Anmerkungen Sebastian

paper.bib

@@ -10,7 +10,7 @@ @article{Rausch2017
 	journaltitle = {Consciousness and Cognition},
 	author = {Rausch, Manuel and Zehetleitner, Michael},
 	date = {2017},
-	keywords = {Cognitive modeling, Generalized linear regression, Logistic regression, Metacognition, Metacognitive sensitivity, Signal detection theory, Type 2 signal detection theory},
+	keywords = {Metacognition, Signal detection theory, Logistic regression, Type 2 signal detection theory, Cognitive modeling, Generalized linear regression, Metacognitive sensitivity},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\S5EC6DSC\\Rausch (2017) Should metacognition be measured by logistic regression.pdf:application/pdf},
 }
 
@@ -54,10 +54,25 @@ @article{Rausch2018
 	author = {Rausch, Manuel and Hellmann, Sebastian and Zehetleitner, Michael},
 	date = {2018},
 	note = {Publisher: Attention, Perception, \& Psychophysics},
-	keywords = {Cognitive modeling, Confidence, Masking, Math modeling, Metacognition, Perceptual decision making, Signal detection theory, Visual perception},
+	keywords = {Confidence, Metacognition, Signal detection theory, Masking, Visual perception, Cognitive modeling, Perceptual decision making, Math modeling},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\3H5ZZCLZ\\Rausch (2018) Confidence is informed by both evidence and visibility.pdf:application/pdf},
 }
 
+@article{Rahnev2020,
+	title = {The Confidence Database},
+	volume = {4},
+	rights = {All rights reserved},
+	issn = {2397-3374},
+	url = {http://www.nature.com/articles/s41562-019-0813-1},
+	doi = {10.1038/s41562-019-0813-1},
+	abstract = {Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects. This Resource introduces a new public database that enables researchers to re-analyse a large corpus of studies into meta-cognitive confidence judgements.},
+	pages = {317--325},
+	journaltitle = {Nature Human Behaviour},
+	author = {Rahnev, Dobromir and Desender, Kobe and Lee, Alan L. F. and Adler, William T. and Aguilar-Lleyda, David and Akdoğan, Başak and Arbuzova, Polina and Atlas, Lauren Y. and Balcı, Fuat and Bang, Ji Won and Bègue, Indrit and Birney, Damian P. and Brady, Timothy F. and Calder-Travis, Joshua and Chetverikov, Andrey and Clark, Torin K. and Davranche, Karen and Denison, Rachel N. and Dildine, Troy C. and Double, Kit S. and Duyan, Yalçın A. and Faivre, Nathan and Fallow, Kaitlyn and Filevich, Elisa and Gajdos, Thibault and Gallagher, Regan M. and de Gardelle, Vincent and Gherman, Sabina and Haddara, Nadia and Hainguerlot, Marine and Hsu, Tzu-Yu and Hu, Xiao and Iturrate, Iñaki and Jaquiery, Matt and Kantner, Justin and Koculak, Marcin and Konishi, Mahiko and Koß, Christina and Kvam, Peter D. and Kwok, Sze Chai and Lebreton, Maël and Lempert, Karolina M. and Ming Lo, Chien and Luo, Liang and Maniscalco, Brian and Martin, Antonio and Massoni, Sébastien and Matthews, Julian and Mazancieux, Audrey and Merfeld, Daniel M. and O’Hora, Denis and Palser, Eleanor R. and Paulewicz, Borysław and Pereira, Michael and Peters, Caroline and Philiastides, Marios G. and Pfuhl, Gerit and Prieto, Fernanda and Rausch, Manuel and Recht, Samuel and Reyes, Gabriel and Rouault, Marion and Sackur, Jérôme and Sadeghi, Saeedeh and Samaha, Jason and Seow, Tricia X. F. and Shekhar, Medha and Sherman, Maxine T. and Siedlecka, Marta and Skóra, Zuzanna and Song, Chen and Soto, David and Sun, Sai and van Boxtel, Jeroen J. A. and Wang, Shuo and Weidemann, Christoph T. and Weindel, Gabriel and Wierzchoń, Michał and Xu, Xinming and Ye, Qun and Yeon, Jiwon and Zou, Futing and Zylberberg, Ariel},
+	date = {2020},
+	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\AMX7QXU7\\Rahnev (2020) The confidence database.pdf:application/pdf},
+}
+
 @article{Maniscalco2012,
 	title = {A signal detection theoretic method for estimating metacognitive sensitivity from confidence ratings},
 	volume = {21},
@@ -79,7 +94,7 @@ @article{rausch_modelling_2021
 	author = {Rausch, Manuel and Hellmann, Sebastian and Zehetleitner, Michael},
 	date = {2021},
 	note = {Publisher: Attention, Perception, \& Psychophysics},
-	keywords = {cognitive modeling, consciousness, metacognition, visibility, visual awareness},
+	keywords = {metacognition, consciousness, visibility, visual awareness, cognitive modeling},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\NWVZAFA9\\Rausch (2021) Modelling visibility judgments.pdf:application/pdf},
 }
 
