The integrated hazard function for redundant-target trials will, therefore, equal the integrated hazard function for single-target trials without distractors, i.e., H AB (t) = H A (t).
C t) rests on the conceptualization of the hazard function for speeded responses as a gauge of moment-to-moment cognitive expenditure.
b For α=2, a=25 and γ=1 Fig. 6 Plot of the GW-LL hazard function for some parameter values.
The log-logistic distribution corresponds to α=β= 1. Plots of (6) and hazard function for selected parameter values are displayed in Figs. 5 and 6, respectively.
The fitted hazard function for the OLLLWcr model is displayed in Fig. 10 b, which we observe bimodal shapes, thus indicating a good fit.
b For α=2, μ=1 and σ=2 Fig. 4 Plot of the GW-LN hazard function for some parameter values.
The numerator of Eq. 1 is the integrated hazard function for multiple sources of information presented simultaneously and the denominator is the summation of the integrated hazard functions of performance for each single source presented in isolation.
Elapsed duration in the current episode prior to being at risk is negatively related to the hazard, consistent with the standard finding of a downward sloping hazard function for welfare exit.
For the family of PH models with covariates z= z 1,z 2,…,z p )′, the hazard function for T can be expressed as h(t;mathbf{z})=h_{0}(t boldsymbol{alpha})~ e^{mathbf{z}'boldsymbol{beta}}, (5).
The TTT plot in Fig. 1(c) suggests an increasing hazard function for T ′. Figure 4(a c) shows diagnostic plots for the generalized log-logistic, Weibull and log-logistic fits (without covariate).
The time in the trial when the target stimulus appeared followed an exponential distribution (a flat hazard function for speed change) in order to encourage the animal to keep its vigilance constant throughout each trial.
