ArticlePsychological review2013
Likelihood-free Bayesian analysis of memory models.
Article in Psychological review, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Processing of different word list lengths during encoding and retrieval in Broca's area.Frontiers in human neuroscience · 2026Article
- The Cognitive Lens: a primer on conceptual tools for analysing information processing in developmental and regenerative morphogenesis.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation.Cognitive science · 2019Article
- Thermodynamic integration via differential evolution: A method for estimating marginal likelihoods.Behavior research methods · 2019Article
- Some task demands induce collapsing bounds: Evidence from a behavioral analysis.Psychonomic bulletin & review · 2018Article
- Determining informative priors for cognitive models.Psychonomic bulletin & review · 2018Article
- Approaches to Analysis in Model-based Cognitive Neuroscience.Journal of mathematical psychology · 2017Article
- Hierarchical approximate Bayesian computation.Psychometrika · 2014Article
- A generalized, likelihood-free method for posterior estimation.Psychonomic bulletin & review · 2014Review
- Double jeopardy in inferring cognitive processes.Frontiers in psychology · 2014Article
- A method for efficiently sampling from distributions with correlated dimensions.Psychological methods · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Many influential memory models are computational in the sense that their predictions are derived through simulation. This means that it is difficult or impossible to write down a probability distribution or likelihood that characterizes the random behavior of the data as a function of the model's parameters. In turn, the lack of a likelihood means that these models cannot be directly fitted to data using traditional techniques. In particular, standard Bayesian analyses of such models are impossible. In this article, we examine how a new procedure called approximate Bayesian computation (ABC), a method for Bayesian analysis that circumvents the evaluation of the likelihood, can be used to fit computational models to memory data. In particular, we investigate the bind cue decide model of episodic memory (Dennis & Humphreys, 2001) and the retrieving effectively from memory model (Shiffrin & Steyvers, 1997). We fit hierarchical versions of each model to the data of Dennis, Lee, and Kinnell (2008) and Kinnell and Dennis (2012). The ABC analysis permits us to explore the relationships between the parameters in each model as well as evaluate their relative fits to data-analyses that were not previously possible.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.