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Setting attentional priorities : a comparison of the contributions of new objects and luminance changes Austen, Erin Leigh 1999

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SETTING ATTENTIONAL PRIORITIES: A COMPARISON OF THE CONTRIBUTIONS OF NEW OBJECTS AND LUMINANCE CHANGES by ERIN LEIGH AUSTEN B.A., Saint Francis Xavier University, 1996 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER' OF ARTS in THE FACULTY OF GRADUATE STUDIES (Department of Psychology; PCE) We accept this thesis as conforming ^^ to^ he^ re^ tiired standard THE UNIVERSITY OF BRITISH COLUMBIA June 1999 (c) Erin Leigh Austen, 1999 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of T ~ ^ e U o l o ^ ^ The University of British Columbia Vancouver, Canada DE-6 (2/88) 11 Abstract New objects capture attention more reliably than sudden changes in features of existing objects. Item visibility may be responsible. In the present study, visibility of new objects and luminance changes was controlled by luminance contrast manipulations. In Experiment 1, search for new targets was faster than old targets, at all contrasts. This benefit for new targets was indexed by a search efficiency measure that served as a standard to evaluate other feature changes. In Experiment 2, the attentional effect of luminance changes in old items was measured. Luminance changes generally resulted in small search benefits, although polarity reversals led to larger benefits than changes in magnitude or contrast. Only a simultaneous polarity and contrast change captured attention as efficiently as a new object. It is suggested that new objects are assigned attentional priority; and old objects are treated as new when they undergo changes typically reserved for signaling new objects. iii TABLE OF CONTENTS Abstract ii List of Tables '. iv List of Figures v Introduction 1 Competing Attentional Capture hypotheses 4 Evaluating the current visual capture hypotheses 6 Experiment 1: Feature strength and attentional capture by new objects 8 Method 10 Results 13 Discussion 17 Experiment 2: Luminance Changes 20 Method 21 Results 24 Discussion 25 General Discussion 27 Tables 32 Figure Captions 38 Figures 40 References 49 iv List of Tables Table 1: Proportion of errors for new and old targets in Experiment la 32 Table 2: Proportion of errors for old targets only in Experiment la 33 Table 3: Proportion of errors for new and old targets in Experiment lb 34 Table 4: Proportion of errors for old targets only in Experiment lb 35 Table 5: Proportion of errors for targets in Experiment 2 36 Table 6: Search efficiency ratios 37 List of Figures Figure 1 40 Figure 2 41 Figure 3 42 Figure 4 43 Figure 5 44 Figure 6 45 Figure 7 46 Figure 8 47 Figure 9 48 ATTENTIONAL CAPTURE 1 Setting Attentional Priorities: A Comparison of the Contributions of New Objects and Luminance Changes Every time we open our eyes, we are hit with an abundance of visual stimuli. With so much information confronting us, it is necessary to have some way of selecting what is most important, so that we may act upon it efficiently. What, then, determines selection? One might think that what is selected is determined on the basis of our personal goals, that is, what we want to see. This, however, is only part of the story. Selection is also dependent on the neural machinery used to process visual information. To complicate matters, selection in this way does not always coincide with selection that is goal-directed (Egeth & Yantis, 1997). A complete account of visual selection must distinguish between goal-directed processing (i.e., what we want to see) and stimulus-driven processing (i.e., reflexive selection on the basis of stimulus properties). The distinction between these two dimensions is clear when we consider our own life experiences. Imagine searching for your car in a crowded parking lot, or searching for a friend who is saving a spot in line at the movie theatre. In each case you have specific search goals in mind: to locate your small red sports car, or to find your friend with the blue sweater and long black hair. Selection under these circumstances is often referred to as goal-directed, top-down, effortful, and intentional, and is said to be under endogenous control. As this type of search proceeds, however, you may unintentionally process other events. For instance, while searching for your car, you may take note of a plastic shopping bag being carried by the wind, or similarly, while looking for your friend, you may notice a dog scampering ATTENTIONAL CAPTURE 2 by. This type of selection is often referred to as stimulus-driven, bottom-up, automatic, and unintentional, and is said to be under exogenous control. When everyday examples such as these are considered, it is clear that selection is determined both by intentions, and by the saliency of a stimulus event. Selection that is governed by our intentions seems fairly straightforward, but what determines selection that is stimulus-driven? What is considered important enough to be selected by our visual systems? Much investigative effort has recently been expended to answer this question (Gibson, 1996; Theeuwes, 1991; Todd & Kramer, 1994; Warner, Juola, & Koshino, 1990; Yantis, 1993). One area of research that has proved particularly fruitful for understanding stimulus-driven processes is that of visual search. For instance, in a variety of studies on stimulus-driven attentional capture, capture by the sudden appearance of new objects has been tested, as have a variety of feature changes, such as luminance and color changes (e.g., Folk & Annett, 1994; Jonides & Yantis, 1988; Theeuwes, 1994; Yantis & Jonides, 1984). The logic of the typical visual search task that is designed to test whether new objects capture attention is as follows: If attention is 'captured' by a new element then it will be one of the first elements examined during search. This capture will occur despite top-down knowledge that the new item is non-informative with respect to the target of search. Search time for the target when it is the new item should be fast and independent of display size. In contrast, search time for the target when it is not the new item should increase as a function of display size. Jonides and Yantis (1988), for example, tested search time for new and old items. They presented observers with als preview display of digital clock figure eights, followed by a search display of letters. The observer's task ATTENTIONAL CAPTURE 3 on each trial was to indicate, as quickly as possible, the presence/absence of a pre-specified target letter. Letters in the search display could be formed by dropping line segments from the figure eights. In the onset/no