command line.txt

./level1 sample save_warmup=true num_warmup=250 num_samples=250 data file=data.json init=inits.json output refresh=1 random seed=2741745723

data.json

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  "Ncnds": 3,
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3, 3, 3, 3, 3, 3],
  "a": 1,
  "v": [3.5, 2.5, 1.5],
  "w": 0.45,
  "t0": 0.3
}

inits.json

{
  "sv": 1.06423015012406,
  "sw": 0.0941928617656231,
  "st0": 0.171247761650011
}

level1.stan

data {
  int<lower=0> N;  // number of trials
  int<lower=1> Ncnds;  // number of conditions: 3
  array[N] real rt;  // response times (seconds)
  array[N] int<lower=1, upper=Ncnds> cnd;  // condition (1, 2, 3)
  array[N] int<lower=1, upper=2> stim;  // stimuli (1, 2)
  array[N] int<lower=1, upper=2> resp;      // responses (1, 2)
  real<lower=0> a;  // fixed: boundary separation (a>0)
  real<lower=0, upper=1> w;  // fixed: relative starting point
  array[Ncnds] real v;  // fixed: mean drift for each stimulus type
  real<lower=0> t0;  // fixed: non-decision-time (lower bound)
}

parameters {
  real<lower=0, upper=1> sw;  // variability of relative starting point
  real<lower=0> sv;  // variability of drift-rate
  real<lower=0> st0; // variability of non-decision-time
}

model {
  // priors
  sv ~ normal(1, 3)T[0,];
  st0 ~ normal(0.5, 1)T[0,];
  sw ~ normal(0.5, 1)T[0,1];
  for (i in 1:N) {
    if (stim[i] == 2) {  // stimulus "Left": v_L=v, w_L=w
      if (resp[i] == 2) {  // response "Left" -> v'=v_L=v, w'=w_L=w
        target += wiener_lpdf(rt[i] | a,  t0,w,v[cnd[i]],  sv,  sw, st0);
      } else {  // response "Right" -> switch boundary: v'=-v_L=-v, w'=1-w_L=1-w
        target += wiener_lpdf(rt[i] | a, t0, 1-w, -v[cnd[i]], sv,  sw, st0);
      }
    } else {  // stimulus "Right": v_R=-v, w_R=w
      if (resp[i] == 1) {  // response "Right" -> switch boundary v'=-v_R=v, w'=1-w_R=1-w
        target += wiener_lpdf(rt[i] | a, t0,1-w, v[cnd[i]], sv, sw, st0);
      } else {  // response "Left" -> v'=v_R=-v, w'=w_R=w
        target += wiener_lpdf(rt[i] | a, t0,w,-v[cnd[i]], sv,  sw, st0);
      }
    } 
  }
}