ream (version 1.0-5)

UGM: Urgency Gating Model

Description

The Urgency Gating Model (UGM) is a decision-making model which proposes that stimulus information is first low pass filtered, then used to update the decision state through a time varying gain function (Cisek et al., 2009). Though not initially formulated as an EAM, following the procedure of (Trueblood et al., 2021) it can be written as one. Doing so modifies the drift rate to $$v(x,t) = E_0*(1 + k*t) + (k/(1+k*t) - L)*x.$$

Usage

dUGM(rt, resp, phi, x_res = "default", t_res = "default")
pUGM(rt, resp, phi, x_res = "default", t_res = "default")
rUGM(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. Stimulus strength (\(E_0\)). Strength of the stimulus.

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

5. Log10-urgency (\(log_{10}(k)\)). Decision urgency. If \(k\) is small, the choice is dominated by leakage and approximates a LM. If \(k\) is large, it is an urgency dominated decision.

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

7. 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 \(a = 2b\).

8. 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 \(f_i(t)\).

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

10. 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

Cisek, P., Puskas, G. A., & El-Murr, S. (2009). Decisions in changing conditions: the urgency-gating model. Journal of Neuroscience, 29(37), 11560-11571.

Trueblood, J. S., Heathcote, A., Evans, N. J., & Holmes, W. R. (2021). Urgency, leakage, and the relative nature of information processing in decision-making.

Examples

# Probability density function
dUGM(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
     phi = c(0.3, 0.5, 1.0, 0.5, 0.5, 1.0, 1.5, 0.0, 0.0, 1.0))

# Cumulative distribution function
pUGM(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
     phi = c(0.3, 0.5, 1.0, 0.5, 0.5, 1.0, 1.5, 0.0, 0.0, 1.0))

# Random sampling
rUGM(n = 100, phi = c(0.3, 0.5, 1.0, 0.5, 0.5, 1.0, 1.5, 0.0, 0.0, 1.0))