dLIM_grid: Generate Grid for PDF of the Leaky Integration Model

View source: R/LIM.R

dLIM_gridR Documentation

Generate Grid for PDF of the Leaky Integration Model

Description

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

Usage

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

Arguments

rt_max

maximal response time <- max(rt)

phi

parameter vector in the following order:

  1. 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.

  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 = b_l + w*(b_u - b_l).

  3. Stimulus strength (\mu). Strength of the stimulus.

  4. Log10-leakage (log_{10}(L)). Rate of leaky integration.

  5. Noise scale (\sigma). Model scaling parameter.

  6. 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.

  7. 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).

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

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

x_res

spatial/evidence resolution

t_res

time resolution

Value

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

Author(s)

Raphael Hartmann & Matthew Murrow

References

Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432-459.

Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550-592.

Wang, J.-S., & Donkin, C. (2024). The neural implausibility of the diffusion decision model doesn’t matter for cognitive psychometrics, but the Ornstein-Uhlenbeck model is better. Psychonomic Bulletin & Review.

Wong, K.-F., & Wang, X.-J. (2006). A Recurrent Network Mechanism of Time Integration in Perceptual Decisions. The Journal of Neuroscience, 26(4), 1314-1328.


ream documentation built on Oct. 7, 2024, 1:20 a.m.