<p>This link contains the FORTRAN source codes used for simulating the baseline logical- rule models, the EBRW model, and the free stimulus drift-rate model from:</p><p>Fific, M., Little, D.R., & Nosofsky, R.M. (accepted pending revision). Logical-rule models of classification response times: A synthesis of mental-architecture, random- walk, and decision-bound approaches. Psychological Review. serst.for = serial self-terminating model serexh.for = serial exhaustive model parst.for = parallel self-terminating model parexh.for = parallel exhaustive model coact.for = coactive model ebrw.for = EBRW model freestm.for = free stimulus drift-rate model ************************************************************* Each of the simulation subroutines receives the following inputs and produces the following outputs: intsep = an indicator variable equal to 1 for separable-dimension stimuli and equal to 0 for integral-dimension stimuli (relevant only for EBRW model). abase = parameter vector (described in next section) x = stimulus coordinates matrix [x(i,m) is the value of stimulus i on dimension m] cat = category-membership vector [cat(i) is the category assignment for stimulus i] nstim = number of stimuli [9 in the experiments from Fific et al.] nsim = number of simulations per stimulus [10000 in Fific et al.] is1 = a five digit integer-valued random seed nbin = number of RT quantiles into which the correct RTs are divided quant = quantile cutoff matrix for the correct RTs associated with each stimulus [quant(i,j) is the jth quantile cutoff for stimulus i] theory = theoretical predictions output matrix [theory(i,k) gives the predicted probability that a response for stimulus i is correct and falls in the k'th RT quantile (k=1,nbin) or that the response is incorrect (k=nbin+1)] mrt = predicted mean correct RT output vector [mrt(i) is the predicted mean correct RT for stimulus i] </p><p>(Continued) ******************************************************************* For the logical-rule models and the EBRW model, the input parameters (abase) are as follows (see Fific et al. for an explanation of the free parameters): abase(1) = px abase(2) = Dx abase(3) = Dy abase(4) = σx abase(5) = σy abase(6) = +A abase(7) = -B abase(8) = mean of normal distribution that is exponentiated abase(9) = standard deviation of normal distribution that is exponentiated</p><p>Note:</p><p>μR = exp[abase(8)+.5*abase(9)]</p><p>2 σR = {exp[abase(9)]-1}*{exp[2*abase(8)+abase(9)]} abase(10) = k</p><p>*******************************************************************</p><p>(Continued) ******************************************************************* For the free stimulus-drift-rate model, the input parameters are as follows: abase(1) = +A abase(2) = -B abase(3) = mean of normal distribution that is exponentiated abase(4) = standard deviation of normal distribution that is exponentiated abase(5) = k abase(6) through abase(14) = p(1) through p(9)</p><p>******************************************************************* *******************************************************************</p><p>Within each simulation, the following other standard subroutines are called: rand = assigns a (uniformly distributed) random value between 0 and 1 to pran zscor = provides the z value below which a given pran of the cases fall in a standardized normal distribution. pzscor = provides the proportion of cases p that fall below a given z value in a standardized normal distribution.</p><p>**********************************************************************</p>