Although the use of the psychological construct of situational awareness (SA) assists researchers in creating a flight environment that is safer and more predictable, its true potential remains untapped until a valid means of predicting SA a priori becomes available. Previous work proposed a computational model of SA (CSA) that sought to fill that void. The current line of research is aimed at validating that model. The results show that the model accurately predicted SA in a piloted simulation.

Although generally introduced to guard against human error, automated devices can fundamentally change how people approach their work, which in turn can lead to new and different kinds of error. The present study explored the extent to which errors of omission (failures to respond to system irregularities or events because automated devices fail to detect or indicate them) and commission (when people follow an automated directive despite contradictory information from other more reliable sources of information because they either fail to check or discount that information) can be reduced under conditions of social accountability. Results indicated that making participants accountable for either their overall performance or their decision accuracy led to lower rates of “automation bias”. Errors of omission proved to be the result of cognitive vigilance decrements, whereas errors of commission proved to be the result of a combination of a failure to take into account information and a belief in the superior judgement of automated aids.

The Man-machine Integration Design and Analysis (MIDAS) tool combines human performance and cockpit models for evaluation through computer-based simulation. MIDAS was developed to support concept exploration and development in software, rather than hardware, thereby reducing design cycle costs. The current MIDAS study replicated a part-task experiment performed by the Israeli Air Force. The purpose of this study was to validate several of the operator models in MIDAS. Specifically under test was the computational model of situational awareness as described in Ref 1; however the entire range of cognitive models, from perception to decision making, was ultimately tested. The experiment was a simulation of an air-to-ground mission performed by the Co-Pilot Gunner (CPG) in an attack helicopter.

Computerized system monitors and decision aids are increasingly common additions to critical decision-making contexts such as intensive care units, nuclear power plants and aircraft cockpits. These aids are introduced with the ubiquitous goal of “reducing human error”. The present study compared error rates in a simulated flight task with and without a computer that monitored system states and made decision recommendations. Participants in non-automated settings out-performed their counterparts with a very but not perfectly reliable automated aid on a monitoring task. Participants with an aid made errors of omission (missed events when not explicitly prompted about them by the aid) and commission (did what an automated aid recommended, even when it contradicted their training and other 100% valid and available indicators). Possible causes and consequences of automation bias are discussed

Automated aids and decision support tools are rapidly becoming indispensable tools in high-technology cockpits and are assuming increasing control of"cognitive" flight tasks, such as calculating fuel-efficient routes, navigating, or detecting and diagnosing system malfunctions and abnormalities. This study was designed to investigate automation bias, a recently documented factor in the use of automated aids and decision support systems. The term refers to omission and commission errors resulting from the use of automated cues as a heuristic replacement for vigilant information seeking and processing. Glass-cockpit pilots flew flight scenarios involving automation events or opportunities for automation-related omission and commission errors. Although experimentally manipulated accountability demands did not significantly impact performance, post hoc analyses revealed that those pilots who reported an internalized perception of "accountability" for their performance and strategies of interaction with the automation were significantly more likely to double-check automated functioning against other cues and less likely to commit errors than those who did not share this perception. Pilots were also lilkely to erroneously "remember" the presence of expected cues when describing their decision-making processes.

Although use of the term situational awareness (SA) assists researchers in creating more fruitful environments for pilots to operate in, its true potential as a psychological construct remains untapped until a valid means of a priori predicting SA becomes available. Shively, Brickner and Silbiger (1997) proposed a computational model of SA (CSA) that seeks to do just that, and the current line of research is a series of studies aimed at validating that model. Originally developed for the Man-machine Integration Design and Analysis System (MIDAS), the CSA model is comprised of two features: situational elements and situation-sensitive higher-order nodes. Situational elements comprise what is known/perceiv ed about the environment (e.g. tank1 or waypoint3). Each is associated with a particular higher-order node, and as a group define the situation. Higher-order nodes are semantically related groups of SE's (e.g. threats or navigation) that are weighted based on their importance in the situation. One original aspect of the CSA model is the differentiation between perceived, actual and error SA, a proportion of which produces the operator's predicted SA. Initial validation studies using low-fidelity tasks supported the predictions of the model. Preliminary data analysis of a mid-fidelity, full-mission task completed in the Rotorcraft Part-Task Laboratory (RPTL) at NASA Ames also indicates support for predictions of the model.

In the user-centered approach to software design and development, end-users act as evaluators in usability tests at various points during the development life-cycle. Some usability professionals argue that these usability tests simply reflect the preferences of the participants and should not be used in place of objective performance measures. In an attempt to strengthen the user-centered approach to software usability testing, the present study examined the relationship between computer experience, user preference, and performance measures. User preference and performance data were collected in a comparative usability test of three user interface designs. A simple data retrieval task was used to test three user interfaces: (1) a command line, (2) a listbox, and (3) a 2-level menu. Users (N = 36) rated and ranked the three user interfaces according to their preferences. To measure user performance, the computer measured the time needed to complete each of the twenty database searches performed by users using each of the three interfaces. User preference and performance were found to be significantly and positively correlated. User preference and performance data were then ranked from most preferred to least preferred and from fastest to slowest average search completion time, respectively. The preference and performance rankings were compared and assigned a "match score" of 1 for agreement between rankings and O for non-agreement. Further, volunteers (N = 241) completed a survey of hardware and software use and familiarity. A factor analysis was performed on the data to establish "computer experience" factors. The factor analysis resulted in four factors of "computer experience:" (1) software familiarity, (2) general hardware familiarity, (3) technical hardware familiarity, and (4) Internet/communications familiarity. The four "computer experience" factors were tested as predictors of the dichotomous "match score" to test the hypothesis that "computer experience" influences preference-performance agreement. Logistic regression analyses of data from participants (N = 36) who compared the three user interfaces showed that a user's "computer experience" influences the probability of agreement between user preference and performance rankings. The results of the present study suggest that human factors professionals should factor in users' "computer experience" when making performance-related design decisions based solely on user preference data.