onset condition, one new letter appeared in the search display in a position that did not contain a figure eight in the preview display. This manipulation occurred independently of the target of search, such that the target was the new letter on only a fraction of the experimental trials. Jonides and Yantis reported response time slopes (i.e., change in RT with each additional element) for new target letters that were shallower than those for old targets (i.e., targets formed by dropping line segments from the figure eights). These results suggest that new objects capture attention and are assigned attentional priority in search. Evidence for attentional capture by new objects is strongest under conditions of minimal observer expectation. For instance, a new object will not capture attention if observers can attend to a specific target location prior to each trial (Theeuwes, Kramer, Hahn, Irwin, 1998; Yantis & Jonides, 1984), or if observers are aware that the target is uniquely defined by color (e.g., Theeuwes, 1991). Feature changes, such as changes in luminance or color, capture attention less reliably than new objects (cf., Yantis & Hillstrom, 1994; Posner, Snyder & Davidson, 1980). Results from peripheral cueing tasks (where the cue is a luminance change), for instance, have revealed that response times to targets in the cued location (valid cue) are faster than to targets at an uncued location (Lambert, Spencer & Mohindra, 1987; Posner, Snyder & Davidson, 1980). This is true even when cues are known to be irrelevant for target search. Nonpredictive luminance changes within other search displays, however, have resulted in minimal reductions in response times to a changing target letter ATTENTIONAL CAPTURE 4 compared to a nonchanging target (Yantis & Hillstrom, 1994). What is it then about new objects that allow them to capture attention with greater consistency than other changes? Competing Attentional Capture hypotheses: Current hypotheses about the capture of attention by new objects fall into two camps. The first will be referred to as the new features hypothesis (Gellatly, Cole & Blurton, 1998; Theeuwes, 1994; Thomas & Luck, 1998), and the second as the new objects hypothesis (Jonides & Yantis, 1988;Yantis & Hillstrom, 1994;Yantis & Jones, 1991; Yantis & Jonides, 1984). According to proponents of the new features hypothesis, features such as form, color and motion, activate relevant areas in the visual system. This is illustrated in Figure 1. The parvocellular visual pathway, for example, would be activated in response to form and color changes, and the magnocellular visual pathway to transients. The strength of the combined activation in these areas within the visual system therefore determines the saliency of an object. In the case of new objects, changes occur all at once along several feature dimensions, and each leads to some activation within the visual system. For instance, new objects can be associated with a luminance change, the appearance of contours, and a color change. The combined activation from these features will result in attention being directed to the location of change. One prediction from the new features hypothesis is that the greater the number of features that are varied at a particular location, the more salient the element at that location will be, and the stronger the resulting attentional capture effect. A second prediction that arises from this hypothesis is that by increasing the strength of any given feature within an item (e.g., increasing the magnitude of a change), the item's saliency will increase, as will the likelihood that that item will capture attention. It is important to ATTENTIONAL CAPTURE 5 note that proponents of the new features hypothesis afford no special status to new objects. Rather, new objects are believed to be effective in capturing attention simply because they are associated with several new features, each which activates areas of the visual system, and increases the saliency of the object. This point is the main difference underlying the new features hypothesis and the new objects hypothesis. Proponents of the new objects hypothesis argue that attentional priority is assigned on the basis of new visual representations (e.g., Jonides & Yantis, 1988, Yantis & Jonides, 1984). In this view, an object representation is created in conjunction with the appearance of new objects, and stored as an "object file". This obligatory creation of object representations is central to object-based theories of attention (Duncan, 1984; Kanwisher & Driver, 1992; Kahneman, Treisman & Gibbs, 1992). Unlike new objects, existing objects that are changed in some minimal way require only an update of their current object files rather than the creation of new object fdes. It has been suggested that new object fdes are created only in response to the appearance of a new object, or when an element is somehow segregated from its background (e.g., motion-induced segregation), and not simply with changes in luminance or color of an existing object (Yantis & Hillstrom, 1994). Larger attentional priority tags are assigned to new object fdes compared to updated files. It is these priority tags that determine the order in which elements in a visual scene are examined. In this view, new objects maintain a special status within the visual system, and as such, they Eire among the first elements to be examined in a visual array. ATTENTIONAL CAPTURE 6 Evaluating the current visual capture hypotheses: Strong evidence for the new objects hypothesis comes from demonstrations of capture by stimuli that are not associated with any luminance transients (Gellatly, Cole & Blurton, Exp. 2, 1998; Thomas & Luck, Exp 4, 1998; Yantis & Hillstrom, 1994). Yantis and Hillstrom (1994), for instance, used stimuli whose contours were defined by texture, depth, or motion, thus limiting the luminance transients typically associated with the appearance of a new object. In all three of these cases, the new objects were detected faster than old objects. Thus, luminance transients, which might at first seem to be a potentially important feature of new objects, are evidently not necessary for attentional capture. Similarly, Yantis and Hillstrom have demonstrated that luminance transients, in the absence of a new object, are not sufficient to capture attention. There is some evidence, however, to challenge a conclusion that feature changes alone (such as a luminance change where there is no new object) are not sufficient to capture attention (e.g., Lambert, Spencer & Mohindra, 1987; Thomas & Luck, 1998). For instance, Thomas and Luck showed that cues involving a color change (Exp 1), or a motion change (Exp 2) in a pre-existing object, produced