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.
Highlights
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
General Condorcet Model for Recognition (GCMR) is an Item response theory (IRT) model, which separately parametrizes item difficulty and discrimination ability to enable ability estimation to be independent of the item set presented
Using both simulated and real data, we show that the proposed GCMR model outperforms both the Rasch an 2HT models in modeling old-new recognition memory data
Summary
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. 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. Modeling of recognition memory has largely seen two classes of stochastic models: signal-detection theory (SDT) models[2] and threshold models[3]. Both make predictions about the latent processes leading to mnemonic decision making. When comparing the 2HT model with the standard SDT model (d′ ), Snodgrass and C orwin[3] noted that ability and bias parameters from the 2HT model were generally more sensitive to disease status
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