RIDER_data/datacite.yml

authors:
  -
    firstname: "Frieda"
    lastname: "Born"
    affiliation: "Adaptive Memory and Decision Making (AMD), Max Planck Institute for Human Development,Berlin, Germany"
    id: "ORCID:0009-0002-1214-4864"
  -
    firstname: "Bernhard"
    lastname: "Spitzer"
    affiliation: "Adaptive Memory and Decision Making (AMD), Max Planck Institute for Human Development, Berlin, Germany"
    id: "ORCID:0000-0001-9752-932X"

title: "The RIDER_data"

description: |
  Which factors determine whether information temporarily held in working memory (WM) is transferred to long-term memory (LTM)?
  Previous work has shown that retrieving (“testing”) memories from LTM can benefit their future LTM recall.
  Here, we examined the extent to which a benefit for subsequent LTM may also occur after retrieval from WM, depending on whether the WM contents were retrieved from a prioritized or deprioritized state.
  In three experiments, we combined variants of a novel visual WM paradigm with a subsequent surprise LTM recall test.

keywords:
  - cognitive neuroscience
  - workig memory
  - long-term memory
  - attention
  - behavior


references:
  -
    id: "doi:tba"
    reftype: "IsSupplementTo"
    citation: "Born, F., Spitzer, B.: Long-Term Effects of Working Memory Retrieval From Prioritized and Deprioritized States."
  -
    id: doi:10.5281/zenodo.13867139
    reftype: "IsReferencedBy"
    citation: "Born Frieda. (2024). RIDER Analysis Code (v1.0.0)"
  -
    id: doi:10.5281/zenodo.13867798
    reftype: "IsReferencedBy"
    citation: "Born Frieda. (2024). RIDER Experiment Code (v1.0.0)"

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

funding:
  - "EU; ERC grant ERC-2020-COG-101000972"
  - "DFG; 462752742"

resourcetype: Dataset

templateversion: 1.1