search benefits for a target that appeared in the cued location compared to one that appeared in an uncued location. Clearly then, neither featural changes alone, nor new objects alone appear to be the sole contributors to stimulus-driven selection. Rather, a more complete view is that featural changes contribute in some way to the attentional capture effect observed for new objects. The question that remains, however, is to what degree do feature changes contribute to this benefit in response time that is typically observed for new objects? To date, there has been no systematic way to evaluate the influence of such feature changes ATTENTIONAL CAPTURE 7 on the attentional capture effect, nor is there a way to directly compare the search benefit from new objects to that of luminance changes. Similarly, no one has yet ruled out the possibility that the reported failures of luminance transients and the success of new objects to capture attention is due to nothing else but a difference in visibility between the two. That is, the possibility has not yet been eliminated that if new objects and luminance changes are somehow matched in visibility any differences between the two will disappear. Results reported by Theeuwes (1994) suggest that this might be the case. Theeuwes reported that the saliency of an element determined whether or not it would capture attention, that is, when the saliency of the color change was reduced, more salient new object distractors disrupted search for a uniquely colored item. Thus, until new objects and luminance changes have been equated for visibility, no conclusive decisions can be made with respect to which capture hypothesis is more accurate, the new features hypothesis, or the new objects hypothesis. The primary goal of the present research was to make a direct comparison between the contributions of feature changes and new objects to the setting of search priority. It is assumed that if an item has been assigned attentional priority in search, response time to that item will increase minimally with increases in display size, that is, response time slopes for the item will be shallow. Search priority (measured by response time slope) was tested for new objects as well as for old objects that underwent a feature change in the form of a luminance change. In order for a direct comparison between the two to be valid, it was first necessary to try and equate the luminance change with the new object on some other variable (e.g., visibility). ATTENTIONAL CAPTURE 8 Two experiments were designed to meet the goal of the present study, using the well-known visual search task as a starting point. The novel feature of these experiments was a systematic manipulation of the visibility of the search items. This was done by holding constant the luminance of the background across both experiments, and manipulating only the luminance values of the search items (see Figure 2). In this way, the contrast of the search items was varied so that there were both high contrast (most visible) and low contrast (less visible) items. Contrast, in this sense, is simply a means of expressing the visibility of an item relative to its background. In Experiment 1, we measured response times for identifying targets, both old and new, as a function of background contrast. We then established a measure of search efficiency for each contrast value. Search efficiency was defined as: RT slope for old items / RT slope for new items This measure served as a standard by which to evaluate other feature changes in the absence of new objects. In Experiment 2, we again measured search times for targets under a range of luminance values, but this time there were no new objects. Instead, there was one luminance change among all old search items. Search efficiency measures were established by taking the ratio of response time slopes for non-changing targets to changing targets. Then, by using the standard established in Experiment 1, we were able to directly compare search efficiency for new targets to that of old targets involving various types of luminance changes. Experiment 1: Feature strength and attentional capture by new objects The first experiment was designed to establish a standard of search efficiency for new objects that could be used later to evaluate the relative contributions of feature ATTENTIONAL CAPTURE 9 changes in old targets. Experiment 1 also provided a test of the importance of feature strength (in this case, item contrast) for attentional capture by new objects. By obtaining a continuum of contrast values it was possible to manipulate target visibility. The design of this experiment was a visual search task in which observers were asked to rapidly discriminate a target letter among distractors. Elements were presented in one of four luminance values on a background of intermediate luminance (refer to Figure 2). That is, two of these luminance values were greater than the luminance of the background, while the remaining two luminance values were less than that of the background. One way to compare the luminance value of an element to the luminance of the background is to express the difference between the two in terms of contrast. Contrast is calculated here in Michelson units as: (Lmax-Lmin) / (L m ax Lmin) where L m a x refers to the larger of two luminance values (e.g., background vs. element), and Lmin represents the smaller of the two. Using elements of varying luminance and a constant background luminance, we were able to obtain a range of item contrasts, including items of very low contrast to items of relatively high contrast. If feature strength is important for attentional capture, then the attentional capture effect that is typically observed for new objects should decrease with reductions in contrast. If, on the other hand, new objects themselves are important for attentional capture, rather than the strength and number of features, then the response time benefit observed for new targets compared to old targets should remain, even at low contrasts. ATTENTIONAL CAPTURE 10 Method Experiment 1 was conducted in two steps because the luminance values of our initial low contrast items (Experiment 1 A) produced the same overall pattern of search results as the high contrast items, with the exception that overall search times were shifted in an upwards direction. It was not clear whether this same pattern of results would hold over all manipulations in contrast, or whether a larger contrast reduction was necessary before a different pattern of results would emerge. It was thus necessary to test another series of luminance values with even smaller background contrasts (Experiment IB) before any conclusions could be drawn about the contributions