Set size and crowding affect search efficiency by limiting attention for recognition and attention against competition; however, these factors can be difficult to quantify in complex search tasks. The current experiments use a quantitative measure of the amount and variability of visual information (i.e., clutter) in highly complex stimuli (i.e., digital aeronautical charts) to examine limits of attention in visual search. Undergraduates at a large southern university searched for a target among 4, 8, or 16 distractors in charts with high, medium, or low global clutter. The target was in a high or low local-clutter region of the chart. In Experiment 1, reaction time increased as global clutter increased, particularly when the target was in a high local-clutter region. However, there was no effect of distractor set size, supporting the notion that global clutter is a better measure of attention against competition in complex visual search tasks. As a control, Experiment 2 demonstrated that increasing the number of distractors leads to a typical set size effect when there is no additional clutter (i.e., no chart). In Experiment 3, the effects of global and local clutter were minimized when the target was highly salient. When the target was nonsalient, more fixations were observed in high global clutter charts, indicating that the number of elements competing with the target for attention was also high. The results suggest design techniques that could improve pilots' search performance in aeronautical charts.

The C3 model could be used to improve the design of electronic geospatial displays by suggesting when a display will be too cluttered for its intended audience.

Clutter can slow visual search. However, experts may develop attention strategies that alleviate the effects of clutter on search performance. In the current study we examined the effects of global and local clutter on visual search performance and attention strategies. Pilots and undergraduates searched for an elevation marker in charts of high, medium, and low global clutter. The target was in a low or high local clutter region of the chart or it was absent. High global and local clutter slowed search performance for both pilots and undergraduates. Pilots were more accurate but slower. Pilots' search strategies differed from undergraduates in the following ways: they had more conservative criteria for responding target absent and spent more time processing the information within each fixation. Pilots and undergraduates used a coarseto-fine search strategy in which, as the trial progressed, fixation durations increased and saccade distance decreased.

Clutter can slow visual search. However, experts may develop attention strategies that alleviate the effects of clutter on search performance. In the current study we examined the effects of global and local clutter on visual search performance and attention strategies. Pilots and undergraduates searched for an elevation marker in charts of high, medium, and low global clutter. The target was in a low or high local clutter region of the chart or it was absent. High global and local clutter slowed search performance for both pilots and undergraduates. Pilots were more accurate but slower. Pilots' search strategies differed from undergraduates in the following ways: they had more conservative criteria for responding target absent and spent more time processing the information within each fixation. Pilots and undergraduates used a coarseto-fine search strategy in which, as the trial progressed, fixation durations increased and saccade distance decreased.

Abstract-Intelligent vehicles offer hope for a world in which crashes are rare, congestion is reduced, carbon emissions are decreased, and mobility is extended to a wider population. As long as humans are in the loop, over a half century of research in human factors suggests that this hope is unlikely to become a reality unless careful attention is paid to human behavior and, conversely, the potential for harm is real if little attention is given to said behavior. Different challenges lie with each of the two middle levels of automation which are the primary focus of this article. With Level 2 automation (National Highway Traffic Safety Administration; NHTSA), the driver is removed from always having to control the position and speed of the vehicle, but is still required to monitor both position and speed. Humans are notoriously bad at vigilance tasks, and can quickly lose situation awareness. Moreover, even if vigilant, the driver needs to interact with the vehicle. But voice-activated systems which let the driver continue to glance at the forward roadway are proving to be a potential source of cognitive distraction. With Level 3 automation (NHTSA), the driver is out of the loop most of the time, but will still need to interact with the vehicle. Critical skills can be lost over time. Unexpected transfers of control need to be considered. The surface transportation and aviation human factors communities have proposed ways to solve the problems that will inevitably arise, either through careful experimentation or extensions of existing research.