R Community -
I am attempting to fit a model as described in Hampton, Bossaerts, and
O'doherty (J. Neuroscience) 2006. They use a bayesian hidden markov model
to model the Reversal Learning data. I have tried using HMM and depmixS4
with no success. My data is a Reversal Learning Task in which there are 3
sets of patterns over 3 blocks. The participant receives incorrect or
correct feedback. 20% of the time they receive false feedback (they are
told incorrect when they were in fact correct). Once the person achieves
the criterion of 9/10 correct responses the contigencies reverse. I am
confused on how to set up my states, symbols, starting probabilities,
transition probabilities, and emission probabilities in R. This is what I
have so far.
hmm <- initHMM(c("stay", "switch"), c("correct", "incorrect"), c(.5, .5),
matrix(c(.9, .1, .1, .9),2), matrix(c(.2, .8, .8, .2), 2))
dat$test <- ifelse(dat$Slide1_ACC == 0, "incorrect", "correct")
viterbi(hmm, dat$test)
The sequence of observations I run through the model is the feedback the
participant receives. Any help would be greatly appreciated. I think what
I want to do is run the model on each participant to generate the most
probable path and then compare that to the actual path to see if they match
up.
Best,
--
Edward H. Patzelt
Research Assistant – TRiCAM Lab
University of Minnesota – Psychology/Psychiatry
VA Medical Center
Office: S355 Elliot Hall - Twin Cities Campus
Phone: 612-626-0072 Email: patze003@umn.edu
Please consider the environment before printing this email
www.psych.umn.edu/research/tricam
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