DOI: 10.6084/m9.figshare.1281118.v1
title: Dataset for "Auditory feedback in error-based learning of motor regularity"

description: <br>This is the empirical data that was collected for the study "Auditory feedback in error-based learning of motor regularity" by Floris T. van Vugt<br>and<br>Barbara Tillmann This is the dataset for the article published in the journal Brain Research: 10.1016/j.brainres.2015.02.026 http://www.sciencedirect.com/science/article/pii/S0006899315001262 Data collected: 2012, Lyon, France; We make this data available freely online to aid future researchers. If you end up using this data or any part of it, please acknowledge the source and cite our paper. More details:<br>http://www.florisvanvugt.com DATA STRUCTURE This contains data for 36 participants who learned to tap a sequence of 7 digits over the course of two days. Please refer to the original paper for full details.<br>Participants are assigned an anonymous ID. A list of participants, the experimental conditions they are in, and the sequences on which they are trained, are in the file participants.txt. QUESTIONNAIRE Basic details about the participants are in the folder "questionnaire" in the file AccompanyingQuestionnaireResults.csv. This lists handedness, age, sex, musical experience, etc. The original paper questionnaire is also included in PDF form. SEQUENCE TAPPING The main experimental files are found in the folder "sequence_learning". Each participant has four files, two per day, one of which lists basic information about each block (the file {name}.blocks.txt), and one that contains timing information for each trial in each block (the file {name}.trials.txt). {name}.blocks.txt<br>The columns of this file are: PARTICIPANT (the participant ID), CONDITION (the feedback condition, listed as normal, mute or jitter; see the paper for details), BLOCK (the name of the block, please refer to Table 1 in the original paper), SEQUENCE (the numeric representation of the sequence that was tapped during that block), METRONOME (the IOI of the visual count-in cue, in seconds; where 0 means that no visual count-in cue was presented), TBEGIN is the exact timestamp of the beginning of the block. {name}.trials.txt<br>Each line in this file corresponds to one keystroke.<br>The columns of this file are: PARTICIPANT (the participant ID), BLOCK (the name of the block, please refer to Table 1 in the original paper for details), TRIAL (the trial number within the block), N (the number of the keystroke in that trial, from 1 to 7, if the sequence was tapped correctly), RESPONSE (the digit corresponding to the key that was pressed), PYTHONTIME (the timestamp assigned by Python when the key was pressed; this timestamp is probably less accurate because Python's interface with the computer hardware is probably not very reliable), TIME (the timestamp that was associated with this keystroke; this timestamp is assigned by the custom button box that we used for the experiment and is therefore very precise). In each case, {name} contains the participant ID (for easy reference), as well as the date and time that this participant was recorded, and the condition (normal, mute or jitter). TAPPING Each participant was furthermore recorded in a synchronisation-continuation tapping paradigm. For each participant, you will find three files, pertaining to three blocks that were tested (see the supplementary materials to the original paper). Each line in the data files pertains to one keystroke or one metronome click. In this way, you can easily relate the keystrokes and the metronome clicks.<br>The columns in this file are: PARTICIPANT (the participant ID), BLOCK (the number of the block, from 1 to 3), EVENTTYPE (the type of event, KEYDN refers to the time the key was pressed down, and CLICKON/CLICKOFF refers to the time the metronome started or stopped), and T was the timestamp assigned by the button box. The metronome click onsets and offsets were detected by the voice-onset detector built in to the button box. ANISOCHRONY / DELAY DETECTION We include data for basic psychophysical thresholds for anisochrony (detection of a delay in a train of regularly timed tones) and delay detection (detection of a delay between a keystroke and a subsequent tone). This data was collected using PythonMLP. The PythonMLP outputs are organised as follows. For each task, each participant and each block, two files are created. One file contains the metadata (FILENAME.metadata.txt) and the other file (FILENAME.txt) contains the full details of all trials in the block. For details about how this output is created, please refer to the pythonMLP package ( https://github.com/florisvanvugt/PythonMLP ). In brief, the trial listing of each block is organised as follows: PARTICIPANT TRIAL TYPE STIMULUS RESPONSE<br>B9-activeagency-2 0 mlp 600.000000 1<br>B9-activeagency-2 1 mlp 173.411931 1<br>B9-activeagency-2 2 mlp -3.890364 0<br>B9-activeagency-2 3 mlp 90.998540 1<br>B9-activeagency-2 4 mlp 48.928423 0<br>B9-activeagency-2 5 catch 0.000000 0<br>[...] Each line corresponds to one trial.<br>- "PARTICIPANT" lists the participant number, a dash, the task (delay detection is referred to as "activeagency" for historical reasons), another dash, and the block number (from 1 to 3).<br>- "TYPE" corresponds to the trial type: catch trials (with zero stimulus level) or mlp trials (in which the stimulus level is determined by the MLP algorithm).<br>- "STIMULUS" lists the stimulus level (stimulus level refers to the amount of delay in msec between keystroke and sound in the delay detection task; and amount of lateness of the fourth tone in the anisochrony task).<br>- "RESPONSE" encodes the participants' response. In the case of the delay detection, 0 stands for "immediate" and 1 stands for "delayed". In the case of the anisochrony test, 0 stands for "regular" and 1 stands for "irregular". Note: for historical reasons, we have called the delay detection trials "activeagency". This is why you will see it pop up occasionally when you go through this data.