As the focus of this research, a new methodology -- human Performance Assessment Methodology (PAM), is introduced. PAM provides a quantitative basis for evaluating display image quality based on the visual events that occur in a task. The PAM approach identifies the visual events, decisions, and actions for a display system. To support PAM, a theoretical model, the Model of Visual Events (MOVE), is proposed for describing the relationship between visual events, decisions, and actions. MOVE describes four categories of perceptual decisions (i.e., detect, identify, discriminate, and evaluate) associated with visual events. Formal efficiency metrics are introduced in PAM to describe performance at the visual event, task, and network levels. Using PAM, an efficiency model was created for one visual display parameter (i.e., luminance), one decision type (i.e., detection) and one dependent variable (i.e., visual angle).

Two experiments were accomplished to examine the validity of PAM. A two-factor mixed design was employed for both experiments, where decision type was varied between-subjects and visual display parameter (i.e., luminance or sharpness) was varied within-subjects. In the first experiment, luminance was varied across four levels (3.2, 4.5, 8.6, 16.5 cd/m²) for two decision types (detection and identification). In the second experiment, three levels of sharpness (50% spot width - 0.508, 0.711, 0.864 mm) were combined factorially with two decision types (detection and identification). In both experiments, participants visually 'walked down a path' and either detected or identified visual targets presented on the screen. Time-to-target and subjective responses were measured for each study.

The results of the first experiment show that time-to-target and subjective rating significantly change as a function of luminance. For the sharpness variable in the second experiment, a significant difference was found for time-to-target while subjective rating was non-significant. In both studies, participants detected visual targets quickly, but required more time to identify targets.

Using the PAM, functional relationships for luminance and sharpness were determined for detection and identification decisions. When detection data from the current study were contrasted with previous detection data, general agreement was found between the data sets. This research defines PAM and shows its utility for modeling the functional relationships among visual parameters. Further research is needed to validate and refine the PAM approach.