@@ -92,7 +107,7 @@ @article{Pleskac2010
 	journaltitle = {Psychological Review},
 	author = {Pleskac, Timothy J and Busemeyer, Jerome R},
 	date = {2010},
-	keywords = {a measure of cognitive, confidence, confidence has long been, diffusion model, for example, in, inner workings of the, mind, optimal solution, performance, psychophysics confidence was originally, subjective probability, thought to be a, time pressure, used to chart the, window},
+	keywords = {confidence, for example, mind, diffusion model, in, a measure of cognitive, confidence has long been, inner workings of the, optimal solution, performance, psychophysics confidence was originally, subjective probability, thought to be a, time pressure, used to chart the, window},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\46XSG6J7\\Pleskac (2010) two stage dynamic signal detection.pdf:application/pdf},
 }
 
@@ -120,7 +135,7 @@ @article{Shekhar2020a
 	author = {Shekhar, Medha and Rahnev, Dobromir},
 	date = {2021},
 	pmid = {32673034},
-	keywords = {Computational model, Confidence, Metacognition, Metacognitive noise, Perceptual decision making},
+	keywords = {Confidence, Metacognition, Perceptual decision making, Computational model, Metacognitive noise},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\I2Y99AIH\\Shekhar (2020) The nature of metacognitive inefficiency.pdf:application/pdf},
 }
 
@@ -158,7 +173,7 @@ @article{Maniscalco2016
 	journaltitle = {Neuroscience of Consciousness},
 	author = {Maniscalco, Brian and Lau, Hakwan},
 	date = {2016},
-	keywords = {awareness, consciousness, contents of consciousness, perception, psychophysics, theories and models},
+	keywords = {perception, consciousness, psychophysics, awareness, contents of consciousness, theories and models},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\FUXY3PI9\\Maniscalco (2016) The signal processing architecture underlying subjective reports of sensory awareness.pdf:application/pdf},
 }
 
@@ -189,7 +204,7 @@ @article{Desender2021
 	date = {2021},
 	pmid = {33256974},
 	note = {Publisher: Elsevier B.V.},
-	keywords = {Confidence, Decision making, Drift diffusion model, Metacognition},
+	keywords = {Confidence, Metacognition, Drift diffusion model, Decision making},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\7ZB7YBS8\\Desender (2021) Dynamic expressions of confidence.pdf:application/pdf},
 }
 

paper.md

@@ -33,11 +33,11 @@ We present the statConfR package for R, which allows researchers to conveniently
 
 # Statement of need
 
-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 accuracy, 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].
+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 in MATLAB, so statConfR makes these confidence models available to researchers who do not have access to MATLAB.  
+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 as MATLAB implementations, so statConfR makes these confidence models available to researchers who do not have access to MATLAB.  
 
-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]. 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. 
+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. 
 
 # Acknowledgements