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ManuelRausch committed May 31, 2024
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2 changes: 1 addition & 1 deletion TestScript.R
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Expand Up @@ -306,7 +306,7 @@ recov_metaDprime_ML <-
filter(participant!=11) %>% # subject 11 performed very low.
select(participant, d_3, c:theta_plus.4, m) %>%
rename(d_1 = d_3) %>%
mutate(N = 400) %>% # simulate 400 trials because 400 trials considered to be required to estimate meta-d′/d′
mutate(N = 10000) %>% #
group_by(participant) %>%
simConf(model="ITGcm") %>%
fitMetaDprime(model="ML", .parallel = TRUE)
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88 changes: 2 additions & 86 deletions paper.bib
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Expand Up @@ -256,7 +256,7 @@ @article{hellmann_simultaneous_2023
@article{rahnev_consensus_2022,
title = {Consensus Goals in the Field of Visual Metacognition},
volume = {17},
doi = {10.1177/174569162210756},
doi = {10.1177/17456916221075615},
abstract = {Despite the tangible progress in psychological and cognitive sciences over the last several years, these disciplines still trail other more mature sciences in identifying the most important questions that need to be solved. Reaching such consensus could lead to greater synergy across different laboratories, faster progress, and increased focus on solving important problems rather than pursuing isolated, niche efforts. Here, 26 researchers from the field of visual metacognition reached consensus on four long-term and two medium-term common goals. We describe the process that we followed, the goals themselves, and our plans for accomplishing these goals. If this effort proves successful within the next few years, such consensus building around common goals could be adopted more widely in psychological science.},
pages = {1746--1765},
number = {6},
Expand Down Expand Up @@ -290,6 +290,7 @@ @book{hautus_detection_2021
@article{egan_operating_1959,
title = {Operating Characteristics Determined by Binary Decisions and by Ratings},
volume = {31},
doi = {10.1121/1.1907783},
pages = {768--773},
number = {6},
journaltitle = {Journal of the Acoustical Society of America},
Expand Down Expand Up @@ -327,88 +328,3 @@ @misc{hellmann_confidence_2024
langid = {english},
file = {Hellmann et al. - 2023 - Confidence is influenced by evidence accumulation .pdf:C\:\\Users\\mru\\Zotero\\storage\\XRR256WT\\Hellmann et al. - 2023 - Confidence is influenced by evidence accumulation .pdf:application/pdf},
}

@online{shekhar_nature_2020,
title = {The nature of metacognitive inefficiency in perceptual decision making},
url = {https://osf.io/s8fnb/},
abstract = {Hosted on the Open Science Framework},
author = {Shekhar, Medha and Rahnev, Dobromir},
urldate = {2024-04-04},
date = {2020-04-03},
note = {Publisher: {OSF}},
file = {Snapshot:C\:\\Users\\mru\\Zotero\\storage\\DFGNZ5E7\\s8fnb.html:text/html},
}

@online{shekhar_how_2022,
title = {How do humans give confidence? A comprehensive comparison of process models of metacognition},
url = {https://osf.io/g8f9x/},
shorttitle = {How do humans give confidence?},
abstract = {Hosted on the Open Science Framework},
author = {Shekhar, Medha and Rahnev, Dobromir},
urldate = {2024-04-04},
date = {2022-03-30},
note = {Publisher: {OSF}},
file = {Snapshot:C\:\\Users\\mru\\Zotero\\storage\\7Y9RWYDA\\g8f9x.html:text/html},
}

@online{rausch_full_2017,
title = {Full material to "Confidence in masked orientation judgments is informed by both evidence and visibility"},
url = {https://osf.io/ty4h8/},
abstract = {Hosted on the Open Science Framework},
author = {Rausch, Manuel and Zehetleitner, Michael},
urldate = {2024-04-04},
date = {2017-04-07},
note = {Publisher: {OSF}},
file = {Snapshot:C\:\\Users\\mru\\Zotero\\storage\\BCV2C5UX\\ty4h8.html:text/html},
}

