ReviewBehavioural processes2017
Methods of comparing associative models and an application to retrospective revaluation.
Review in Behavioural processes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Testing models at the neural level reveals how the brain computes subjective value.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Behavior Stability and Individual Differences in Pavlovian Extended Conditioning.Frontiers in psychology · 2020Article
- An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation.Perspectives on behavior science · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
Contemporary theories of associative learning are increasingly complex, which necessitates the use of computational methods to reveal predictions of these models. We argue that comparisons across multiple models in terms of goodness of fit to empirical data from experiments often reveal more about the actual mechanisms of learning and behavior than do simulations of only a single model. Such comparisons are best made when the values of free parameters are discovered through some optimization procedure based on the specific data being fit (e.g., hill climbing), so that the comparisons hinge on the psychological mechanisms assumed by each model rather than being biased by using parameters that differ in quality across models with respect to the data being fit. Statistics like the Bayesian information criterion facilitate comparisons among models that have different numbers of free parameters. These issues are examined using retrospective revaluation data.
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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.