Update paper.bib

paper.bib

@@ -2,157 +2,157 @@
 @article{Rausch2017,
 	title = {Should metacognition be measured by logistic regression?},
 	volume = {49},
-	copyright = {All rights reserved},
+	rights = {All rights reserved},
 	issn = {10902376},
 	doi = {10.1016/j.concog.2017.02.007},
 	abstract = {© 2017 Elsevier Inc. Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria.},
-	journal = {Consciousness and Cognition},
+	pages = {291--312},
+	journaltitle = {Consciousness and Cognition},
 	author = {Rausch, Manuel and Zehetleitner, Michael},
-	year = {2017},
+	date = {2017},
 	keywords = {Cognitive modeling, Generalized linear regression, Logistic regression, Metacognition, Metacognitive sensitivity, Signal detection theory, Type 2 signal detection theory},
-	pages = {291--312},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\S5EC6DSC\\Rausch (2017) Should metacognition be measured by logistic regression.pdf:application/pdf},
 }
 
 @book{Green1966,
-	address = {New York},
+	location = {New York},
 	title = {Signal detection theory and psychophysics},
 	publisher = {Wiley},
 	author = {Green, D. M. and Swets, J. A.},
-	year = {1966},
+	date = {1966},
 }
 
 @article{Rausch2018,
 	title = {Confidence in masked orientation judgments is informed by both evidence and visibility},
 	volume = {80},
-	copyright = {All rights reserved},
+	rights = {All rights reserved},
 	issn = {1943393X},
 	doi = {10.3758/s13414-017-1431-5},
-	abstract = {How do human observers determine their degree of belief that they are correct in a decision about a visual stimulus—that is, their confidence? According to prominent theories of confidence, the quality of stimulation should be positively related to confidence in correct decisions, and negatively to confidence in incorrect decisions. However, in a backward-masked orientation task with a varying stimulus onset asynchrony (SOA), we observed that confidence in incorrect decisions also increased with stimulus quality. Model fitting to our decision and confidence data revealed that the best explanation for the present data was the new weighted evidence-and-visibility model, according to which confidence is determined by evidence about the orientation as well as by the general visibility of the stimulus. Signal detection models, postdecisional accumulation models, two-channel models, and decision-time-based models were all unable to explain the pattern of confidence as a function of SOA and decision correctness. We suggest that the metacognitive system combines several cues related to the correctness of a decision about a visual stimulus in order to calculate decision confidence.},
+	abstract = {How do human observers determine their degree of belief that they are correct in a decision about a visual stimulus—that is, their confidence? According to prominent theories of confidence, the quality of stimulation should be positively related to confidence in correct decisions, and negatively to confidence in incorrect decisions. However, in a backward-masked orientation task with a varying stimulus onset asynchrony ({SOA}), we observed that confidence in incorrect decisions also increased with stimulus quality. Model fitting to our decision and confidence data revealed that the best explanation for the present data was the new weighted evidence-and-visibility model, according to which confidence is determined by evidence about the orientation as well as by the general visibility of the stimulus. Signal detection models, postdecisional accumulation models, two-channel models, and decision-time-based models were all unable to explain the pattern of confidence as a function of {SOA} and decision correctness. We suggest that the metacognitive system combines several cues related to the correctness of a decision about a visual stimulus in order to calculate decision confidence.},
+	pages = {134--154},
 	number = {1},
-	journal = {Attention, Perception, and Psychophysics},
+	journaltitle = {Attention, Perception, and Psychophysics},
 	author = {Rausch, Manuel and Hellmann, Sebastian and Zehetleitner, Michael},
-	year = {2018},
+	date = {2018},
 	note = {Publisher: Attention, Perception, \& Psychophysics},
 	keywords = {Cognitive modeling, Confidence, Masking, Math modeling, Metacognition, Perceptual decision making, Signal detection theory, Visual perception},
-	pages = {134--154},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\3H5ZZCLZ\\Rausch (2018) Confidence is informed by both evidence and visibility.pdf:application/pdf},
 }
 