@online{rausch_full_2022,
title = {Full material to "Measures of metacognitive efficiency across cognitive models of decision confidence"},
url = {https://osf.io/72uds/},
abstract = {Abstract
Meta-d’/d’ has become the quasi-gold standard to quantify metacognitive efficiency because meta-d’/d’ is widely believed to control for discrimination performance, discrimination criteria, and confidence criteria even without the assumption of a specific generative model underlying confidence judgments. Using simulations, we demonstrate that meta-d’/d’ is not as model-free as previously thought: Only when we simulated data using a generative model of confidence according to which the evidence underlying confidence judgements is sampled independently from the evidence utilized in the choice process from a truncated Gaussian distribution, meta-d’/d’ was unaffected by discrimination performance, discrimination task criteria, and confidence criteria. According to five alternative generative models of confidence, there exist at least some combination of parameters where meta-d’/d’ is affected by discrimination performance, discrimination criteria and confidence criteria. A simulation using empirically fitted parameter sets showed that the magnitude of the correlation between meta-d’/d’ and discrimination performance, discrimination task criteria, and confidence criteria depends heavily on the generative model and the specific parameter and varies between negligibly small and very large. These simulations imply that a difference in meta-d’/d’ between conditions does not necessarily reflect a difference in metacognitive efficiency but might as well be caused by a difference in discrimination performance, discrimination task criterion, or confidence criteria.
Flies
This files provide all code necessary to replicate the results and Figures.
All analyses were performed using version 4.0.3 running on Windows 10, using the R packages tidyverse 1.3.0, plyr 1.8.7, ggplot2 3.3.3, rjags 4.13, coda 0.19.4., R.utils 2.12.0, {gridExtra} 2.3, Rmisc 1.5.1, snow 0.4.0 and {doSNOW} 1.0.20.
To replicate Simulation 1, download all files to your local computer and place all files in the same directory. Then, run the file "Mratio\_byModels\_analysis.R.R". The file Mratio\_Plotting\_v2023.R contains the code to plot the data. The file "Mratio\_PlotCognitiveModelsAndMethods.{RData}" contains all the Figures.
To replicate Simulation 2, run the file "Reanalyze\_RouaultEtAl2018Exp2\_ConfidenceModels.R". Depending on your hardware, this might take a while. The results are stored in the files "Correlations\_RouaultEtAlExp2\_ConfidenceModels.{RData}", "Simulations\_RouaultEtAlExp2\_ConfidenceModels.{RData}", and "{ModelFits}\_RouaultEtAlExp2\_ConfidenceModels.{RData}".
Hosted on the Open Science Framework},
author = {Rausch, Manuel},
urldate = {2024-04-04},
date = {2022-10-21},
note = {Publisher: {OSF}},
file = {Snapshot:C\:\\Users\\mru\\Zotero\\storage\\E8PLF38I\\72uds.html:text/html},
}

@online{rausch_full_2018,
title = {Full material to "Cognitive modelling reveals distinct electrophysiological markers of decision confidence and error monitoring"},
url = {https://osf.io/93weg/},
author = {Rausch, Manuel and Zehetleitner, Michael and Steinhauser, Marco and Maier, Martin E},
urldate = {2024-04-04},
date = {2018},
file = {OSF | Full material to "Cognitive modelling reveals distinct electrophysiological markers of decision confidence and error monitoring":C\:\\Users\\mru\\Zotero\\storage\\LBPH7N8D\\93weg.html:text/html},
}

@software{maniscalco_type_2020,
title = {Type 2 signal detection theory analysis using meta-d'},
url = {https://www.columbia.edu/~bsm2105/type2sdt/},
author = {Maniscalco, Brian},
date = {2020-07-23},
}

@letter{noauthor_notitle_nodate,
type = {E-mail},
}

@software{fleming_hmeta-d_2017,
title = {{HMeta}-d},
url = {https://github.com/metacoglab/HMeta-d},
author = {Fleming, Stephen M.},
date = {2017},
}
2 changes: 1 addition & 1 deletion paper.md
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Expand Up @@ -35,7 +35,7 @@ We present the `statConfR` package for R, which allows researchers to convenient