of feature strength to the attentional capture effect for new objects. The methods for Experiment 1A and IB are presented together as the only difference between them was the luminance of some of the items. Experiment IB may be considered a replication of 1A over a smaller range of item contrasts. Participants: A total of 24 observers (12 per experiment) were recruited from the undergraduate psychology subject pool at the University of British Columbia to participate in one 50 minute experimental session. Partial course credit was awarded for participation. Observers were uninformed as to the purpose of the experiment until the completion of their session, at which point they were given a full debriefing. All observers reported having normal or corrected-to-normal vision. Apparatus: Displays were generated by a PowerMac 8100/80 AV computer and viewed on a 17" Apple Multiple Scan monitor. Observers were required to use a chin rest to maintain a viewing distance of 80 cm. Forced-choice responses were made by pressing one of two keys on a standard computer keyboard with the index finger of each ATTENTIONAL CAPTURE 11 hand. The 'z' key was used to indicate the target letter E, and the 7' key was used to indicate the target letter S. Stimuli: Digital clock figure eights were used as placeholders prior to the appearance of the search display of letters (see Figure 3). The letters 'E' and 'S' served as target letters, while the letters 'FT, 'P', and 'U' were used as distractor letters. Each letter could be formed by dropping two line segments from the figure eights. This allowed for 'old' letters to be camouflaged by the figure eights prior to their appearance in the search displays. Insert Figure 3 about here Luminance readings were taken with a photometer. Mean luminance readings indicated here have been averaged over several (6 to 10) readings. The display background was a medium gray (17.68 cd/m2) in both experiments. The figure eights and letters appeared in one of four luminance values (labelled A to D in Figure 2); in 9 9 9 Experiment la, white (28.53 cd/m ), light gray (21.14 cd/m ), dark gray (14.69 cd/m ) or black (10.37 cd/m2); in Experiment lb, white (21.14 cd/m2), light gray (18.65 cd/m2), dark gray (16.55 cd/m2), and black (14.69 cd/m2). Each item in the display measured approximately 0.5 * 1.0 degrees of visual angle. Items were randomly located in one of eight equally spaced positions on the circumference of an imaginary circle (radius of 3.0 degrees from center). Feedback was provided at the end of each trial in the form of a plus sign (correct response), a minus sign (incorrect response) or a circle (timeout symbol), at the center of the screen. These symbols served as a fixation point for the start of the next trial. ATTENTIONAL CAPTURE 12 Design: In order to familiarize each observer with the task and to ensure that they had understood the instructions, observers were allowed 10 practice trials, in the presence of the experimenter. After the experimenter was satisfied that the instructions were understood, and the observer was comfortable with the task at hand, the experiment began. Each observer participated in 8 blocks of 60 trials for a total of 480 experimental trials. Two display sizes (4 and 8) were equally and randomly intermixed throughout the experiment. Each trial consisted of an initial figure eight display (either 3 or 7 figure eights) followed by the search display. The search display included an 'E' or an 'S' as the target letter, and an equal number of each 'H', 'P', and 'U' distractor letters. When the search display of letters appeared, observers were required to indicate as quickly as possible whether an E or an S was present in the display. On each trial, one new letter onset at the same time that the remaining letters were uncamouflaged from their preceding figure eights. The new letter was the target letter on only 1/n trials (where n refers to the display size). That is, the appearance of the new onset letter rarely coincided with the appearance of the target letter, and thus offered no predictive information with respect to the location of the target. Procedure: Observers indicated as quickly as possible the identity of the target letter ('E' or 'S'). They were instructed to "respond as quickly as possible, without making errors". A fixation point was present at the beginning of each trial and observers were asked to fix their gaze on this location. Each preview display of figure eights appeared for 1000 ms followed by the search display which remained on screen until the observer responded or until 2000 ms had elapsed. A feedback symbol was then presented ATTENTIONAL CAPTURE 13 and remained on screen for 500 ms, after which 1.5 seconds elapsed before the appearance of the next preview display. After each block, a message box appeared informing observers of the test block and the error rate. If more than 10% errors were made on any given block, observers received a warning message to reduce the number of errors. Blocks were self-initiated and observers were encouraged to rest between each block before beginning the next one. Results Experiment la: Mean correct RT for new (i.e., onsetting) targets and old (i.e., uncamouflaged) targets are presented in the left-side panel of Figure 4. Statistical analyses revealed that new targets captured attention, and were identified significantly faster than old targets, even at low contrasts. A 3 within, 0 between repeated measures ANOVA was computed using Response Time (RT) as the dependent variable, and Target Type (new and old), Target Contrast (high and low), and Display Size (4 and 8) as factors. All three main effects were significant (Target Type, F (1, 11) = 47.14, p<.001, Target Contrast, F (1, 11) = 23.44, p<.001, and Display Size, F (1, 11) = 88.64, p <.001). Most important for our purposes was the significant Target Type x Display Size interaction, F (1, 11) = 20.30, p <.001. Clearly, when the target was a new item, display size had a minimal effect on RT, however, when the target was an old item RT increased significantly with an increase in display size. The Target Contrast x Display Size interaction was significant, F (1, 11) = 4.813, p<.051. High contrast targets resulted in smaller overall mean RT slopes than low contrast targets. No other interactions were significant. Insert Figure 4 about here ATTENTIONAL CAPTURE 14 Mean error rates are presented in Table 1. As can be noted from the table, errors accounted for less than three percent of all responses. An accuracy analysis revealed no significant effects (all p's > .05). Speed-accuracy tradeoffs are characterized by an inverse relationship between errors and RT. That is, a speed-accuracy tradeoff