In sum, the eye-tracking data support an account in which Os make a single search through the visual image in a hybrid search. The amount of time required to process each visual item increases as a function of the log of the MSS. At low MSS, the demands of the memory search are moderate enough to allow the observer to determine that some objects near the current fixation can be rejected without additional foveal processing. Thus, high performance can be obtained despite Os having fixated only about one quarter of the items on the screen. As a corollary, the FVF is relatively large. At large MSS, it takes longer to process each item. Indeed, by the time MSS is 100, the memory search burden is so great that determining whether a single item is a target occupies the entire dwell time. We did not test if Os could determine if an item was one of 100 target types while not fixating on the object. Left to their normal devices, Os undoubtedly fixated the current object of attention while determining if it was in the memory set. Some items are refixated, perhaps because memory for rejected distractors is imperfect. MSS increases the time required to handle each visual item that is selected. Other changes in the visual search appear to follow from changes in that memory search time.
This work is related to a number of recent studies that have investigated the role of the target template in visual search. According to this line of research, Os determine where to look for potential targets primarily on the basis of three sources of information: low-level salience, scene context, and target template information (Malcolm & Henderson,
2010). Target templates are held in memory, then compared to visual information in order to determine whether a given item is a target (Olivers, Peters, Houtkamp, & Roelfsema,
2011; Zelinsky,
2008). As a result, distractors that are more similar to the target are more likely to be fixated (Findlay,
1997; Zelinsky,
2008). In the current study, low-level salience and scene context were carefully equated across conditions, so in this view, only differences in the target template would modulate behavior. Previous research has demonstrated that searching for more than one category of targets results in less efficient guidance toward target features (Menneer et al.,
2012). Similarly, Godwin, Hout, and Menneer (
2014) manipulated the fidelity of the target template by providing a target cue picture that either exactly or approximately matched the target. They found that RT, scan-path ratio (which measures the efficiency of the eye-movement path to the target), and decision time (which measures the time that elapses between first fixating a target and an affirmative button press) all increased when targets were less well defined. Thus, increasing the number of potential targets appears to result in similar changes in behavioral and eye-tracking differences observed when the target template is deliberately weakened.
As noted above, these results demonstrate that the FVF is not a simple product of the physical stimulus. In the same display, given a particular fixation, an object away from the point of fixation may be successfully processed when MSS is one but not processed when MSS is 100. This can be seen as an example of a form of tunnel vision produced by cognitive load—in this case, a memory load (de Haas, Schwarzkopf, Anderson, & Rees,
2014; Mackworth,
1965; Williams,
1985). Based on this interpretation, one might imagine that something like the dramatic inattentional blindness effects of Mack and Rock (
1998) could be obtained with the larger MSS.
The large changes observed in our estimates of FVF illustrate an important aspect of eye-tracking research that is often overlooked. Many studies of visual attention necessarily make assumptions about the size of the FVF although it is unusual to see these assumptions formalized. Any time we estimate the percentage of an image that has been covered by the eyes and any time we estimate the dwell time on an object, we are making an assertion about how far away an object can be from fixation and still be processed. Indeed, the heat maps in
Figure 4 use this same line of reasoning, which is why coverage for MSS one appears so much lower than MSS 100. However, by estimating coverage based on the functional field of view size in target-present trials, we saw that the coverage estimate did not vary with the MSS in target-absent trials. Closely examining the data with FVF in mind reveals that this simple shortcut of assuming a constant FVF across conditions may lead to misleading conclusions if taken at face value. One illustrative example of this comes from the medical image perception literature. Many studies have shown that experts make far fewer fixations and longer saccades than novices evaluating the same medical image (e.g., Bertram et al.,
2016; Kundel & La Follette Jr.,
1972). It is therefore likely that simple measures of coverage would suggest that experts examine less of the image yet find more abnormalities. We predict that a FVF analysis would produce larger FVFs in experts than in novices. This could reflect an expert's ability to pull more meaningful information from the periphery thanks to years of experience. Alternatively, it could reflect the expert's knowledge of where
not to look. Either way, traditional methods of estimating coverage based on a static estimate of FVF may lead to misleading results, particularly when analyzing between-subject differences in expertise.
Using the methods of Young and Hulleman (
2013), we could use our estimate of FVF from present trials to estimate the proportion of items that were processed on these trials as a function of MSS. Young and Hulleman estimated that between ∼71% and ∼89% of the items were processed, depending on the difficulty of their search tasks. If the remainder of items were not processed, one would expect overall error rates to be approximately 100% minus the percentage coverage. Thus, when our FVF calculation yields a coverage of ∼76%, this means that 24% of items were not processed and that 24% of targets should be missed. Moreover, our estimate of this rate did not vary with MSS. This prediction is at odds with the actual error data. Error rates are generally too low and the false-alarm rate clearly increases with MSS. Young and Hulleman note that such apparent discrepancies suggest that the methods of deriving FVF may be underestimating the actual size of the FVF. Clearly more work needs to be done to map out the implications of this issue.