IDEAS home Printed from https://ideas.repec.org/p/mnh/spaper/2314.html
   My bibliography  Save this paper

The revelation effect for autobiographical memory : a mixture-model analysis

Author

Listed:
  • Bernstein, Daniel M.
  • Rudd, Michael E.
  • Erdfelder, Edgar
  • Godfrey, Ryan
  • Loftus, Elizabeth F.

Abstract

Participants provided information about their childhood by rating the confidence that they had experienced various events (e.g., 'broke a window playing ball'). On some trials, participants unscrambled a key word from the event-phrase (e.g., wdinwo – window) or an unrelated word (e.g., gnutge – nugget) before seeing the event and giving their confidence rating. Unscrambling led participants to increase their confidence that the event occurred in their childhood, but only when the confidence rating immediately followed the act of unscrambling. This increase in confidence mirrors the “revelation effect” observed in word recognition experiments. We analyze our data using a new signal detection mixture distribution model which does not require that the researcher knows the veracity of memory judgments a priori. Our analysis reveals that unscrambling a key word or an unrelated word affects response bias and discriminability in autobiographical memory tests in ways that are very similar to those that have been previously found for word recognition tasks.

Suggested Citation

  • Bernstein, Daniel M. & Rudd, Michael E. & Erdfelder, Edgar & Godfrey, Ryan & Loftus, Elizabeth F., 2008. "The revelation effect for autobiographical memory : a mixture-model analysis," Papers 08-25, Sonderforschungsbreich 504.
    • Handle: RePEc:mnh:spaper:2314
    as

    Download full text from publisher

    File URL: https://madoc.bib.uni-mannheim.de/2314/1/dp08_25.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeffrey Rouder & Jun Lu & Dongchu Sun & Paul Speckman & Richard Morey & Moshe Naveh-Benjamin, 2007. "Signal Detection Models with Random Participant and Item Effects," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 621-642, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark Steyvers & Thomas S. Wallsten & Edgar C. Merkle & Brandon M. Turner, 2014. "Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models," Risk Analysis, John Wiley & Sons, vol. 34(3), pages 435-452, March.
    2. Dora Matzke & Conor Dolan & William Batchelder & Eric-Jan Wagenmakers, 2015. "Bayesian Estimation of Multinomial Processing Tree Models with Heterogeneity in Participants and Items," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 205-235, March.
    3. Sun-Joo Cho & Sarah Brown-Schmidt & Paul De Boeck & Matthew Naveiras & Si On Yoon & Aaron Benjamin, 2023. "Incorporating Functional Response Time Effects into a Signal Detection Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1056-1086, September.
    4. Paul Boeck, 2008. "Random Item IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 533-559, December.
    5. T. Loeys & Y. Rosseel & K. Baten, 2011. "A Joint Modeling Approach for Reaction Time and Accuracy in Psycholinguistic Experiments," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 487-503, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mnh:spaper:2314. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Katharina Rautenberg (email available below). General contact details of provider: https://edirc.repec.org/data/sfmande.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.