may be indicated by an increase in errors alongside a reduction in RT, or by a decrease in errors alongside an increase in RT. The small positive correlation obtained between errors and RT is inconsistent with a speed-accuracy tradeoff (r = .252). Insert Table 1 about here Mean correct RT for old targets only are presented in the right-hand panel of Figure 4. Analyses revealed that high contrast targets are identified faster than low contrast targets. Since this analysis focused on old targets only, this meant that one of the distractors was the new or onsetting item. The contrast of this onset distractor (Onset Contrast) was a used as a factor in the following analysis so that we could determine whether RT to the target letter (old) changed as a function of the contrast of the onset letter (new). For instance, it may be that RT to the target were slower when the contrast of the distractor onset was high compared to when it was low. The following analysis allowed us to uncover such trends. A 3 within repeated measures ANOVA of Display Size (4 and 8) by Target Contrast (high and low) by Onset Contrast (high and low) was conducted using only trials in which the target letter was an old item. The main effects of Target Contrast and Display Size were significant (ps < .05), while the main effect of Onset Contrast did not reach significance, F (1,11) = 4.316, p < .07. The Target Contrast x Display Size interaction was significant F (1, 11) = 17.755. p < .002. This interaction reflects a steeper RT slope for low contrast targets than for high contrast targets. The ATTENTIONAL CAPTURE 15 three-way interaction of Target Contrast x Display Size x Onset Contrast approached significance, F (1, 11) = 4.586, p < .06. The steepest RT slope was for low contrast target letters when the onsetting letter was a high contrast distractor. No other interactions were significant. Mean error rates for old targets only are presented in Table 2. As can be noted from the table, errors accounted for less than four percent of all responses. A repeated measures ANOVA was conducted as above using accuracy as the dependent variable. Only the main effect of Onset Contrast approached significance, F (1, 11) = 4.033, p >.69 revealing the pattern for errors to be somewhat higher when the onsetting distractor letter was high contrast than when it was low contrast. There were no other significant effects or interactions (all p's > .05). Evidence against a speed-accuracy tradeoff is provided by the positive correlation between errors and RT (r = .257). Insert Table 2 about here Experiment lb: Mean correct RT for new and old targets as a function of display size and target contrast are presented in the left-side panel of Figure 5. The attentional capture effect for new objects was attenuated at low contrasts, although the benefit for new targets over old targets remained. A 3 within, 0 between repeated measures ANOVA was conducted using response time (RT) as the dependent variable, and factors of Target Type (new and old), Target Contrast (high and low), and Display Size (4 and 8). Similar to the corresponding analysis in Experiment 1 A, there were significant main effects of Target Type, F (1, 11)=18.02, p<.01, Target Contrast, F (1, 11)= 50.49, p<.001 and Display Size, F (1, 11)=90.76, p<.001. The interaction of Target Type x Target Contrast was significant, F (1, 11) = 5.17, p<.05. Response times to new targets were ATTENTIONAL CAPTURE 16 significantly faster than to old targets for high contrast targets only. The Target Contrast x Display Size interaction was also significant, F (1, 11)= 12.28, p <.005. Overall, RT slopes for high contrast targets were shallower than those for low contrast targets. No other interactions were significant. These initial results for Experiment IB reveal that with a large enough reduction in target contrast, the attentional capture effect for new targets is attenuated, although the trend in RT suggests that a benefit for new targets over old targets remains. Insert Figure 5 about here Mean error rates are presented in Table 3. As can be seen from this table, less than 6% of all responses were errors. An analysis of error data revealed no significant interactions (all ps > .05). These results, in conjunction with the positive correlation between errors and RT data (r = .585), suggest that there was no speed-accuracy tradeoff. Insert Table 3 about here Mean correct RT for old targets only are presented in the right-hand panel of Figure 5 as a function of Target Contrast, Display Size, and Contrast of the new item (Onset Contrast). To examine RT data for old targets in more detail, a 3 within, 0 between repeated measures ANOVA of Display Size (4 and 8) by Target Contrast (high and low) by Onset Contrast (high and low) was conducted using only trials for old targets. This analysis revealed significant main effects of Target Contrast, Display Size and Onset Contrast (all ps < .05). There were significant interactions of Target Contrast x Display Size, F (1, 11) = 43.45. p < .001, and Display Size x Onset Contrast, F (1, 11) = 7.822, p<.02. These interactions reflect the findings that RT slopes for high contrast targets were shallower than those for low contrast targets, and further that RT slopes were ATTENTIONAL CAPTURE 17 shallower when the onset distractor letter was low contrast than when it was high contrast. No other interactions were significant. Mean error rates are presented in Table 4. As can be seen from this table, less than 5% of all responses were errors. An analysis of error data revealed a significant main effect of Target Contrast, F (1, 11) = 9.79, p <.01, and a significant Target Contrast x Display Size interaction, F (1, 11) = 7.90, p <.18. This reflects the finding of a steeper error slope for high contrast targets than for low contrast targets. There were no other significant main effects or interactions (all ps > .10). A positive correlation between RT and errors (r = .682) can be taken as evidence against a speed-accuracy tradeoff. — Insert Table 4 about here Discussion In Experiment 1 we replicated the results of past studies in which search times for new targets were faster than for old targets of equal contrast (Jonides & Yantis, 1988; Yantis & Jonides, 1984). This result held even when the contrast of the targets to the background was reduced from .25 to .03. This suggests that new objects are assigned attentional priority in search. Target identification was influenced by the contrast of the targets. High contrast targets were identified faster than low contrast targets. This is consistent with past research indicating that reductions in visual quality result in a general slowing in detection or discrimination times (Duncan & Humphreys, 1989; Palmer, 1995; Pashler, 1987). Further evidence that reductions in contrast had an impact on the visibility of targets in Experiment 1 comes from a near significant trend for identification of old target letters to be slowed more by the onset of high contrast distractors than low contrast ATTENTIONAL CAPTURE 18 distractors. The visual quality manipulation used in the present study (contrast reduction) thus impaired identification of both old and new letters. The effect of this manipulation was strongest for old letters. New letters, on the other hand, maintained priority in search despite the reductions in item contrast. Which of the two current attentional capture hypotheses, the new features hypothesis, or the new objects hypothesis, provides the best account of the results of Experiment 1 ? Recall that the number and strength of features determines search priority according to the proponents of the new features hypothesis (e.g., Gellately, Cole & Blurton, 1998; Theeuwes, 1994; Thomas & Luck, 1998). In other words, the most visible items enjoy the highest priority. Reductions in feature strength (e.g., item contrast) should, therefore, result in an observable search priority for high contrast targets compared to low contrast targets, even when the latter are new targets. In Experiment 1 A, contrast of items was reduced from .25 to .09. Even with such a large reduction in feature strength, however, search times for new low contrast targets tended to be faster than for old low contrast targets, and old high contrast targets. That is, in the indirect competition between new low contrast targets and old high contrast items, the former won. The predictions from the new features hypothesis are less than a perfect match to the present data. A closer match was observed with the results obtained from Experiment IB. Old high contrast targets resulted in faster search times than new low contrast targets. Keep in mind, however, that at this point in the experiment item contrasts were as low as .03. At such low contrasts, there was most likely an increase in difficulty of target identification, and of the detection of new items. Thus not only would discrimination of items from their background and from other items be difficult, but ATTENTIONAL CAPTURE 19 detecting a new item in order to assign it search priority would also be more difficult. In other words, although the results from Experiment IB correspond with the predictions of the new features hypothesis, there are alternative accounts of the data that must also be considered. According to proponents of the new objects hypothesis, search priority is assigned on the basis of newness (e.g., Jonides & Yantis, 1988; Yantis & Jonides, 1984). In contrast to predictions derived from the new features hypothesis, the main prediction that follows from the new objects hypothesis is that new objects will be attended to prior to old objects. The results from Experiment 1 support this prediction. Even when feature strength (i.e., item contrast) was drastically reduced, search for new items tended to be faster than for old items of the same contrast. As predicted, search times to new low contrast targets tended to be faster than to both old high contrast targets, and old low contrast targets (Experiment 1 A). When the contrast of new targets was very low (.03), however, search was faster for old high contrast targets (Experiment IB). As mentioned earlier, however, this somewhat contradictory result can likely be attributed to reductions in identification and detection of targets at very low contrasts, and thus a reduction in the ability to assign search priority to new items. When just new and old low contrast items are compared, the predicted observable RT advantage for new targets is present. The new objects hypothesis provides a better account of the results from Experiment 1 than its alternative, the new features hypothesis. More specifically, the results from Experiment 1 suggest that although feature strength is important for new objects to capture attention, it is still not as important as newness. This idea was further explored in Experiment 2. ATTENTIONAL CAPTURE 20 As noted earlier, a convenient way to summarize the results from this first experiment is to express the relationship between RT slopes for old and new targets as a function of item contrast. Such a summary is shown in Figure 6 where search efficiency is defined as the ratio of old and new RT slopes for each item contrast value. A search efficiency ratio of 1.0 would indicate that there were no differences between the RT slopes for old and new items. A search efficiency value less than 1.0 would indicate shallower slopes for old items compared to new items, while a value greater than 1.0 would indicate the reverse. The first three points on the graph, associated with the lower end of the item contrast scale, are derived from the present experiment. The remaining point at the higher end of the scale is a search efficiency ratio that has been taken from an experiment by Enns, Austen, Di Lollo, Rauschenberger, & Yantis (1999) in which a higher range of luminance values was used. As is evident from Figure 6, an increase in item contrast leads to an increase in search efficiency. Even at the lowest contrast (i.e., .03), there is a trend for search to be more efficient for new targets than it is for old targets. Insert Figure 6 about here Experiment 2: Luminance Changes In the second experiment, we evaluated attentional capture for a variety of luminance changes, in the absence of a new object. The design of this experiment was similar to that of Experiment 1, with two exceptions. One, there were no new objects present in the search display. That is, all objects were old, formed by dropping line segments from preceding figure eights. Two, during the transition from the preview display of figure eights to the search display of letters, one figure eight changed in ATTENTIONAL CAPTURE 21 luminance. Once again, this change occurred independently from the location of the target letter. Using a wider range of item contrasts than in Experiment 1 (see Figure 2) we sought first to determine the effects of luminance changes of various sizes. With an experimental design that included varying levels of luminance values and contrasts, a number of different transitions from figure eight to letter were possible. This meant that the contribution of each of several features of a luminance change to the setting of search priority could be evaluated. The four levels of luminance used in Experiment 2 are represented in Figure 2. Each luminance value has been assigned a label; A, B, C, or D which will be used to describe the feature changes. One, magnitude of