I want to fit a drift diffusion model to a task which involves multiple decisions (n=400) between two different valuable choice options . I do understand how I would do that in general, also with the help of that great tutorial (http://gru.stanford.edu/doku.php/tutorials/diffusion). What I want, is to consider input strength in the DDM process as well. I guess the main problem here is that the difference in values in our task are continuous and can be all kind of values between 1 and 100. In all examples I have found so far, DDM's (or simulations ) which considered input strength were fitted seperately for each level of strength such that the scaled input strength is considered in the update rule:
d(i,t+1) = d(i,t) + random('norm',k*C,1));
(where C is the input strength, k is a scaling parameter and d is accumulated evidence). I wondered why parameters are never fitted across all trials ( and not seperated by input strength) where the update happens on the trial type dependent input? E.G I would end up with one drift rate and bounds for the whole task rather than one drift for each possible value difference.