N-SEEV is a model that predicts the noticeability of events that occur in the context of routine task-driven scanning across large scale visual environments. The model is an extension of the SEEV (salience, effort, expectancy, value) model, incorporating the influence of attentional set and allowing the possibility of a dynamic environment. The model was validated against two empirical data sets. In a study of pilot scanning across a high fidelity automated 747 cockpit, the SEEV component of the model predicted the distribution of attention with correlations of 0.85 and 0.88. In a lower fidelity study of pilot noticing of the onset of critical cockpit events (flight mode annunciators) the model predicted differences in noticing time and accuracy with correlations (across conditions) above 0.95. Other properties of the model are described.
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