Extending debugging section

BayesianCognitiveModeling/bayesian_cognitive_modeling.Rmd

@@ -1636,3 +1636,50 @@ As always in everyday life when working with code, it is likely that you will ru
 While there is an unpredictable number of errors that JAGS can throw out, here, we treat the below list as a work-in-progress documentation of common errors that we have encountered, and what might be potential ways to solve them.
 
 We will continue expanding this section.
+
+### I.) Node inconsistent with parents
+
+Sometimes you might get errors of the following format:
+
+```
+Error in checkForRemoteErrors(val) : 
+  8 nodes produced errors; first error: Error in node choices[31,22]
+Node inconsistent with parents
+```
+
+These are very common in JAGS and it is often not immediately clear what the cause is. It is recommendable to take a very close look at the underlying data at the specific datapoint.
+In this example, it would mean to check what the outcome and probability information of problem 31 for person 22 is, that the model has to work with, and what the choice was that the individual made. Here, the individual chose option B (choice = 0):
+
+```
+> choices[31,22]
+07-JE-SU-MU 
+          0 
+```
+
+... even though the choice problem had a clear dominant option of a sure win in option A compared to a sure high loss in option B:
+
+```
+> prospectsA[31,,22]
+[1]  150    1    0    0 -730    0
+> prospectsB[31,,22]
+[1]   810     0     0     0 -1000     1
+```
+
+Consequently, the model would predict a choice of A, whereas the empirical data (the participant) chose otherwise. 
+Safe options like this (where one probability = 1) can appear frequently in experience-based choice data, when people do not sample a lot.
+
+Here, the JAGS error might be caused by a computational problem, when the model predicts a choice probability of 0 which causes the model to crash. 
+We can circumvent the problem in this case by constraining the predicted choice probability to a range between 0.00001 and 0.99999, for example. 
+I.e., in the model code, we substitute 
+
+```
+choices[i,j] ~ dbern(binval[i,j])
+```
+with 
+```
+choices[i,j] ~ dbern(min(max(binval[i,j],0.00001),0.99999))
+```
+
+
+
+

BayesianCognitiveModeling/bayesian_cognitive_modeling.md

@@ -2,7 +2,7 @@ Bayesian Cognitive Modeling Tutorial
 ================
 Linus Hof & Nuno Busch
 
-Last updated: 2025-07-25
+Last updated: 2025-07-28
 
 ## Part I: Risky Choice and its Models
 
@@ -2416,3 +2416,48 @@ as a work-in-progress documentation of common errors that we have
 encountered, and what might be potential ways to solve them.
 
 We will continue expanding this section.
+
+### I). Node inconsistent with parents
+
+Sometimes you might get errors of the following format:
+
+    Error in checkForRemoteErrors(val) : 
+      8 nodes produced errors; first error: Error in node choices[31,22]
+    Node inconsistent with parents
+
+These are very common in JAGS and it is often not immediately clear what
+the cause is. It is recommendable to take a very close look at the
+underlying data at the specific datapoint. In this example, it would
+mean to check what the outcome and probability information of problem 31
+for person 22 is, that the model has to work with, and what the choice
+was that the individual made. Here, the individual chose option B
+(choice = 0):
+
+    > choices[31,22]
+    07-JE-SU-MU 
+              0 
+
+… even though the choice problem had a clear dominant option of a sure
+win in option A compared to a sure high loss in option B:
+
+    > prospectsA[31,,22]
+    [1]  150    1    0    0 -730    0
+    > prospectsB[31,,22]
+    [1]   810     0     0     0 -1000     1
+
+Consequently, the model would predict a choice of A, whereas the
+empirical data (the participant) chose otherwise. Safe options like this
+(where one probability = 1) can appear frequently in experience-based
+choice data, when people do not sample a lot.
+
+Here, the JAGS error might be caused by a computational problem, when
+the model predicts a choice probability of 0 which causes the model to
+crash. We can circumvent the problem in this case by constraining the
+predicted choice probability to a range between 0.00001 and 0.99999, for
+example. I.e., in the model code, we substitute
+
+    choices[i,j] ~ dbern(binval[i,j])
+
+with
+
+    choices[i,j] ~ dbern(min(max(binval[i,j],0.00001),0.99999))

