Introduction

Attention Deficit Disorder (ADD) involves severe inattention and/or hyperactivity relative to age cohorts, impairment is present in at least two settings, and these symptoms interfere with social, academic, or occupational functioning. Its prevalence is estimated at 3%-5% in school age children, and is much more common in males than females (APA, 1994). ADD is highly heritable and is associated with prefrontal cortex deficits (Spencer, Beiderman, Wilens & Farone, 2002). ADD is both the most studied and most controversial mental disorder (Wolraich, 1999). There are no pathognomic measures to diagnose it (Wolraich, 1999) and prevalence rates vary immensely. Having ADD puts a child at risk for developing other serious problems (Brown, 2000).

ADD is the most common neurodevelopmental disorder of childhood (Rowland, Lesesne, & Abramowiz, 2002), and carries along with it serious consequences (Barkeley, 2002). ADD not only affects school performance but also has a profound effect on the child’s personal and social development, as well as being a risk factor for the development of other comorbid disorders. Children with ADD are also more likely to experience problems such as school failure, criminal behavior, and substance abuse (Magyary & Brant, 2002).

There are no laboratory tests that diagnose ADD, however, some tests involving effortful mental processing have been found to be abnormal in clinical groups with ADD compared with controls (APA, 1994). Behavioral checklists often have been used as part of diagnostic assessment; however, some researchers believe that clinicians rely too heavily on behavioral checklists when making their diagnosis (Colegrove, Homayounjam, Williams, Hanken, & Horton, 2000). Due to the limitations of traditional assessment measures, information from many different types of ADD measures are needed in order for a diagnosis to be given (Barkley, 1990).

Rating Scales

Rating scales have been commonly used in the diagnosis of ADD (Demary, Elting & Schaefer, 2003). These scales generally rely on input from teachers, parents, or both. Although some studies have found parent and teacher ratings to accurately differentiate between children with ADD and controls (Power et al., 1998), giving the same scale given to both teachers and parents may produce contradictory results. One study found low to moderate agreement for symptoms, but high agreement for diagnosis (Biederman, Faraone, Milberger, & Doyle, 1993). However, others have shown agreement between teachers and parents to be relatively poor (Mitsis, McKay, Schultz, Newcorn, & Halperin, 2000) with one reporting agreement to range from .0 to .38 (Boyle, Offord, Racine, & Szatmari, 1996). Based on these findings researchers suggest that a diagnosis based on information from either one of these sources alone would be inaccurate (Mitsis et al.)

Continuous Performance Task
One common measurement that involves effortful mental processing which has been used in the assessment of ADD is the Continuous Performance Task. Studies have noted that people with ADD perform significantly poorer on continuous performance tasks than controls (Aylward, Verhulst, & Bell, 1990). Specifically, those with ADD make more omission errors (Barkley, Grodinsky & DuPaul, 1992; Klee, Garfinkel, and Beauchesne, 1986; Shallice, et al., 2002), commission errors (Shallice et al.), and have poorer performance on the overall index score (Perugini, Harvey, Lovejoy, Sandstorm & Webb, 2000). This finding has been consistent across child (Barkley et al.; Perugini et al.; Shallice et al.) and adult (Klee et al.; Siedman, Biederman, Weber, Hatch, & Faraone, 1998) populations, with most research being conducted on males.

Barkley, Murphy, and Kwasnik (1996) state that adults with ADD “made more impulsive errors and errors of omissions, and were more variable and less attentive in their response patterns than the control subjects (p.48). However, a review of the literature by Corkum and Siegel (1993) found mixed results, depending on subtypes and comorbidity with other mental illnesses. Grodzinsky and Barkley (1999) found that for boys, the Continuous Performance Test scores of number correct and number of commissions had a positive predictive power of over 80%; however, these scores had only a moderate negative predictive power. The authors suggest that while “abnormal scores on the CPT may indicate a relatively high probability for the diagnosis of ADD…nearly 60% of the ADD children received normal scores on this test (p.17).

Virtual Reality Classroom

Recently, a virtual reality classroom has been created in order to assess attentional difficulties in a controlled, more ecologically valid environment. The classroom allows researchers to measure responses to a CPT with distracters in the environment. Distracters can be audio, visual, or mixed audio/visual. Examples include classroom sounds, a paper airplane flying by, a car that drives by the window, and a man walking in and out of the classroom.

Rizzo et al. (2002) conducted a clinical pilot study utilizing the Virtual Reality Classroom in a head-mounted display with eight boys with ADD and ten control children without diagnosis. Their ages ranged from six to twelve years. ADD children performed significantly poorer than control children in both non-distracter and distracter conditions. Specifically, those with ADD had a slower correct hit reaction time in the distraction condition, higher correct hit reaction time variability in both conditions, as well as more omission and commission errors in both conditions than did controls.

Eye-tracking

It has been suggested that people with ADD spend more time off-task during mundane activities than the general population. With the advent of virtual reality and eye tracking systems, we can now measure this objectively. The use of eye tracking enables us to observe and record the participant’s line of vision and movement of the eyes throughout testing to both distracter objects and target objects, as well as giving us the ability to measure time off task. Researchers who have used eye-tracking within virtual environments state that an eye movement analysis algorithm can be used to analyze the visual processes of participants in a virtual environment and may lead to insights in the cognitive processes of participants in virtual reality (Duchowski et. al., 2002).

Research involving eye tracking has important implications for ADD. Sneed (1999) found that eye gaze tracking measuring time on task and time to return to task was able to correctly classify 64.7 % of adolescents as either having or not having ADD. By using the eye tracking technology together with the virtual ADD classroom presented in a virtual reality dome, more can be added to the information found by Rizzo et. al.’s (2002) use of the classroom with a head mounted display.

By combining the CPT and the eye tracker system together with a controlled virtual reality environment, researchers may develop a tool that in combination with a clinical interview can more accurately assess and diagnose ADD. The present study will investigate the ability of the virtual classroom and eye tracker combination to discriminate between an ADD and a control group. The BASC, a parent rating scale, will be administered in order to provide more information. Specifically, it is hypothesized that overall scores on the Virtual Reality Classroom will be significantly lower for children with ADD than controls, and that children with ADD will have significantly more head turn variance and exhibit more time off task, as measured by an eye-tracker, than controls.