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
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