# Take The Best With Known Search Order
model {
   # Observed Decisions
   for (i in 1:nq) {
      k[i] ~ dbern(theta[i,d1[i]])
   }   
   # TTB Decision
   for (i in 1:nq){   
      # Cue/Feature contributions to decision
      # the pow function is a weight so that the first discriminating feature determines the decision.
      for (j in 1:nc){
         w[i,j] <- (c[p[i,1],j]-c[p[i,2],j])*pow(2,vo[j]-1)
      }      
      # TTB takes the first feature, or they are not different
      # First object gives d1[i]=3, Second object gives d1[i]=1, Same gives d1[i]=2. The meaning of these values is coming!
      dtmp[i] <- sum(w[i,1:nc])
      d[i] <- -1*step(-dtmp[i])+step(dtmp[i])
      d1[i] <- d[i]+2      
      # Goes with TTB decision with probability gamma, or guesses. This means if we got a 1, then the city is not chosen by this feature. 3 means it was. 2 corresponds to the .5 uniform distribution - 50/50 chance.
      theta[i,1] <- 1-gamma
      theta[i,2] <- 0.5
      theta[i,3] <- gamma    
      # Whether decision was correct.
      correct[i] <- equals(k[i],1)*equals(d1[i],3)
                    +equals(k[i],0)*equals(d1[i],1)
                    +0.5*equals(d1[i],2)
   }
   # Proportion of correct decision, with hack to insure monitoring
   sc <- sum(correct[1:nq])/nq+gamma-gamma   
   # Priors
   gamma ~ dbeta(1,1) 
}