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Publication Detail
Bayesian modeling of item heterogeneity in dichotomous recognition memory data and prospects for computerized adaptive testing
  • Publication Type:
    Journal article
  • Authors:
    Guesten J, Berron D, Duezel E, Ziegler G
  • Publisher:
    NATURE PORTFOLIO
  • Publication date:
    24/01/2022
  • Journal:
    Scientific Reports
  • Volume:
    12
  • Issue:
    1
  • Article number:
    1250
  • Medium:
    Electronic
  • Status:
    Published
  • Country:
    England
  • PII:
    10.1038/s41598-022-04997-3
  • Language:
    English
  • Keywords:
    Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, MEDIAL TEMPORAL-LOBE, RESPONSE THEORY, MNEMONIC DISCRIMINATION, RECOLLECTION, FAMILIARITY, OBJECTS, BIAS
  • Notes:
    This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Abstract
Most current models of recognition memory fail to separately model item and person heterogeneity which makes it difficult to assess ability at the latent construct level and prevents the administration of adaptive tests. Here we propose to employ a General Condorcet Model for Recognition (GCMR) in order to estimate ability, response bias and item difficulty in dichotomous recognition memory tasks. Using a Bayesian modeling framework and MCMC inference, we perform 3 separate validation studies comparing GCMR to the Rasch model from IRT and the 2-High-Threshold (2HT) recognition model. First, two simulations demonstrate that recovery of GCMR ability estimates with varying sparsity and test difficulty is more robust and that estimates improve from the two other models under common test scenarios. Then, using a real dataset, face validity is confirmed by replicating previous findings of general and domain-specific age effects (Güsten et al. in Cortex 137:138–148, https://doi.org/10.1016/j.cortex.2020.12.017, 2021). Using cross-validation we show better out-of-sample prediction for the GCMR as compared to Rasch and 2HT model. In addition, we present a hierarchical extension of the model that is able to estimate age- and domain-specific effects directly, without recurring to a two-stage procedure. Finally, an adaptive test using the GCMR is simulated, showing that the test length necessary to obtain reliable ability estimates can be significantly reduced compared to a non-adaptive procedure. The GCMR allows to model trial-by-trial performance and to increase the efficiency and reliability of recognition memory assessments.
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