authors:
  -
    firstname: "Stefan"
    lastname: "Appelhoff"
    affiliation: "Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany"
    id: "ORCID:0000-0001-8002-0877"
  -
    firstname: "Ralph"
    lastname: "Hertwig"
    affiliation: "Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany"
    id: "ORCID:0000-0002-9908-9556"
  -
    firstname: "Bernhard"
    lastname: "Spitzer"
    affiliation: "Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany"
    id: "ORCID:0000-0001-9752-932X"

title: "The mpib_ecomp_sourcedata dataset"

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.

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

# "reftype": IsSupplementTo, IsDescribedBy, or IsReferencedBy
# "id": doi:, arxiv:, or pmid:
# "citation": any string
references:
  -
    reftype: "IsSupplementTo"
    id: doi:10.1101/2022.03.31.486560
    citation: "Appelhoff, S., Hertwig, R. & Spitzer, B. EEG-representational geometries and psychometric distortions in approximate numerical judgment. (2022) doi:10.1101/2022.03.31.486560"
  -
    reftype: "IsDescribedBy"
    id: doi:10.5281/zenodo.6411313
    citation: "Appelhoff, Stefan. (2022). eComp Experiment Code (2022.1.0). Zenodo. https://doi.org/10.5281/zenodo.6411319"
  -
    reftype: "IsReferencedBy"
    id: doi:10.5281/zenodo.6411287
    citation: "Appelhoff, Stefan. (2022). eComp Analysis Code (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6411288"
  -
    reftype: "IsReferencedBy"
    citation: "https://gin.g-node.org/sappelhoff/mpib_ecomp_dataset"
  -
    reftype: "IsReferencedBy"
    citation: "https://gin.g-node.org/sappelhoff/mpib_ecomp_derivatives"

license:
  name: "Open Data Commons Public Domain Dedication and License (PDDL) v1.0"
  url: "https://opendatacommons.org/licenses/pddl/1-0/"

funding:
  - "Max Planck Institute for Human Development"

resourcetype: Dataset

templateversion: 1.2