 @article{Maniscalco2012,
 	title = {A signal detection theoretic method for estimating metacognitive sensitivity from confidence ratings},
 	volume = {21},
+	pages = {422--430},
 	number = {1},
-	journal = {Consciousness and Cognition},
+	journaltitle = {Consciousness and Cognition},
 	author = {Maniscalco, Brian and Lau, Hakwan},
-	year = {2012},
-	pages = {422--430},
+	date = {2012},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\GY4SAJRN\\Maniscalco (2012) estimating metacognitive sensitivity from confidence ratings with supplementary material.pdf:application/pdf},
 }
 
 @article{rausch_modelling_2021,
 	title = {Modelling visibility judgments using models of decision confidence},
 	volume = {83},
-	copyright = {All rights reserved},
+	rights = {All rights reserved},
 	doi = {10.3758/s13414-021-02284-3},
-	journal = {Attention, Perception \& Psychophysics},
+	pages = {3311--3336},
+	journaltitle = {Attention, Perception \& Psychophysics},
 	author = {Rausch, Manuel and Hellmann, Sebastian and Zehetleitner, Michael},
-	year = {2021},
+	date = {2021},
 	note = {Publisher: Attention, Perception, \& Psychophysics},
 	keywords = {cognitive modeling, consciousness, metacognition, visibility, visual awareness},
-	pages = {3311--3336},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\NWVZAFA9\\Rausch (2021) Modelling visibility judgments.pdf:application/pdf},
 }
 
 @article{Fleming2017a,
-	title = {{HMeta}-d: hierarchical {Bayesian} estimation of metacognitive efficiency from confidence ratings},
+	title = {{HMeta}-d: hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings},
 	volume = {1},
 	doi = {10.1093/nc/nix007},
-	journal = {Neuroscience of Consciousness},
+	pages = {1--14},
+	journaltitle = {Neuroscience of Consciousness},
 	author = {Fleming, Stephen M},
-	year = {2017},
+	date = {2017},
 	keywords = {bayes, confidence, metacognition, signal detection theory},
-	pages = {1--14},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\3HKZC26B\\Fleming (2017) HMeta-d.pdf:application/pdf},
 }
 
 @article{Shekhar2020a,
-	title = {The {Nature} of {Metacognitive} {Inefficiency} in {Perceptual} {Decision} {Making}},
+	title = {The Nature of Metacognitive Inefficiency in Perceptual Decision Making},
 	volume = {128},
 	issn = {19391471},
 	doi = {10.1037/rev0000249},
-	abstract = {Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across 5 different data sets and 4 different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency.},
+	abstract = {Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across 5 different data sets and 4 different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear {zROC} curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency.},
+	pages = {45--70},
 	number = {1},
-	journal = {Psychological Review},
+	journaltitle = {Psychological Review},
 	author = {Shekhar, Medha and Rahnev, Dobromir},
-	year = {2021},
+	date = {2021},
 	pmid = {32673034},
 	keywords = {Computational model, Confidence, Metacognition, Metacognitive noise, Perceptual decision making},
-	pages = {45--70},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\I2Y99AIH\\Shekhar (2020) The nature of metacognitive inefficiency.pdf:application/pdf},
 }
 
 @incollection{Maniscalco2014,
-	address = {Berlin Heidelberg},
-	title = {Signal {Detection} {Theory} {Analysis} of {Type} 1 and {Type} 2 {Data}: {Meta}-d', {Response}- {Specific} {Meta}-d', and the {Unequal} {Variance} {SDT} {Model}},
-	booktitle = {The {Cognitive} {Neuroscience} of {Metacognition}},
+	location = {Berlin Heidelberg},
+	title = {Signal Detection Theory Analysis of Type 1 and Type 2 Data: Meta-d', Response- Specific Meta-d', and the Unequal Variance {SDT} Model},
+	pages = {25--66},
+	booktitle = {The Cognitive Neuroscience of Metacognition},
 	publisher = {Springer},
 	author = {Maniscalco, Brian and Lau, Hakwan C.},
 	editor = {Fleming, Stephen M. and Frith, C D},
-	year = {2014},
+	date = {2014},
 	doi = {10.1007/978-3-642-45190-4_3},
-	pages = {25--66},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\XJU2VSSC\\Maniscalco (2014) Signal detection theory analysis of type 1 and type 2 data.pdf:application/pdf},
 }
 
