dRTM_grid {ream} | R Documentation |
Generate Grid for PDF of the Rational Threshold Model
Description
Generate a grid of response-time values and the corresponding PDF values.
For more details on the model see, for example, dRTM
.
Usage
dRTM_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:
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) .
Stimulus strength (\mu ). Strength of the stimulus and used to set the drift
rate. For changing threshold models v(x,t) = \mu .
Noise scale (\sigma ). Model noise scale parameter.
Initial decision threshold location (b_0 ). Sets the location of each decision
threshold at time t = 0 .
Amount of decision threshold collapse (\kappa ).
Semi-saturation constant (t_{0.5} ). The semi-saturation constant is the value of
time at which the boundaries have collapsed by half \kappa .
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 .
|
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
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008). Decision-making with multiple
alternatives. Nature Neuroscience, 11(6), 693-702.
Hanks, T. D., Mazurek, M. E., Kiani, R., Hopp, E., & Shadlen, M. N. (2011). Elapsed
Decision Time Affects the Weighting of Prior Probability in a Perceptual Decision
Task. The Journal of Neuroscience, 31(17), 6339-6352.
Voskuilen, C., Ratcliff, R., & Smith, P. L. (2016). Comparing fixed and collapsing boundary
versions of the diffusion model. Journal of Mathematical Psychology, 73, 59-79.
[Package
ream version 1.0-5
Index]