Update paper.md

paper.md

@@ -33,7 +33,7 @@ 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 across a wide range of research areas in the Cognitive Sciences: In perception research, confidence judgements can be used to quantify perceptual sensitivity based on receiver operating characteristics (Egan et al., 1959; Swets et al., 1961), a method that relies on the signal detection rating model (Green & Swets, 1966; Hautus et al., 2021; Wickens, 2002). In metacognition research, the most popular measure of metacognitive accuracy, the meta-d′/d′ method (Maniscalco & Lau, 2012, 2014), depends on the independent truncated Gaussian model (Rausch et al., 2023). Finally, models of confidence have become a flourishing research topic itself (e.g. Boundy-Singer et al., 2022; Desender et al., 2021; Fleming & Daw, 2017; Guggenmos, 2022; Hellmann et al., 2023b, 2023a; Pereira et al., 2021; Rausch et al., 2018, 2020; Shekhar & Rahnev, 2021, 2023). However, the number of studies comparing model fit between different models of confidence is still relatively low (Rausch et al., 2018, 2020, 2023; Shekhar & Rahnev, 2021, 2023) and there is still no consensus about the computational principles underlying confidence judgments (Rahnev et al., 2022). This is problematic because meta-d′/d′ can be biased by discrimination sensitivity, discrimination criteria, and/or confidence criteria if the generative model underlying the data is not independent truncated Gaussian model (Rausch et al., 2023). Likewise, receiver operating characteristics in rating experiments are appropriate measures of discrimination sensitivity only if the assumptions of the signal detection rating model are correct (Green & Swets, 1966; Hautus et al., 2021). 
+Cognitive models of confidence are currently used implicitly and explicitly across a wide range of research areas in the Cognitive Sciences: In perception research, confidence judgements can be used to quantify perceptual sensitivity based on receiver operating characteristics (Egan et al., 1959; Swets et al., 1961), a method that relies on the signal detection rating model [@Green:1966](Green & Swets, 1966; Hautus et al., 2021; Wickens, 2002). In metacognition research, the most popular measure of metacognitive accuracy, the meta-d′/d′ method (Maniscalco & Lau, 2012, 2014), depends on the independent truncated Gaussian model (Rausch et al., 2023). Finally, models of confidence have become a flourishing research topic itself (e.g. Boundy-Singer et al., 2022; Desender et al., 2021; Fleming & Daw, 2017; Guggenmos, 2022; Hellmann et al., 2023b, 2023a; Pereira et al., 2021; Rausch et al., 2018, 2020; Shekhar & Rahnev, 2021, 2023). However, the number of studies comparing model fit between different models of confidence is still relatively low (Rausch et al., 2018, 2020, 2023; Shekhar & Rahnev, 2021, 2023) and there is still no consensus about the computational principles underlying confidence judgments (Rahnev et al., 2022). This is problematic because meta-d′/d′ can be biased by discrimination sensitivity, discrimination criteria, and/or confidence criteria if the generative model underlying the data is not independent truncated Gaussian model (Rausch et al., 2023). Likewise, receiver operating characteristics in rating experiments are appropriate measures of discrimination sensitivity only if the assumptions of the signal detection rating model are correct (Green & Swets, 1966; Hautus et al., 2021). 
 
 # Acknowledgements