturing metronome beats, response types, and feedback outcomes across 72 trials per participant.

Participants

21

Channels

31 (10-10)

Size

4.76 GB

Version

v1.0.0

Updated

Jul 10, 2026

…targets and 2 distractors [2T2D]). Behavioral data were collected from two additional…

Participants

71

Size

77.1 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains EEG recordings from Experiment 2 of a study investigating whether memory reactivation levels are affected by anticipated interference during a dual-task paradigm. Participants performed a visual working memory task with varying cognitive load (single vs. dual task conditions), where they memorized lateral objects and later identified them among probes, with half the blocks including distractor identification tasks. The dataset includes raw EEG data collected during this experiment. For preprocessing details, see the related preprint and OSF repository listed in related_identifiers.

Participants

56

Channels

30 (10-10)

Size

40.1 GB

Version

v1.0.0

Updated

Jul 10, 2026

The TX22 dataset investigates real-time prediction and management of trust-based decisions during simulated semi-automated driving in a leader-follower paradigm. This neuroimaging study examines how operators' trust in autonomous vehicle systems can be predicted and influenced through targeted feedback interventions, with the goal of resolving discrepancies between perceived trust and actual system trustworthiness to optimize joint human-automation performance.

Participants

17

Channels

64 (10-10)

HED

v8.1.0

Size

22.3 GB

Version

v1.0.0

Updated

Jul 10, 2026

…The “sourcedata” folder contains the trial behavioral data collected during the EEG…

Participants

21

Channels

64

Size

35.5 GB

Version

v1.0.0

Updated

Jul 10, 2026

…contains MEG data in BIDS format, behavioral data and HPI position in…

Participants

22

Size

167 GB

Version

v1.0.0

Updated

Jul 10, 2026

This derivative dataset contains processed MEG data from a study investigating the neural mechanisms of model-based aversive learning in humans. The research examines how task state representations are preferentially reactivated during aversive learning, providing insights into the computational and neural basis of threat-related decision-making and learning processes.

Participants

28

Size

301 GB

Version

v1.0.0

Updated

Jul 10, 2026

…One participant was included only in the behavioral analysis because of abnormal…

Participants

32

Channels

64 (10-10)

Size

52.8 GB

Version

v1.0.0

Updated

Jul 10, 2026