ArticleBehaviormetrika2023
Bayesian Modeling of the Mnemonic Similarity Task Using Multinomial Processing Trees.
Article in Behaviormetrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The Strength of Target Representations Drives Mnemonic Discrimination: Evidence From Extended Delay and Hippocampal Damage.Hippocampus · 2026Article
- Encoding-related fMRI BOLD activity predicts subsequent memory for studied scenes, but not subsequent identification of perceptually similar lures.bioRxiv : the preprint server for biology · 2026Article
- Divergent modes of episodic organization underlie whether emotional learning enhances memory across event boundaries.Psychonomic bulletin & review · 2026Article
- Towards a unified theory of false memory for similar episodes.Psychonomic bulletin & review · 2026Review
- MST in the wild: Optimizing the mnemonic similarity task for use in diverse environments.Neuropsychologia · 2026Article
- Misclassification in memory modification ineLife · 2025Article
- Cognitive modeling of lure discriminability in the Mnemonic Similarity Task.Behavior research methods · 2025Article
- A New Measure of Mnemonic Discrimination Applicable to Recognition Memory Tests With Continuous Variation in Novel Stimulus Interference.Brain and behavior · 2025Article
- Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's Disease.bioRxiv : the preprint server for biology · 2024Article
- A response time model of the three-choice Mnemonic Similarity Task provides stable, mechanistically interpretable individual-difference measures.Frontiers in human neuroscience · 2024Article
- Introduction to the Vol. 50, No. 2, 2023.Behaviormetrika · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a "similar" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.
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