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ream (version 1.0-5)

dETM_grid: Generate Grid for PDF of the Exponential Threshold Model

Description

Generate a grid of response-time values and the corresponding PDF values. For more details on the model see, for example, dETM.

Usage

dETM_grid(rt_max = 10, phi, x_res = "default", t_res = "default")

Value

list of RTs and corresponding defective PDFs at lower and upper threshold

Arguments

rt_max

maximal response time <- max(rt)

phi

parameter vector in the following order:

  1. Non-decision time (tnd). 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.

  2. 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=bl+w(bubl).

  3. Stimulus strength (μ). Strength of the stimulus and used to set the drift rate. For changing threshold models v(x,t)=μ.

  4. Noise scale (σ). Model noise scale parameter.

  5. Initial decision threshold location (b0). Sets the location of each decision threshold at time t=0.

  6. Log10-rate of threshold change (log10(τ)).

  7. Contamination (g). Sets the strength of the contamination process. Contamination process is a uniform distribution fc(t) where fc(t)=1/(gugl) if gl<=t<=gu and fc(t)=0 if t<gl or t>gu. It is combined with PDF fi(t) to give the final combined distribution fi,c(t)=gfc(t)+(1g)fi(t), which is then output by the program. If g=0, it just outputs fi(t).

  8. Lower bound of contamination distribution (gl). See parameter g.

  9. Upper bound of contamination distribution (gu). See parameter g.

x_res

spatial/evidence resolution

t_res

time resolution

Author

Raphael Hartmann & Matthew Murrow

References

Murrow, M., & Holmes, W. R. (2023). PyBEAM: A Bayesian approach to parameter inference for a wide class of binary evidence accumulation models. Behavior Research Methods, 56(3), 2636-2656.