Cognitive models of confidence are currently used implicitly and explicitly in a wide range of research areas in the cognitive sciences: In perception research, confidence judgments can be used to quantify perceptual sensitivity based on receiver operating characteristics [@egan_operating_1959], a method based on the signal detection rating model [@Green1966; @hautus_detection_2021]. In metacognition research, the most popular measure of metacognitive performance, the meta-d$^\prime$/d$^\prime$ method [@Maniscalco2012; @Maniscalco2014], implicitly relies on the independent truncated Gaussian model [@rausch_measures_2023]. Finally, confidence models have become a flourishing research topic in their own right [@boundy-singer_confidence_2022; @Desender2021; @guggenmos_reverse_2022; @hellmann_confidence_2024; @hellmann_simultaneous_2023; @pereira_evidence_2021; @Rausch2018; @Rausch2020; @Shekhar2020a; @shekhar_how_2023]. However, too few studies have empirically compared different confidence models [@Rausch2018; @Rausch2020; @rausch_measures_2023; @Shekhar2020a; @shekhar_how_2023], so there is still no consensus about the computational principles underlying confidence judgments [@rahnev_consensus_2022]. This is problematic because meta-d$^\prime$/d$^\prime$ can be biased by discrimination sensitivity, discrimination criteria, and/or confidence criteria if the generative model underlying the data is not the independent truncated Gaussian model [@rausch_measures_2023]. Likewise, receiver operating characteristics in rating experiments are only appropriate measures of discrimination sensitivity if the assumptions of the signal detection rating model are correct [@Green1966; @hautus_detection_2021].

At the time of writing, `statConfR` is the only available package for an open software that allows researchers to fit a set of static models of decision confidence. The ReMeta toolbox provides functions for MATLAB to also fit a variety of different confidence models [@guggenmos_reverse_2022], but some important models such as the independent truncated Gaussian model are missing. Previous studies modelling confidence have made their analysis scripts freely available on the OSF website [@rausch_full_2017; @rausch_full_2018; @rausch_full_2022; @shekhar_nature_2020; @shekhar_how_2022], but these analysis scripts are often tailored to specific experiments and require time and effort to adapt to new experiments. In addition, the documentation of these scripts is not always sufficient to be used without export knowledge in cognitive modelling. Finally, the lognormal noise model and the lognormal weighted evidence and visibility model were previously only available implemented in MATLAB, so `statConfR` makes these confidence models available to researchers who do not have access to MATLAB. The `statConfR` package also provides a faithful implementation of meta-d$^\prime$/d$^\prime$, which has been originally implemented in MATLAB [@maniscalco_type_2020]. Fleming provides R code for Hmetad, a Bayesian hierarichical version of meta-d$^\prime$/d$^\prime$ [@fleming_hmeta-d_2017], but notably the model specification used for Hmetad is not the same as in meta-d$^\prime$/d$^\prime$ [@rausch_measures_2023].
At the time of writing, `statConfR` is the only available package for an open software that allows researchers to fit a set of static models of decision confidence. The ReMeta toolbox provides functions for MATLAB to also fit a variety of different confidence models [@guggenmos_reverse_2022], but some important models such as the independent truncated Gaussian model are missing. Previous studies modelling confidence have made their analysis scripts freely available on the OSF website [ @Rausch2018; @Rausch2020; @rausch_measures_2023; @Shekhar2020a; @shekhar_how_2023], but these analysis scripts are often tailored to specific experiments and require time and effort to adapt to new experiments. In addition, the documentation of these scripts is not always sufficient to be used without export knowledge in cognitive modelling. Finally, the lognormal noise model and the lognormal weighted evidence and visibility model were previously only available implemented in MATLAB, so `statConfR` makes these confidence models available to researchers who do not have access to MATLAB. The `statConfR` package also provides a faithful implementation of meta-d$^\prime$/d$^\prime$, which has been originally implemented in MATLAB [@maniscalco2012]. Fleming provides MATLAB and R code for Hmetad, a Bayesian hierarichical version of meta-d$^\prime$/d$^\prime$ [@Fleming2017a], but notably the model specification used for Hmetad is not the same as in meta-d$^\prime$/d$^\prime$ [@rausch_measures_2023].

An important limitation of the models implemented in `statConfR` is that the dynamics of the decision process are not taken into account. This is a problem because confidence judgments are related to the dynamics of decision making [@hellmann_confidence_2024; @Pleskac2010; @Rahnev2020]. However, most previously proposed dynamical models of confidence do not include a parameter to represent metacognitive ability. There is one proposal for a dynamical measure of metacognitive efficiency, the v-ratio [@desender_dynamic_2022], which is based on two-stage signal detection theory [@Pleskac2010], but two-stage signal detection theory has been outperformed by other models in a number of visual discrimination tasks [@hellmann_simultaneous_2023; @hellmann_confidence_2024; @shekhar_how_2023]. Thus, the static confidence models included in `statConfR` may still be useful for many researchers.

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