Comparatively little perceptual research has been carried out
on the relative strengths of various depth cues, and only a small
portion of that research has addressed issues specifically related
to computer interfaces. In one early cue conflict study, Schriever
(1925) compared the relative influences of binocular disparity
(i.e., stereoscopic displays), perspective, shading and occlusion,
and showed, among other things, the dominance of occlusion over
disparity information. More recently, Braunstein, Anderson, Rouse,
and Tittle (1986) showed that edge-occlusion dominates disparity
when the two conflict, a result which has important implications
for planning the placement of objects in depth in stereoscopic
display design (McAllister, 1993). Even more recently, Wickens
et al. (1989) , in a review of the depth combination literature,
concluded that motion, disparity and occlusion are the most powerful
depth cues for computer displays. The results presented here clearly
contribute to that literature by illustrating some of the powerful
advantages that can be afforded by augmenting visual feedback
through both semi-transparency and binocular disparity.
5.4.1 Properties of Semi-transparency: Discrete, Relational
Depth Cueing
Two particular properties of semi-transparency are especially relevant to 3D interaction systems. One of these is the fact that a semi-transparent surface does not completely block out the view of any object which it (partially) occludes. This eliminates one of the disadvantages of the powerful total occlusion cue and permits the user to maintain awareness of the background information.
The second property relates to the fact that the semi-transparency cue provides primarily relational or discrete depth information about the position of a semi-transparent surface relative to other objects. This information is discrete in the sense that it can take on only one of three possible values: in front of, within and behind. This is in contrast to stereoscopic displays, which provide continuous quantitative depth information. As illustrated in Figure 5.1 and Figures 5.5 to 5.7, we see how the silk covering on the volume cursor directly reveals whether an object is in front of the cursor volume, within it, or behind it. When an object is behind a semi-transparent surface, however, the user is not able to tell by how much the object is separated from the surface in depth. For some tasks, such as making an absolute judgement of distance, the discrete nature of the silk surface may represent a shortcoming, whereas for others it will be a distinct advantage, since the user does not have to use such continuous information for making discrete decisions. This was precisely the case in the experiment presented here, where the objective was to manipulate the cursor such that it totally enveloped the fish being chased. This is clearly a discrete task, as the subjects were instructed simply to capture the fish and not necessarily to centre the cursor on it as accurately as possible. This contention is supported by evidence from the experiment, where in Figures 5.8 and 5.9 we see clearly that semi-transparency was a more effective cue than binocular disparity for successful target acquisition. However, upon examining Figure 5.10, we note that the mean error magnitude of the SilkMono case was larger than that of the WireframeStereo case. The implication of this is that, although fewer errors were made under the SilkMono condition relative to the WireframeStereo condition, the magnitude of those fewer errors must have been relatively larger than with the WireframeStereo case, thus supporting the distinction between discrete versus continuous depth information.
Although static semi-transparent surfaces provide primarily discrete
cues, continuous depth information can nevertheless be acquired
when semi-transparent surfaces are used as a dynamic interaction
tool. That is, when the silk cursor is being actively moved through
another 3D object, the user can potentially estimate the object's
depth by estimating the distance travelled in passing through
the object, through timing and kinaesthesia.
5.4.2 Interactions among depth cues: Modelling of 3D performance
The manipulation of two sources of depth information in this experiment, occlusion and binocular disparity, brings to the fore an important theoretical question: When multiple sources of depth information are provided, how does the visual system judge actual depth information and how does performance change accordingly? Our visual system could either select one of the multiple sources or integrate them to form a decision. Two classes of models have been applied to address this issue, additive models and multiplicative models (Bruno and Cutting, 1988; Sollenberger, 1993). An additive model represents the fact that either depth cue can improve performance on its own and when both sources of information are present simultaneously the resulting performance improvement is a simple summation of the benefits from the two sources individually. A multiplicative model describes the fact that the two sources of information can interact, causing a combined effect either greater or less than the additive effects. In their study of the combination of relative size, projection height, occlusion, and motion parallax, Bruno and Cutting (1988) concluded that additive models produced the best fit to their experimental data. In a series of experiments with motion parallax (kinetic depth) and binocular disparity, Sollenberger (1993) found some evidence for a multiplicative model with greater than additive effects for his path-tracing task.
In the present experiment, binocular disparity and partial occlusion, as measured by both trial completion time and error rate were also found to be compatible with a multiplicative model, but with less than additive effects. As shown in Figures 5.8 and 5.9, a strong interaction was found between display mode and cursor type for both trial completion time and error rate. That is, both stereo display alone (i.e. WireframeStereo) and silk cursor alone (i.e. SilkMono) greatly improved performance relative to WireframeMono, but further improvements from SilkMono to SilkStereo (i.e. with both cues present) was marginal.
For cases in which targets were missed, on the other hand, the
pattern of error magnitudes (Figure 5.10) conformed with an additive
model, since no interaction was found between display mode and
cursor type (F(1,11) =0.0004, p = .97).