The mean RT data were submitted to a mixed analysis of variance (ANOVA) with the following factors: 4(Block) x 2(Cuing) x 2(Group: With vs.
There was also a main effect of Block, F(3, 42)=16.66, MSE=4844, p<.0001, indicating a gradual decrease in RT with practice in the task, and a Block x Cuing interaction, F(3, 42)=7.11, MSE=1218, p<.001, revealing a linear shift of the cuing effect toward facilitation with practice, F(1, 14)=10.97, p<.01 (F< 1, for both the quadratic and cubic tendencies): As it can be observed in Figure 2, in the group without fixation cue, the facilitatory cuing effect increased with practice, whereas in the group with fixation cue, the IOR effect decreased with practice, F(1, 14)=4.37, p=.05, and F(1, 14)=6.73, p<.05, respectively for the linear components.
Here the instance of an automation cuing failure can be categorized into one of three cases: (a) The automation misses an event it should have noted (and fails to provide a cue); (b) the automation incorrectly classifies an event as important (and cues it); and (c) the automation does not cue the location of the event with great precision, indicating only the general area of the event.
At the time of the initial failure of what had previously been a perfect system, the operator will typically be overreliant on the cuing (Parasuraman & Riley, 1997) and may fail to detect the target (Case A: automation miss) or may falsely classify a nontarget as a target (Case B: automation false alarm).
The analysis revealed main effects of Cuing, F(1, 54) = 17.12, MSe = 503.03, p<0.0005, and Spatial Congruency, F(1, 54) = 62.82, MSe = 1229.04, p<0,0001.
There was a two way interaction between Cuing and Distractor group F(1, 54) = 27.17, MSe = 503.03, p<0.0001.
However, Jansen, See, Riegler, and Davis (1999) found that observers' use of cues in target acquisition was unaffected by the cue's accuracy but that cuing prompted faster decision speed.
All these measures are potentially affected by an automated cuing system.
Such imperfect attention-guiding automation has been frequently examined in the context of alarm systems (Parasuraman et al., 1997; Sorkin, Kantowitz, & Kantowitz, 1988; Swets, 1998) and in intelligent diagnosis (Mosier, Skitka, Heers, & Burdick, 1998), but it has received less examination in the context of target cuing (Entin, 1998; Merlo et al., 1999).
In the present experiment we examine this time course of automation reliance before and after the occurrence of a first failure in a target cuing paradigm.
It is relatively straightforward to predict that cuing
a target location will facilitate the detection of that target (Flannagan, McAnally, Martin, Meehan, & Oldfield, 1998).
For example, auditory cuing
in an environment that provides a high amount of visual information to a target's location may not need to be as precise as when little or no visual target information is present.