Academic journal article Human Factors

Heuristic Automation for Decluttering Tactical Displays

Academic journal article Human Factors

Heuristic Automation for Decluttering Tactical Displays

Article excerpt

INTRODUCTION

Clutter can become a serious problem for users monitoring situation displays. For example, in naval air defense, users must monitor airspaces to find threatening aircraft. These airspaces are frequently in busy environments near land and contain multiple commercial air lanes and other air traffic. Clutter increases search times by increasing the number of objects that must be sifted through or searched to find objects of interest (e.g., Treisman & Gelade, 1980). Clutter also increases the chance for "change blindness," the chronic human inability to detect changes occurring in a scene when attention is focused elsewhere (Rensink, 2002). These problems can result in reduced situation awareness and delayed response times to critical events.

A common method for reducing clutter and promoting situation awareness is to identify important objects and then mark or highlight them in some manner. Highlighting, when the identification process is reliable, allows users to focus on a subset of objects and thereby effectively reduces the number of objects that must be sifted through or monitored. For example, in a search through a matrix of words, Fisher, Coury, Tengs, and Duffy (1989) found that highlighting a subset of words improved response time, even when the highlighting was less than completely reliable. In a visual search task for symbols on a tactical map display, Van Orden, DiVita, and Shim (1993) found that highlighting a category of symbols improved response time. In an augmented reality search task, Yeh and Wickens (2001b) found that highlighting targets improved response time. However, one downside of highlighting is that because it is such an effective form of cuing, it can impede the detection of important objects that are mistakenly left unhighlighted (and hence uncued) when the automation is imperfect or the situation is uncertain (e.g., Baddeley, 1972; Posner, 1980; Yeh & Wickens, 2001b).

A related method for reducing clutter is to identify less important objects and then declutter them from the display by making them less visually salient in some manner. This method also reduces the effective search space by eliminating some objects from the search set. In several studies of visual search for targets in tactical map displays, researchers have shown that users appreciate and benefit from the decluttering of irrelevant categories of symbols (Johnson, Liao, & Granada, 2002; Nugent, 1996; Osga & Keating, 1994; Schultz, Nichols, & Curran, 1985; Yeh & Wickens, 2001a).

A number of methods have been used to declutter objects by reducing their visual salience, including size reduction, dimming, turning symbols into dots, and even complete removal. Ideally, a good declutter method should visually segregate important from less important objects but with minimal disruption to the information content of the symbols. For example, in a visual search task for target symbols on a cluttered display, St. John, Feher, and Morrison (2002) found that simply dimming irrelevant symbols to one third of their initial luminance (thereby reducing their contrast against a dark background) supported easy segregation but without removing any identifying information.

An often overlooked issue, which we address here, is how the highlighted or decluttered objects are identified in the first place. In most experimental studies, the identification function is simply assumed to exist, but it is left unspecified. In applied tactical domains such as air defense, the identification functions are typically simple classification rules, such as all friendly aircraft or all aircraft with altitudes over 25,000 feet (standard U.S. Navy practice). Although attractive because of their simplicity, these rules often fail to meet the needs of sophisticated users because they do not align with the categories of most interest to these users.

A more sophisticated approach is to define meaningful categories of objects and then use these categories as the basis for decluttering. …

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