Generate a grid of response-time values and the corresponding PDF values.
For more details on the model see, for example, dCDSTP.
dCDSTP_grid(rt_max = 10, phi, x_res = "default", t_res = "default")list of RTs and corresponding defective PDFs at lower and upper threshold
maximal response time <- max(rt)
parameter vector in the following order:
Non-decision time (\(t_{nd}\)). Time for non-decision processes such as stimulus encoding and response execution. Total decision time t is the sum of the decision and non-decision times.
Relative start (\(w\)). Sets the start point of accumulation as a ratio of the two decision thresholds. Related to the absolute start z point via equation \(z = b_l + w*(b_u - b_l)\).
Relative start of the target selection process (\(w_{ts}\)). Sets the start point of accumulation for the target selection process as a ratio of the two decision thresholds. Related to the absolute start \(z_{ts}\) point via equation \(z_{ts} = b_{lts} + w_ts*(b_{uts} – b_{lts})\).
Target stimulus strength (\(\mu_t\)).
Congruence parameter (\(c\)). Set experiment congruency. In congruent condition \(c = 1\), in incongruent condition \(c = -1\), and in neutral condition \(c = 0\).
Non-target stimulus strength (\(\mu_{nt}\)).
Drift rate following target selection i.e. stage 2 (\(\mu_2\)).
Target selection drift rate (\(\mu_{ts}\)).
Noise scale (\(\sigma\)). Model scaling parameter.
Effective noise scale of continuous approximation (\(\sigma_{eff}\)). See ream publication for full description.
Decision thresholds (\(b\)). Sets the location of each decision threshold. The upper threshold \(b_u\) is above 0 and the lower threshold \(b_l\) is below 0 such that \(b_u = -b_l = b\). The threshold separation \(a = 2b\).
Target selection decision thresholds (\(b_{ts}\)). Sets the location of each decision threshold for the target selection process. The upper threshold \(b_{uts}\) is above 0 and the lower threshold \(b_{lts}\) is below 0 such that \(b_{uts} = -b_{lts} = b_{ts}\). The threshold separation \(a_{ts} = 2b_{ts}\).
Contamination (\(g\)). Sets the strength of the contamination process. Contamination process is a uniform distribution \(f_c(t)\) where \(f_c(t) = 1/(g_u-g_l)\) if \(g_l <= t <= g_u\) and \(f_c(t) = 0\) if \(t < g_l\) or \(t > g_u\). It is combined with PDF \(f_i(t)\) to give the final combined distribution \(f_{i,c}(t) = g*f_c(t) + (1-g)*f_i(t)\), which is then output by the program. If \(g = 0\), it just outputs \(f_i(t)\).
Lower bound of contamination distribution (\(g_l\)). See parameter \(g\).
Upper bound of contamination distribution (\(g_u\)). See parameter \(g\).
spatial/evidence resolution
time resolution
Raphael Hartmann & Matthew Murrow
Hübner, R., Steinhauser, M., & Lehle, C. (2010). A dual-stage two-phase model of selective attention. Psychological review, 117(3), 759.