 @article{Maniscalco2016,
 	title = {The signal processing architecture underlying subjective reports of sensory awareness},
 	volume = {1},
 	doi = {10.1093/nc/niw002},
-	journal = {Neuroscience of Consciousness},
+	pages = {1--17},
+	journaltitle = {Neuroscience of Consciousness},
 	author = {Maniscalco, Brian and Lau, Hakwan},
-	year = {2016},
+	date = {2016},
 	keywords = {awareness, consciousness, contents of consciousness, perception, psychophysics, theories and models},
-	pages = {1--17},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\FUXY3PI9\\Maniscalco (2016) The signal processing architecture underlying subjective reports of sensory awareness.pdf:application/pdf},
 }
 
 @article{Rausch2020,
 	title = {Cognitive modelling reveals distinct electrophysiological markers of decision confidence and error monitoring},
 	volume = {218},
-	copyright = {All rights reserved},
+	rights = {All rights reserved},
 	doi = {10.1016/j.neuroimage.2020.116963},
+	pages = {1--14},
 	number = {116963},
-	journal = {NeuroImage},
+	journaltitle = {{NeuroImage}},
 	author = {Rausch, Manuel and Zehetleitner, Michael and Steinhauser, Marco and Maier, Martin E.},
-	year = {2020},
-	pages = {1--14},
+	date = {2020},
 	file = {PDF:C\:\\Users\\mru\\Zotero\\storage\\CVK3TM6G\\Rausch (2020) Distinct EEG correlates of confidence and error monitoring.pdf:application/pdf},
 }
 
 @article{shekhar_how_2023,
-	title = {How {Do} {Humans} {Give} {Confidence}? {A} {Comprehensive} {Comparison} of {Process} {Models} of {Perceptual} {Metacognition}},
+	title = {How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition},
 	doi = {10.1037/xge0001524},
 	abstract = {Several process models have attempted to describe the computations that underlie metacognition in humans. However, due to lack of systematic, widespread comparisons between these models, there is no consensus on what mechanisms best characterize the process of confidence generation. In this study, we tested 14 popular models of metacognition on three large data sets from basic perceptual tasks, using multiple quantitative as well as qualitative metrics. Our results highlight two mechanisms as the most plausible, generalizable features of confidence—the selective corruption of confidence by signal-dependent metacognitive noise and a heuristic strategy that uses stimulus visibility to estimate confidence. Analyzing the qualitative patterns of confidence generated by the models provides additional insights into each model’s success or failure. Our results also help to establish a comprehensive framework for model comparisons that can guide future efforts.},
-	language = {en},
-	journal = {Journal of Experimental Psychology: General},
+	journaltitle = {Journal of Experimental Psychology: General},
 	author = {Shekhar, Medha and Rahnev, Dobromir},
-	year = {2023},
+	date = {2023},
+	langid = {english},
 	file = {Shekhar und Rahnev - How Do Humans Give Confidence A Comprehensive Comp.pdf:C\:\\Users\\mru\\Zotero\\storage\\549ZQDCI\\Shekhar und Rahnev - How Do Humans Give Confidence A Comprehensive Comp.pdf:application/pdf},
 }
 
 @article{rausch_measures_2023,
 	title = {Measures of metacognitive efficiency across cognitive models of decision confidence.},
-	copyright = {All rights reserved},
+	rights = {All rights reserved},
 	issn = {1939-1463, 1082-989X},
 	url = {https://doi.apa.org/doi/10.1037/met0000634},
 	doi = {10.1037/met0000634},
 	abstract = {Meta-d′/d′ has become the quasi-gold standard to quantify metacognitive efficiency because meta-d′/d′ was developed 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 free from assumptions about confidence models: Only when we simulated data using a generative model of confidence according to which the evidence underlying confidence judgments 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 set 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.},
-	language = {en},
-	urldate = {2024-03-25},
-	journal = {Psychological Methods},
+	journaltitle = {Psychological Methods},
+	shortjournal = {Psychological Methods},
 	author = {Rausch, Manuel and Hellmann, Sebastian and Zehetleitner, Michael},
-	month = dec,
-	year = {2023},
+	urldate = {2024-03-25},
+	date = {2023-12-14},
+	langid = {english},
 	file = {Rausch et al. - 2023 - Measures of metacognitive efficiency across cognit.pdf:C\:\\Users\\mru\\Zotero\\storage\\JZ2J69PU\\Rausch et al. - 2023 - Measures of metacognitive efficiency across cognit.pdf:application/pdf},
 }