R Community - I'm attempting to apply a softmax action selection to a probability generated by a hidden Markov model. I'm having difficulties in how to apply the softmax temperature parameter (beta). Here is my code thus far. I'm thinking the sigmoid function will work but I need this function to return the total error instead of the probability so I can't optimize it with optim(). calc.probs <- function(delta,beta) { # initial starting probabilities thList = 0; block = 0 for (i in 1:length(dat$choice)) { if (dat$RepNum[i] != block) { pL = 0.5; pR = 0.5; block = dat$RepNum[i]; } # Markov Transitions pL <- pL*(1-delta) + pR*delta pR <- 1-pL # Apply feedback pflc <- ifelse(dat$choice[i] == dat$reward[i], .8, .2) pfrc <- 1 - pflc denom <- pflc * pL + pfrc * pR # What's the new belief given observation posteriorL <- pflc * pL/denom posteriorR <- 1-posteriorL pL <- posteriorL; pR <- posteriorR # Insert softmax here pLlist[i] <- pL } return(pLlist) } -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center S355 Elliot Hall: 612-626-0072 www.psych.umn.edu/research/tricam [[alternative HTML version deleted]]