Generate a grid of response-time values and the corresponding PDF values.
For more details on the model see, for example, dWDSTP.
dWDSTP_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 (\(\mu_2\)).
Scale parameter for Weibull function (\(\lambda\)).
Shape parameter for Weibull function (\(\kappa\)).
Noise scale (\(\sigma\)). Model scaling parameter.
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), 759-784.