dynConfiR (version 0.0.4)

rLCA: Simulation of confidence ratings and RTs in leaky competing accumulator model

Description

Simulates the decision responses, reaction times and state of the loosing accumulator together with a confidence measure in the leaky competing accumulator model. Optionally, there is a post-decisional accumulation period, where the processes continues.

Usage

rLCA(n, mu1, mu2, th1, th2, k = 0, beta = 0, SPV = 0, tau = 0,
  wx = 1, wrt = 0, wint = 0, t0 = 0, st0 = 0, pi = 0, sig = 1,
  time_scaled = TRUE, simult_conf = FALSE, delta = 0.01, maxrt = 15)

Value

Returns a data.frame with three columns and n rows. Column names are rt (response time), response (1 or 2, indicating which accumulator hit its boundary first), and conf (the value of the confidence measure; not discretized!).

Arguments

n

integer. number of samples.

mu1

mean momentary evidence for alternative 1

mu2

mean momentary evidence for alternative 2

th1

decision threshold for alternative 1

th2

decision threshold for alternative 2

k

leakage (default: 0)

beta

inhibition (default: 0)

SPV

variation in starting points (default: 0)

tau

fixed post decisional accumulation period (default: 0)

wx

weight on balance of evidence in confidence measure (default: 1)

wrt

weight on RT in confidence measure (default: 0)

wint

weight on interaction of evidence and RT in confidence measure (default: 0)

t0

minimal non-decision time (default: 0)

st0

range of uniform distribution of non-decision time (default: 0)

pi

factor for input dependent noise of infinitesimal variance of processes (default: 0)

sig

input independent component of infinitesimal variance of processes (default: 1)

time_scaled

logical. Whether a time_scaled transformation for the confidence measure should be used.

simult_conf

logical. Whether in the experiment confidence was reported simultaneously with the decision. If that is the case decision and confidence judgment are assumed to have happened subsequent before the response. Therefore tau is included in the response time. If the decision was reported before the confidence report, simul_conf should be FALSE.

delta

numerical. Size of steps for the discretized simulation (see details).

maxrt

numerical. Maximum reaction time to be simulated (see details). Default: 15.

Author

Sebastian Hellmann.

Details

The simulation is done by simulating discretized steps until one process reaches the boundary with an update rule: $$\delta X_i(t) = \max (0, X_i(t) + \delta_t ((k-1)X_i(t)-\beta X_{j=i} (t) + \mu_i + \varepsilon_i (t)),$$ with \(\varepsilon_i(t) \sim N(0, (\pi \mu_i)^2 + \sigma^2 )\). If no boundary is met within the maximum time, response is set to 0. After the decision, the accumulation continues for a time period (tau), until the final state is used for the computation of confidence.

Examples

# minimal arguments
simus<- rLCA(n=20, mu1=1, mu2=-0.5, th1=1, th2=0.8)
head(simus)

# specifying all relevant parameters
simus <- rLCA(n=1000, mu1 = 2.5, mu2=1, th1=1.5, th2=1.6,
               k=0.1, beta=0.1, SPV=0.2, tau=0.1,
               wx=0.8, wrt=0.2, wint=0, t0=0.2, st0=0.1,
               pi=0.2, sig=1)
if (requireNamespace("ggplot2", quietly = TRUE)) {
  require(ggplot2)
  ggplot(simus, aes(x=rt, y=conf))+
    stat_density_2d(aes(fill = after_stat(density)), geom = "raster", contour = FALSE) +
    facet_wrap(~response)
}
boxplot(conf~response, data=simus)