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The `mpib_ecomp_sourcedata` dataset. https://gin.g-node.org/sappelhoff/mpib_ecomp_sourcedata

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README.md

The mpib_ecomp_sourcedata dataset

This dataset contains the sourcedata from the eComp experiment, conducted at the Max Planck Institute for Human Development (MPIB) in 2021, by Stefan Appelhoff and colleagues.

It is hosted on GIN: https://gin.g-node.org/sappelhoff/mpib_ecomp_sourcedata/

Note that all recording dates have been anonymized.

All important details are reported in the original paper for the project:

The raw data organized in Brain Imaging Data Structure (BIDS; https://bids.neuroimaging.io/) format are available here:

Derived data are available here:

The experiment code used during data collection can be found here:

The analysis code for this project can be found here:

Overview

This is a very condensed overview of the data. For a full understanding, please see the materials linked above, and contact the author in case of doubt.

Each subject folder contains the following files:

  • Two *_info.json files: One for each task condition (called "stream": single and dual) with general participant information
  • Two directories: "single" and "dual", containing the following data for the two task conditions respectively:
  • EEG data: .eeg, .vhdr. .vmrk BrainVision files
  • Behavior data: .tsv file
    • This is a very brief description of the columns:
      • trial: the trial index (integers starting with 0)
      • direction: did participant press "left" or "right" button; is "n/a" if no response (strings)
      • choice: in single --> "lower" vs "higher"; in dual --> "red" vs "blue"; is "n/a" if no response (strings)
      • ambiguous: True/False whether the mean was exactly 5 (single), or the mean difference was exactly 0 (dual) --> in those cases, participants received random feedback (booleans)
      • rt: reaction time in seconds (floats), is "n/a" if no response (string) --> so in this column there may be floats and strings
      • validity : True/False --> is False, if there was a timeout in the choice. Happened rarely --> 6 times over all subjs and trials. If True, "direction", "choice", "rt", and "correct" are "n/a" as a value
      • iti: The inter trial interval for this trial in ms
      • correct: True/False whether the choice was correct (this is random in ambiguous trials; booleans); is "n/a" if no response (string) --> so in this column there may be booleans and strings
      • stream: The stream that this data was produced in: "single" or "dual" (strings)
      • state: 0 or 1, used for presenting the response options one way or the other way around (integers), irrelevant for analysis, because this is already more accessible encoded in "direction"+"choice"
      • sample1 ... sample10: the 1st, 2nd, ... 10th sample in this trial: Integer between 1 and 9 with either positive or negative sign, mapping to blue or red color respectively
  • Eyetracking data: .edf EyeLink file (this is NOT the "European Data Format", but the "Eyelink Data format")
    • Event markers have the same meaning as in the EEG data

Download

The full data can be downloaded using one of these two methods:

  1. Via datalad
    1. install: http://handbook.datalad.org/en/latest/intro/installation.html
    2. clone: datalad clone https://gin.g-node.org/sappelhoff/mpib_ecomp_sourcedata
    3. go to root of dataset: cd mpib_ecomp_sourcedata
    4. get all data: datalad get *
    5. if you want to work on the files or edit them, you may need to run datalad unlock *
  2. Via the GIN client
    1. install: https://gin.g-node.org/G-Node/Info/wiki/GIN+CLI+Setup
    2. clone: gin get sappelhoff/mpib_ecomp_sourcedata
    3. go to root of dataset: cd mpib_ecomp_sourcedata
    4. get all data: gin download --content
    5. if you want to work on the files or edit them, you may need to run gin unlock *

Or click on the small "download icon" on the right side above the list of files in the repository overview on GIN. This will allow you to see the GIN documentation on how to download data.

Using this dataset

If you use this dataset in your work, please consider citing it as well as the references describing it.

See the LICENSE below, as well as the datacite.yml file.

License

This data is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/

See also the human readable summary at: https://opendatacommons.org/licenses/pddl/summary/

Please see the LICENSE file for details.

Contact

datacite.yml
Title The mpib_ecomp_sourcedata dataset
Authors Appelhoff,Stefan;Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany;ORCID:0000-0001-8002-0877
Hertwig,Ralph;Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany;ORCID:0000-0002-9908-9556
Spitzer,Bernhard;Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany;ORCID:0000-0001-9752-932X
Description When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks.
License Open Data Commons Public Domain Dedication and License (PDDL) v1.0 (https://opendatacommons.org/licenses/pddl/1-0/)
References Appelhoff S, Hertwig R, Spitzer B (2022) EEG-representational geometries and psychometric distortions in approximate numerical judgment. PLoS Comput Biol 18(12): e1010747. https://doi.org/10.1371/journal.pcbi.1010747 [doi:10.1371/journal.pcbi.1010747] (IsSupplementTo)
Appelhoff, Stefan. (2022). eComp Experiment Code (2022.1.0). Zenodo. https://doi.org/10.5281/zenodo.6411319 [doi:10.5281/zenodo.6411313] (IsDescribedBy)
Appelhoff, Stefan. (2022). eComp Analysis Code (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6411288 [doi:10.5281/zenodo.6411287] (IsReferencedBy)
Appelhoff S, Hertwig R, Spitzer B (2022) The mpib_ecomp_dataset (BIDS). G-Node. https://doi.org/10.12751/g-node.jtfg5d [doi:10.12751/g-node.jtfg5d] (IsReferencedBy)
Appelhoff S, Hertwig R, Spitzer B (2022) The mpib_ecomp_derivatives dataset. G-Node. https://doi.org/10.12751/g-node.9rtg6f [doi:10.12751/g-node.9rtg6f] (IsReferencedBy)
Funding Max Planck Institute for Human Development
Keywords cognitive neuroscience
decision-making
numerical cognition
sequential sampling
value distortions
compression
anti-compression
EEG
electroencephalography
eyetracking
representational similarity analysis
RSA
multivariate pattern analysis
MVPA
computational modeling
Resource Type Dataset