ream (version 1.0-5)

RDMC: Revised Diffusion Model of Conflict Tasks

Description

A DMC-like model which modifies the shape of the controlled and automatic processes to ensure consistent stimulus representation across the task. It maintains all SDDM parameters outside the drift rate which is \(v(x,t) = w_a(t)*d_a + w_c(t)*d_c\), where \(w_a(t) = A_0*exp(-k*t)\) and \(w_c(t) = 1 - w_a(t)\).

Usage

dRDMC(rt, resp, phi, x_res = "default", t_res = "default")
pRDMC(rt, resp, phi, x_res = "default", t_res = "default")
rRDMC(n, phi, dt = 1e-05)

Value

For the density a list of PDF values, log-PDF values, and the sum of the log-PDFs, for the distribution function a list of of CDF values, log-CDF values, and the sum of the log-CDFs, and for the random sampler a list of response times (rt) and response thresholds (resp).

Arguments

rt

vector of response times

resp

vector of responses ("upper" and "lower")

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. Automatic process amplitude (\(A_0\)). Max value of automatic process.

4. Attention shift parameter (\(k\)). Encodes congruency and thus differs between congruent and incongruent trials.

5. Base drift rate of the automatic channel (\(d_a\)).

6. Base drift rate of the controlled channel (\(d_c\)).

7. Noise scale (\(\sigma\)). Model noise scale parameter.

8. 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 \(a = 2b\).

9. 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 \(f_i(t)\).

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

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

x_res

spatial/evidence resolution

t_res

time resolution

n

number of samples

dt

step size of time. We recommend 0.00001 (1e-5)

Author

Raphael Hartmann & Matthew Murrow

References

Lee, P.-S., & Sewell, D. K. (2023). A revised diffusion model for conflict tasks. Psychonomic Bulletin & Review, 31(1), 1–31.

Examples

# Probability density function
dRDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
     phi = c(0.35, 0.5, 7.5, 40.0, 5.0, 5.0, 1.0, 0.5, 0.0, 0.0, 1.0))

# Cumulative distribution function
pRDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
     phi = c(0.35, 0.5, 7.5, 40.0, 5.0, 5.0, 1.0, 0.5, 0.0, 0.0, 1.0))

# Random sampling
rRDMC(n = 100, phi = c(0.35, 0.5, 7.5, 40.0, 5.0, 5.0, 1.0, 0.5, 0.0, 0.0, 1.0))