BayesianCognitiveModeling/helper_functions/CPT_plotting.R

@@ -7,23 +7,58 @@ color = rgb(1/255, 100/255, 200/255, alpha = 1)
 ####### value function TK92 ##########
 ######################################
 vf_TK92 <- function(samples = samples, # JAGS output object name
-                       color = rgb(1/255, 100/255, 200/255, alpha = 1), # color for lines
+                    color = rgb(1/255, 100/255, 200/255, alpha = 1), # color for lines
+                    fontsize = 1, # controls font size of axis and tick labels
+                    condition = "", # condition for title of plot
                        alpha_subj = "alpha", # individual parameter names
                        lambda_subj = "lambda",
                        beta_subj = "alpha", # by default, the function will assume no separate curvature parameter for the loss domain. if there is one, that can be specified here
                        alpha_mean = "mu.alpha", # parameter names of group_level means
                        lambda_mean = "mu.lambda",
                        beta_mean = "mu.alpha" # by default, the function will assume no separate curvature parameter for the loss domain. if there is one, that can be specified here
 ) {
+
+
   par(mfrow=c(1,1))
   a <- seq(-100, 100, by=0.1)
-  plot(a, a, "l", axes=FALSE, xlab='', ylab='', cex.axis=.7, lty=2, lwd=1, ylim=c(-10, 10), xlim=c(-20, 20), col="white")
-  par(xpd=FALSE)
-  title(paste("Value function"), cex.main=1.5, font.main=1)
-  axis(1, seq(from=-20, to=20, by=5), pos=0, cex.axis=.6, mgp=c(3, .1, 1), tck=-.01)
-  axis(2, seq(from=-10, to=10, by=2), pos=0, cex.axis=.6, tck=-.01, las=1, mgp=c(3, 0.6, 0))
-  mtext(side=1, text="Outcome", line=1)
-  mtext(side=2, text="Subjective Value", line=.5)
+
+  # scale gaps to font size
+  lab_gap   <- 0.6 * fontsize   # distance of tick labels from axis line
+  title_gap <- 1.2 + 0.4*(fontsize - 1)  # distance of axis titles (mtext) from plot
+
+  plot(a, a, type = "l", axes = FALSE, xlab = "", ylab = "",
+       cex.axis = fontsize * 1.1, lty = 2, lwd = 1,
+       ylim = c(-10, 10), xlim = c(-20, 20), col = "white")
+  par(xpd = FALSE)
+  title(paste0("Value function", condition), cex.main = fontsize * 1.5, font.main = 1)
+
+  axis(1, seq(-20, 20, by = 5), pos = 0,
+       cex.axis = 0.9* fontsize, tck = -0.01, mgp = c(3, lab_gap, 0))
+  axis(2, seq(-10, 10, by = 2), pos = 0,
+       cex.axis = 0.9*fontsize, tck = -0.01, las = 1, mgp = c(3, lab_gap, 0))
+
+  mtext(text = "Outcome",          side = 1, line = title_gap, cex = fontsize)
+  mtext(text = "Subjective Value", side = 2, line = title_gap, cex = fontsize)
+
+
+
+  # plot(a, a, "l", axes=FALSE, xlab='', ylab='', 
+  #      cex.axis=fontsize*1.1, # .6, 
+  #      lty=2, lwd=1, ylim=c(-10, 10), xlim=c(-20, 20), col="white")
+  # par(xpd=FALSE)
+  # title(paste("Value function"), cex.main=1.5, font.main=1)
+  # axis(1, seq(from=-20, to=20, by=5), pos=0, 
+  #      cex.axis= fontsize, # .6, 
+  #      mgp=c(3, .1, 1), tck=-.01)
+  # axis(2, seq(from=-10, to=10, by=2), pos=0, 
+  #      cex.axis=fontsize, # .6, 
+  #      tck=-.01, las=1, mgp=c(3, 0.6, 0))
+  # mtext(side=1, text="Outcome", 
+  #       cex = fontsize,
+  #       line=1)
+  # mtext(side=2, text="Subjective Value", 
+  #       cex = fontsize,
+  #       line=.5)
 
   # plot dashed line
   lines(a,a,col="black",lty=2,lwd=1)
@@ -66,7 +101,7 @@ vf_TK92 <- function(samples = samples, # JAGS output object name
 
       legend(1, -2, inset=0, 
              legend = c(expression("Group-level estimate"), expression("Individual estimates")),
-             cex = 1.2, 
+             cex = 0.9 * fontsize, 
              col = c(color, color_subj), horiz = F,bty = "n",
              lty = 1,  # Solid line
              lwd = 2   # Line width