A framework for modelling and analysing variability in visual occlusion experiments

Research using self-paced visual occlusion has traditionally analysed mean occlusion times, thereby neglecting potential insights to be gained from variability across individual visual sampling decisions. This paper proposes a framework for analysing visual occlusion data based on a hierarchy of sampling strategies. The framework describes each sampling decision as being dependent on both system characteristics (mean performance) and information available during sampling (variability). To illustrate the framework, data from an on-road study were analysed. Self-paced occlusion times were shown to fit a descriptive function both for lane deviations observed at the end of previous visual samples and for predicted lane deviations at the end of occlusion intervals. The fact that the latter fit was better suggests that participants, especially the more experienced ones, were indeed able to use predictions in their sampling decisions.