From 01d719b28346d361ad17954f38c01c1c254eba2f Mon Sep 17 00:00:00 2001
From: Manuel Rausch <114725315+ManuelRausch@users.noreply.github.com>
Date: Thu, 4 Apr 2024 14:01:05 +0200
Subject: [PATCH] Update paper.md

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 paper.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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 # Summary
   
-We present the statConfR package for R, which allows researchers to conveniently fit and compare nine different static models of decision confidence applicable to binary discrimination tasks: the signal detection rating model [@Green:1966], the Gaussian noise model [@Maniscalco:2016], the independent Gaussian model [@Rausch:2017], the weighted evidence and visibility model [@Rausch:2018], the lognormal noise model [@Shekhar:2021], the lognormal weighted evidence and visibility model [@Shekhar:2023], the independent truncated Gaussian model based on the original meta-d′/d′ method [@Maniscalco:2012; @Maniscalco:2014; @Rausch, 2023], and the independent truncated Gaussian model based on the Hmetad method  [@Fleming:2017; @Rausch, 2023]. In addition, the statConfR package provides functions to estimate meta-d′/d′, a widely-used measure of metacognitive efficiency based on both @Maniscalco:2012 and @Fleming:2017's model specification. 
+We present the statConfR package for R, which allows researchers to conveniently fit and compare nine different static models of decision confidence applicable to binary discrimination tasks: the signal detection rating model [@green:1966], the Gaussian noise model [@Maniscalco:2016], the independent Gaussian model [@Rausch:2017], the weighted evidence and visibility model [@Rausch:2018], the lognormal noise model [@Shekhar:2021], the lognormal weighted evidence and visibility model [@Shekhar:2023], the independent truncated Gaussian model based on the original meta-d′/d′ method [@Maniscalco:2012; @Maniscalco:2014; @Rausch, 2023], and the independent truncated Gaussian model based on the Hmetad method  [@Fleming:2017; @Rausch, 2023]. In addition, the statConfR package provides functions to estimate meta-d′/d′, a widely-used measure of metacognitive efficiency based on both @Maniscalco:2012 and @Fleming:2017's model specification. 
 
 # Statement of need