An inhibition-based stochastic countable-time decision model

A new stochastic model to account for reaction-time fluctuation in prolonged work tasks is presented. Transition probabilities from work periods to distraction periods and vice versa are dependent on inhibition, which increases during work and decreases during distractions. The model presented here differs from all other inhibition-based models in that transitions can take place only at certain random points in time, and is referred to as a countable-time decision model. It is argued that the proposed model is a more plausible alternative to other existing inhibition-based models, while at the same time being highly flexible in that it is able to approximate other models arbitrarily well. This model is compared to an existing inhibition-based continuous-time decision model and the probability distribution functions for work and distraction periods are derived.