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HGF Multi-armed bandit #243
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Hi Filippo, Sorry that this isn't a complete answer (and may not even be right!), but from having recently started to play with this model myself, I noted the following. From looking at the _binary_mab_config file relative to the _binary_config file, it seems like the trajectory variables have an extra dimension for 'bandit'. Further, looking in _binary_mab relative to _binary, it looks as if at certain time steps, updates are only performed on one of the bandits (I assume the chosen one). This led me to think that y here might be an indication of which bandit corresponds to the respective input at a given time, which made some sense as I don't know how else one would be able to model belief updating without including some information about which bandit was selected on which trial. I tried using the attached y and u (hgf_binary_mab.zip), and was able to fit the model, and the output looked (sort of) sensible in that
Code to produce the plot:
My confusion comes from what the input actually means in this case (i.e. is it that the bandit was rewarded?), and what we would actually be fitting with the softmax_mu3 response model for instance, especially as we don't seem to be able to simulate responses as there is no _binary_mab_namep function. Hopefully Chris (or someone with more knowledge than me!) can help! Hope this helps, and let me know if you can help with my confusion! Best, Tim |
Hi Tim, Yes, the model expects a Regarding the _namep function, I added one in this pull request (#245), together with some changes to make the Best, |
Dear Dr. Mathys,
I am trying to apply the hgf_binary_mab model to a 2-armed bandit task with independent rewards and punishments (a similar setup to Pulcu et al, 2017, eLife).
As suggested in the documentation, I am first testing the model with the Bayesian optimal response model
est = tapas_fitModel([], u, 'tapas_hgf_binary_mab_config', 'tapas_bayes_optimal_binary_config');
but the lack of responses
y
causes the model to crash intapas_hgf_binary_mab.m
at line 70 (https://github.com/translationalneuromodeling/tapas/blob/master/HGF/tapas_hgf_binary_mab.m#L70).Could you provide any help with this?
Thanks,
Filippo
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