CDSTP {ream} | R Documentation |
Continuous Dual-Stage Two-Phase Model of Selective Attention
Description
A continuous approximation of the Dual-Stage Two-Phase model of conflict tasks. The
Dual-Stage Two-Phase model assumes that choice in conflict tasks involves two processes:
a decision process and a target selection process. The target selection process is an
SDDM, while the decision process is an SDDM but with drift rate
v(x,t) = (1 - w(t))*(\mu_t + c*\mu_{nt}) + w(t)*\mu_2,
where w(t) = 0
before target selection and w(t) = 1
after target selection.
A full derivation of this model is in the ream publication.
Usage
dCDSTP(rt, resp, phi, x_res = "default", t_res = "default")
pCDSTP(rt, resp, phi, x_res = "default", t_res = "default")
rCDSTP(n, phi, dt = 1e-05)
Arguments
rt |
vector of response times
|
resp |
vector of responses ("upper" and "lower")
|
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) .
Relative start of the target selection process (w_{ts} ). Sets the start point
of accumulation for the target selection process as a ratio of the two decision
thresholds. Related to the absolute start z_{ts} point via equation
z_{ts} = b_{lts} + w_ts*(b_{uts} – b_{lts}) .
Target stimulus strength (\mu_t ).
Congruence parameter (c ). Set experiment congruency. In congruent condition
c = 1 , in incongruent condition c = -1 , and in neutral condition c = 0 .
Non-target stimulus strength (\mu_{nt} ).
Drift rate following target selection i.e. stage 2 (\mu_2 ).
Target selection drift rate (\mu_{ts} ).
Noise scale (\sigma ). Model scaling parameter.
Effective noise scale of continuous approximation (\sigma_{eff} ). See ream
publication for full description.
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 .
Target selection decision thresholds (b_{ts} ). Sets the location of each decision
threshold for the target selection process. The upper threshold b_{uts} is above 0
and the lower threshold b_{lts} is below 0 such that b_{uts} = -b_{lts} = b_{ts} . The
threshold separation a_{ts} = 2b_{ts} .
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
|
n |
number of samples
|
dt |
step size of time. We recommend 0.00001 (1e-5)
|
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).
Author(s)
Raphael Hartmann & Matthew Murrow
References
Hübner, R., Steinhauser, M., & Lehle, C. (2010). A dual-stage two-phase model of
selective attention. Psychological review, 117(3), 759.
Examples
# Probability density function
dCDSTP(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, 0.5, -0.5, -1.0, -0.5, 8.0, 4.0, 1.0, 2.0, 1.3, 1.3, 0.0, 0.0, 1.0))
# Cumulative distribution function
pCDSTP(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, 0.5, -0.5, -1.0, -0.5, 8.0, 4.0, 1.0, 2.0, 1.3, 1.3, 0.0, 0.0, 1.0))
# Random sampling
rCDSTP(n = 100, phi = c(0.3, 0.5, 0.5, -0.5, -1.0, -0.5, 8.0, 4.0, 1.0, 2.0, 1.3, 1.3,
0.0, 0.0, 1.0), dt = 0.001)
[Package
ream version 1.0-5
Index]