Correcting c riterion setting in ITGc and IG

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: statConfR
 Type: Package
 Title: Models of Decision Confidence and Metacognition
-Version: 0.1.2
+Version: 0.1.1
 Date: 2024-04-18
 Authors@R: c(
   person("Manuel", "Rausch", email="[email protected]", role = c("aut", "cre"),

R/fitConf.R

@@ -182,7 +182,7 @@
 #' @references Rausch, M., & Zehetleitner, M. (2017). Should metacognition be measured by logistic regression? Consciousness and Cognition, 49, 291–312. doi: 10.1016/j.concog.2017.02.007
 #' @references Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. doi: 10.1214/aos/1176344136
 #' @references Shekhar, M., & Rahnev, D. (2021). The Nature of Metacognitive Inefficiency in Perceptual Decision Making. Psychological Review, 128(1), 45–70. doi: 10.1037/rev0000249
-#' @references Shekhar, M., & Rahnev, D. (2023). How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition. Journal of Experimental Psychology: General. doi:10.1037/xge0001524
+#' @references Shekhar, M., & Rahnev, D. (2023). How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition. Journal of Experimental Psychology: General. doi:10.1037/xge0001524
 
 
 #' @examples

R/fitConfModels.R

@@ -196,7 +196,7 @@
 #' @references Rausch, M., & Zehetleitner, M. (2017). Should metacognition be measured by logistic regression? Consciousness and Cognition, 49, 291–312. doi: 10.1016/j.concog.2017.02.007
 #' @references Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. doi: 10.1214/aos/1176344136
 #' @references Shekhar, M., & Rahnev, D. (2021). The Nature of Metacognitive Inefficiency in Perceptual Decision Making. Psychological Review, 128(1), 45–70. doi: 10.1037/rev0000249
-#' @references Shekhar, M., & Rahnev, D. (2023). How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition. Journal of Experimental Psychology: General. doi:10.1037/xge0001524
+#' @references Shekhar, M., & Rahnev, D. (2023). How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition. Journal of Experimental Psychology: General. doi:10.1037/xge0001524
 
 #'
 #' @examples

R/fitMetaDprime.R

@@ -54,7 +54,7 @@
 #' @references Fleming, S. M. (2017). HMeta-d: Hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings. Neuroscience of Consciousness, 1, 1–14. doi: 10.1093/nc/nix007
 #' @references Maniscalco, B., & Lau, H. (2012). A signal detection theoretic method for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21(1), 422–430.
 #' @references Maniscalco, B., & Lau, H. C. (2014). Signal Detection Theory Analysis of Type 1 and Type 2 Data: Meta-d’, Response- Specific Meta-d’, and the Unequal Variance SDT Model. In S. M. Fleming & C. D. Frith (Eds.), The Cognitive Neuroscience of Metacognition (pp. 25–66). Springer. doi: 10.1007/978-3-642-45190-4_3
-#' @references Rausch, M., Hellmann, S., & Zehetleitner, M. (2023). Measures of metacognitive efficiency across cognitive models of decision confidence (Preprint). PsyArXiv. doi: 10.31234/osf.io/kdz34
+#' @references Rausch, M., Hellmann, S., & Zehetleitner, M. (2023). Measures of metacognitive efficiency across cognitive models of decision confidence. Psychological Methods. doi: 10.31234/osf.io/kdz34
 #'
 #' @examples
 #' # 1. Select two subject from the masked orientation discrimination experiment

R/int_fitITG.R

@@ -190,12 +190,14 @@ fitITGc <-
       res$c <-  as.vector(fit$par[nCond+nRatings])
       res[,paste("theta_minus.",(nRatings-1):1, sep="")] <-
         # c(as.vector(fit$par[nCond+nRatings-1] - rev(cumsum(c(exp(fit$par[(nCond+1):(nCond+nRatings-2)]))))),  as.vector(fit$par[nCond+nRatings-1]))
-        exp(fit$par[nCond + nRatings*2]) * as.vector(fit$par[nCond+nRatings]) -
+        #exp(fit$par[nCond + nRatings*2]) *
+        as.vector(fit$par[nCond+nRatings]) -
         rev( cumsum(c(exp(fit$par[(nCond+1):(nCond+nRatings-1)]))))
 
       res[,paste("theta_plus.",1:(nRatings-1), sep="")] <-
         # c(as.vector(fit$par[nCond+nRatings+1]), as.vector(fit$par[nCond+nRatings+1]) + as.vector(cumsum(c(exp(fit$par[(nCond+nRatings+2):(nCond + nRatings*2-1)])))))
-        exp(fit$par[nCond + nRatings*2]) * as.vector(fit$par[nCond+nRatings]) +
+        #exp(fit$par[nCond + nRatings*2]) *
+        as.vector(fit$par[nCond+nRatings]) +
         cumsum(c(exp(fit$par[(nCond+nRatings+1):(nCond + nRatings*2-1)])))
 
       res$m <- exp(fit$par[nCond + nRatings*2])