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Kevin O’Neill
@kevingoneill.github.io

happy to announce the official release of my first R package on CRAN! 🎉 building on the Hmetad toolbox by @smfleming.bsky.social, the hmetad package allows users to fit the meta-d' model of confidence ratings using a familiar brms/lme4-style formula syntax

metacoglab.github.iohmetad: an R package for hierarchical Bayesian modeling of confidence ratingsImplementation of Bayesian regressions over the meta-d' model of psychological data from two alternative forced choice tasks with ordinal confidence ratings. For more information, see Maniscalco & Lau...
Kevin O’Neill
@kevingoneill.github.io

for anyone unfamiliar with Steve's toolbox, the point of these models is to allow independent estimation of task-level psychological factors (i.e., stimulus sensitivity and response bias) from metacognitive factors (e.g., metacognitive sensitivity and bias) see this paper for reference:

columbia.educolumbia.edu
Kevin O’Neill
@kevingoneill.github.io

beyond a *ton* of efficiency upgrades 🚀, the package allows for arbitrary hierarchical structure, easy interfacing to other packages in the Stan ecosystem, simple computation of model-implied estimates (e.g., mean confidence, type 1/type 2 ROCs), and a bunch of other cool features

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Kevin O’Neill
@kevingoneill.github.io

another neat feature is that we have implemented a new measure of metacognitive bias that (unlike mean confidence) is independent from task-level performance and metacognitive sensitivity. we have a paper on this measure in progress so stay tuned!

megan peters 🧠
@meganakpeters.bsky.social

cool, would love to hear more about this! wondering how it fits with the optimal decision strategies we discussed last year: how can you measure bias unless you know what "unbiased" is, which changes based on the observer's decision policy? pmc.ncbi.nlm.nih.gov/articles/PMC...

pmc.ncbi.nlm.nih.govOptimal metacognitive decision strategies in signal detection theorySignal detection theory (SDT) has long provided the field of psychology with a simple but powerful model of how observers make decisions under uncertainty. SDT can distinguish sensitivity from response bias and characterize optimal decision ...