Comments on prior and model specification

JAGS/cpt_trial_level.txt

 model
 {
 
-  # define priors
+  # specify priors for CPT parameters
+
+  ## standard-normal priors on probit-transformed CPT parameters correspond to uniform priors on the original scale (cf. Rouder & Lu, 2005)
 
 	alpha.phi ~ dnorm(0,1)
 	gamma.phi ~ dnorm(0,1)
 	delta.phi ~ dnorm(0,1)
 	rho.lmu ~ dunif(-2.3, 1.61)
 
+	## 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)
 
-  # Trial loop
+  # define CPT model
 
   for (i in start:stop)
 	{
 
+  # 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)
 
+	# 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.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))
@@ -31,8 +39,12 @@ model
 	Vf.a[i]  <- w.a.p1[i] * v.a.o1[i] + w.a.p2[i] * v.a.o2[i]
 	Vf.b[i]  <- w.b.p1[i] * v.b.o1[i] + w.b.p2[i] * v.b.o2[i]
 
-	Vf.a.re[i] = pow(Vf.a[i], (1/alpha))
-	Vf.b.re[i] = pow(Vf.b[i], (1/alpha))
+	# rescale subjective values to alleviate possible parameter intercorrelations (cf. Krefeld-Schwalb et al., 2021)
+
+	Vf.a.re[i] <- pow(Vf.a[i], (1/alpha))
+	Vf.b.re[i] <- pow(Vf.b[i], (1/alpha))
+
+	# stochastic choice rule
 
 	binval[i] <- (1)/(1+exp((-1*rho)*(Vf.a.re[i]-Vf.b.re[i])))
 	choice[i] ~ dbern(binval[i])