This study used archival data on a sample of 186,492 referrals from a southwestern state Juvenile Probation Commission to compare the characteristics of 5,439 male Black, Hispanic, and White juveniles with sexual behavior problems on the five most common sexual offenses in the data set. The characteristics of 181,053 juveniles of the three races without sexual behavior problems were also compared on the basis of the seven most common nonsexual offenses. The bases of comparison were the seven variables: reported incidence of sexual offenses, the primary caregivers or living arrangements, age, suspected sexual abuse, suspected emotional abuse, suspected physical abuse, and special education status, on which racial differences were found. Prevention and treatment implications of findings are discussed.

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A 25-item questionnaire was mailed to sex offender treatment providers from counties with 60 or more reported juvenile sex offenders in a Southwestern state to determine the most effective treatment for juvenile sex offenders. Results indicated that cognitive behavioral therapy was the most successful reported approach to treatment with an average success rate of 87%. The most commonly used approach was cognitive behavioral therapy with relapse prevention. The most common sexual offense was indecency with a child involving sexual contact, contrary to studies that found that in the Probation Commission data, aggravated sexual offense was the most common. These results have ramifications for state policies on treatment for juvenile sex offenders.

Cockpit automation has changed the roles, responstblhae$ and activities of pilots, leading to new types of errors on the flight deck. This research is focused on understanding those errors through the development of a computational cognitive model that describes how pilots interact with automated systems. The cognitive model under development is based on a cognitive task analysis supplemented with eye tracking data collected from commercial pilots flying a fbw-fidelity simulator.The Federal Aviation Administration (FAA), National Aemnautics and Space Administration (NASA), National Transportation Safety Board (NTSB), airline pilots, airline maoagemen< and researchers all have raised questions about the impact of automation in our airliners. Although some researchers have suggested that automation reduces cockpit workload (Wiener,, 1985; Sherman, Hehnreich, &Men& 1997), others have suggested that automation can increase workload and frustration (Wiener, 1988(Wiener, ,1989.Although automation was introduced in pat to reduce error in the aviation system, errors have continued. Indeed, automation has introduoal new error3 into the cockpit. This may be a result of automation changing the roles, responsibilities, and activities of the pilots (e.g., from psychomotor flying skills to monitoring and delegating tasks to the automation), which introduces new errors and new types of errors into the system.One approach to stodying error has been to classify or fonctionally group automation-related errors (e.g., Sarter and Woods, 1995;Wiener, 1989). However, this approach does not allow researchers to pinpoint the causes of errors. Further, this approach does not describe the process of pilotautomation interaction that results in the errors. This makes it impossible to know how to design interventions such as training or the redesign of instroments, displays, or software.An altemativc to the taxonomic approach is cognitive modeling. Detailed cognitive modeling of the processes involved in human-automation systems should give a more complete and systematic picture of automation errors, their detection and possible mitigation In this research project we axe developing a computational model of the cognitive processes underlying performam in an automated cockpit.We decided to build our computational model from an a priori cognitive task analysis coupled with empirical performance data. To make the task analysis tractable, we needed to focus on a pa&alar aspect of flight. Given the large nomber of errors that oar doring changes in vertical position, we chose to focus on the cliib and descent phases. A cognitive task analysis of these phases was then developed using NGOMSL (Natural Language GOMS, see Kieras, 1997). The task analysis focused on the cognitive demands on the pilot responsible for interacting with the automation during these phases of flight. Specifically, the task analysis includes relevant details of the automation interface such as the panels for input and output displays, as well as rckvant cognitive processes such...

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