Sentence examples for stopping probability from inspiring English sources

Table 4 Stopping probability (standard deviation in brackets) Level crossing Stopping probability RX2, no train 69.8%% (.46) RX2, ITS, no train 53.4%% (.48) RX2, failing ITS, train approaching 80.0%% (.38).38

DOI: http://dx.doi.org/10.7554/eLife.08723.005 > -wrap-foot> Contextual factors, such asreward contingencies (Leotti and Wager, 2010) and cued expectations of stopping probability (Jahfari et al., 2012; Smittenaar et al., 2013; Zandbelt et al., 2013) can influence stopping performance by modulating the speed of the execution decision.

For the DRG model, the discount factor is seen as the stopping probability at each stage [17].

The stopping probability observed in the driving simulator for the different sign conditions is reported in Table 4.

The phase II sample size is designed to control early stopping probability based on the exact binomial distribution (Simon, 1989) on optimal two-stage designs and fixed to at least 28 patients per group (optimal design for the first stage).

The time of first incarceration, X, has the following (discrete) probability distribution: P (X < x ) = 1 if x = 0 (1 − p 1 ) x if 0 < x ≤ 5 (1 − p 1 ) 5 (1 − p 2 ) x − 5 if 5 < x ≤ 10 (1 − p 1 ) 5 (1 − p 2 ) 5 (1 − p 3 ) x − 10 otherwise, in which per annum incarceration rates feature as the stopping probability in a standard geometric distribution.

This ratio was selected so as to match the stopping probabilities with the proactive experiment.

The effect of splitting a node is to stop probability flow through the node, and the simplest way to implement that in the matrix representation, is to delete the corresponding row from P1 and hence ultimately from the DAG.

All models were fit to the behavioral data by minimizing a cost function equal to the sum of the squared and weighted errors between vectors of observed and simulated RT quantiles (0.1,0.3,0.5,0.7,0.9) and response probabilities, that is, stop probability curve in the reactive task and the no-go probability curve in the proactive task.

Qualitatively all models appeared to fit the stop probability curves quite well.

Fits of the three reactive models to behavioral data in the baseline condition, shown against (A ) the histogram of RTs for correct (top) and incorrect (i.e., responses made on stop trials; bottom) trials and (B ) the stop probability curve.