use (uninformative) beta priors

JAGS/cpt_trial_level.txt

 model
 {
 
-  # specify priors for CPT parameters
+  # we use uninformative priors for CPT parameters to facilitate comparison of different generative mechanisms
 
-  ## standard-normal priors on probit-transformed CPT parameters correspond to uniform priors on the original scale (cf. Rouder & Lu, 2005)
+  alpha ~ dbeta(1,1)
+	gamma.pre ~ dbeta(1,1)
+	delta.pre ~ dbeta(1,1)
+	rho.pre ~ dbeta(1,1)
 
-	alpha.phi ~ dnorm(0,1)
-	gamma.phi ~ dnorm(0,1)
-	delta.phi ~ dnorm(0,1)
-	rho.lmu ~ dunif(-2.3, 1.61)
+	## extend scales to a reasonable range (cf. Scheibehenne & Pachur, 2015)
 
-	## to extend the range of the original scale [0,1], use linear link function (cf. Scheibehenne & Pachur, 2015)
-
-	alpha <- 2*phi(alpha.phi)
-	gamma <- 2*phi(gamma.phi)
-	delta <- 5*phi(delta.phi)
-  rho   <- exp(rho.lmu)
+	gamma <- 2 * (gamma.pre)
+	delta <- 10 * (delta.pre)
+  rho   <- 5 * (rho.pre)
 
   # define CPT model
 
@@ -24,16 +21,16 @@ model
 
   # value function
 
-	v.a.o1[i] <- pow(a_o1[i],alpha)
-	v.a.o2[i] <- pow(a_o2[i],alpha)
-	v.b.o1[i] <- pow(b_o1[i],alpha)
-	v.b.o2[i] <- pow(b_o2[i],alpha)
+	v.a.o1[i] <- pow(a_o1[i], alpha)
+	v.a.o2[i] <- pow(a_o2[i], alpha)
+	v.b.o1[i] <- pow(b_o1[i], alpha)
+	v.b.o2[i] <- pow(b_o2[i], alpha)
 
 	# weighting function (cf. Prelec, 1998)
 
-	w.a.p2[i] <- (delta * (pow(a_p2_exp[i],gamma))) / (delta * (pow(a_p2_exp[i],gamma)) + pow(a_p1_exp[i],gamma))
+	w.a.p2[i] <- (delta * (pow(a_p2_exp[i], gamma))) / (delta * (pow(a_p2_exp[i], gamma)) + pow(a_p1_exp[i], gamma))
 	w.a.p1[i] <- 1-w.a.p2[i]
-	w.b.p1[i] <- (delta * (pow(b_p1[i],gamma))) / (delta * (pow(b_p1[i],gamma)) + pow(b_p2[i],gamma))
+	w.b.p1[i] <- (delta * (pow(b_p1[i], gamma))) / (delta * (pow(b_p1[i],gamma)) + pow(b_p2[i], gamma))
 	w.b.p2[i] <- 1-w.b.p1[i]
 
 	Vf.a[i]  <- w.a.p1[i] * v.a.o1[i] + w.a.p2[i] * v.a.o2[i]