the luminance change (referring to the absolute size of the luminance change) was examined. In reference to Figure 2, the set of testable magnitudes included small changes (e.g., A to B), medium changes (e.g., A to C) and large changes (e.g., A to D). Two, polarity reversals (referring to a change in contrast sign) were also examined (e.g., B to C). Three, the importance of the direction of the change (referring to whether the luminance value increased (e.g., B to A) or decreased (e.g., A to B) from the transition of figure eight to letter) was evaluated. Finally, it was also possible to examine search efficiency for particular combinations of feature changes, and then to use the search efficiency measure established for new objects (Experiment 1) to make direct comparisons between search priority for the feature change compared to search priority for new objects. Methods Participants: Fifteen observers were selected and tested in the same way as in Experiment 1. ATTENTIONAL CAPTURE 22 Apparatus: Apparatus for this experiment was identical to that used in Experiment 1. Stimuli: Stimuli used were the same as those used in Experiment 1 with two exceptions; a different range of luminance values was used, and all of the elements were old (i.e., all were uncamouflaged from preceding figure eights). A photometer was used to measure the luminance of the elements and the background. The following luminance readings have been averaged across several recordings. The background for the display was the same medium gray used in Experiment 1A (17.68 cd/m ). Figure eights and letters were one of four luminance values: white (54.7 cd/m ), light gray (27.1 cd/m ), dark gray (10.15 cd/m2) or black (6.9 cd/m2). Design: The design of this experiment was similar to that of Experiment 1. Exceptions are noted below. The search task required observers to indicate with a key press as quickly as possible whether an 'E' or an 'S' was present in the display. On each trial, one of the letters in the search display appeared in a luminance value that was different from that of the figure eight that preceded it. Location and luminance of the item that underwent a change varied randomly across trials with the constraint that changes occurred equally often in each location, and for each luminance value. Overall, six different luminance changes were possible (12 transitions in total when direction of change is taken into consideration). As mentioned earlier, luminance changes could be subdivided into three categories, each of which will be examined in turn. One classification of luminance change was based on the magnitude of the change. The class of small changes included ATTENTIONAL CAPTURE 23 transitions from A to B, B to A, B to C, C to B, C to D, and D to C (refer to Figure 2). Medium changes included transitions from A to C, C to A, B to D, and D to B. Large changes were defined as transitions from A to D, and D to A. Note that magnitude changes are in reference to the absolute size of the change in luminance. When the magnitude of the luminance change is expressed in units relative to the background, the change becomes one of contrast. Contrast changes in Experiment 2 ranged from relatively small changes (.06) to large changes (.24). Another classification of luminance changes was based on whether the transition involved a polarity reversal. Polarity reversals are presently defined as a change in luminance that crosses the intermediate luminance value of the background. A transition from B to C, for example, involves a change from a luminance value above that of the background to a luminance value below that of the background, and by definition a change in polarity. Transitions such as A to B, or B to A, on the other hand, involve a change wherein the luminance value of both the initial and final element is above the background luminance value, and thus no polarity change is involved. A third classification of luminance change was based on whether the luminance value of the letter increased or decreased with respect to the luminance value of the figure eight that preceded it. A transition from A to B, for instance, involved a decrease in luminance, whereas a transition from B to A involved an increase in luminance. As in Experiment 1, the "changed" letter was the target letter on only 1/n trials (where n refers to display size), in other words, the luminance change offered no information about the location of the target letter. Procedure: The procedure was identical to that of Experiment 1. ATTENTIONAL CAPTURE 24 Results As can be seen in Figure 7, luminance changes in the absence of a new object were not very successful in capturing attention. An overall 2 within, 0 between repeated measures ANOVA was computed using Display Size (4 and 8) and Target Type (change and no change) as factors, and RT as the dependent variable. The analysis revealed significant main effects of Target Type (F (1, 14)=10.89, p<-01) and Display Size (F (1, 14)=102.51, p<.0001). The Target Type x Display Size interaction was not significant, F (1, 14)=4.13, p > .05. This analysis revealed that the RT slope for changing targets (23 ms/item) was not significantly different from that of no change targets (30 ms/item). This preliminary analysis revealed that a luminance change in the absence of a new object is not sufficient to capture attention to the extent that 'new' targets have been found to capture attention (as in Experiment 1). Insert Figure 7 about here The same analysis as above was computed using accuracy as the dependent variable. There were no significant main effects or interactions (all p's > .08). Errors accounted for less than 3% of all responses (see Table 5). Insert Table 5 about here A multiple regression analysis was used to assess the contributions of (1) magnitude of the luminance change, (2) polarity reversals, and (3) direction of the luminance change, to the search efficiency ratios (see Table 6) computed for each of the twelve transitions. Using the combination of all three variables, the prediction of search efficiency ratios was marginally significant (R = .757, p<.07). Together they accounted for 57.4% of the total variation in the search efficiency ratios. The partial correlation ATTENTIONAL CAPTURE 25 between polarity reversals and search efficiency ratios (holding magnitude and direction constant) was significant (R = .65, p=.04), and the partial correlation between the direction of the change and search efficiency ratios (holding polarity and magnitude constant) was marginally significant (R = -.61, p=.064). The remaining partial correlation between size and search efficiency ratios (holding polarity reversals and direction of change constant) was not significant (R = -.40, p=.258). Insert Table 6 about here Search efficiency ratios were computed for particular types of luminance changes and combinations of changes. These ratios were plotted in the context of search efficiency ratios for new objects, using changes in contrast as a common metric (see Figure 8a-8d). Figure 8a represents the search efficiency ratios for small changes (B to C, and C to B) and large changes (A to D, D to A) that included a polarity reversal. The ratios were computed based on these selective transitions so that only magnitude of the change was free to vary. Figure 8b and 8c represent search efficiency ratios that involve a polarity reversal (B to C, and C to B) with minimal contrast changes, and contrast changes with no polarity reversal (A to B, B to A, C to D, and D to C), respectively. Magnitude of the luminance change was held constant across Figures 8b and 8c, that is, only small luminance changes were included. Figure 8d represents the search efficiency ratio for luminance changes that had both a contrast change and a polarity reversal (A to C, C to A, B to D, and D to B). Discussion The results from this second experiment suggest that although luminance changes may have a small effect on RTs to the target letter, they do not capture attention as ATTENTIONAL CAPTURE 26 efficiently as new objects. A further breakdown of luminance changes into specific types of changes, however, helped to clarify the specific features of luminance changes that were the best predictors of search efficiency ratios. The results showed that polarity reversal best predicted search efficiency ratios, followed by the direction of the luminance change. Magnitude of the luminance change, however, was not a good predictor. As mentioned earlier, the search efficiency ratios corresponding to four particular combinations of luminance changes were examined with reference to the search efficiency ratios computed for new objects in Experiment 1 (see Figure 8). As shown in Figure 8a, increasing the magnitude of the luminance change did not significantly improve search for the target, and search efficiency was much less than for new items of similar contrast. Figure 8b and 8c show that polarity reversals were more effective for target search than contrast changes in the absence of a polarity reversal (recall that the magnitude of the change was held constant across 8b and 8c). Search for a target that reversed in polarity, however, was only as efficient as search for a new object at very low contrasts. Figure 8d, however, illustrates that a polarity change, together with a contrast change (medium luminance change) was as efficient as search for a new object of medium contrast. Insert Figure 8 about here Two possible reasons why a simultaneous contrast and polarity change result in efficient search are as follows: One, such a change involves a combination of feature changes, thus supporting the notion that the number and strength of features are important for attentional capture. This possibility seems unlikely given that other combinations of ATTENTIONAL CAPTURE 27 changes were unsuccessful in capturing attention (e.g., a simultaneous magnitude and contrast change). In addition, the finding that new objects capture attention much more effectively than luminance changes of twice the magnitude, contradicts the idea that strength of a feature change contributes to the setting of search priority. Thus neither number or strength of features accurately predicts the differences in search benefits for new objects and feature changes. Two, it is possible that this combination of changes, a contrast change together with a polarity change, is a special combination of features that is taken by the visual system to signal the presence of a new object. In the natural visual environment, for instance, such a combination of feature changes cannot occur from changes in lighting or from shadowing, but rather is only likely to occur in conjunction with the appearance of a new object. Thus, it is suggested here that a concurrent contrast and polarity change signals the presence of a new object to the visual system, so that even under artificial conditions, such as those used here, such a change will capture attention as efficiently as a new object. General Discussion The results of Experiment 1 together with those of Experiment 2, suggest that new objects are special to our visual systems, and further, that any combination of feature changes that could be interpreted as signaling the presence of a new object will also result in efficient search. These results are more consistent with the new objects hypothesis (Jonides & Yantis, 1988;Yantis & Hillstrom, 1994;Yantis & Jones, 1991; Yantis & Jonides, 1984) than they are with the new features hypothesis (Gellately, Cole & Blurton, 1998; Theeuwes, 1994; Thomas & Luck, 1998). Based on the new features hypothesis, we would have expected large improvements in search benefit with an increase in the ATTENTIONAL CAPTURE 28 number and/or strength of features. This was not the case. Even when the magnitude of the luminance change, and the size of the contrast change associated with an old target were greater than those associated with a new target, search efficiency ratios were higher for the new target (see Figure 8a, 8c). Strength of target features, therefore, did not seem to play any special role in setting search priority. Rather, decreasing item contrast resulted in an expected increase in RT (Duncan & Humphreys, 1989; Palmer, 1995; Pashler, 1987). In addition, only one combination of luminance change features was found to be important in the setting of search priority, not absolute number of features. Search efficiency for a combined contrast change and small luminance change, for instance, did not equal search efficiency for the combination of a similar luminance change with a polarity reversal and no contrast change. That is, rather than the absolute number of features being important, it appears as though a particular combination of features is important. The only time that search efficiency for old targets was equal to that of new targets was when they involved a medium-sized luminance change, a sizable contrast change, and a polarity reversal. The most important factor in this combination, in terms of search benefits, is the polarity reversals. This weighting of polarity reversals is supported by results from the multiple regression analysis that suggest that polarity reversals are the most important component for predicting search efficiency ratios (among the factors we tested). The importance of polarity reversals is further supported by a direct comparison between search efficiency for polarity reversals (Figure 8b), and contrast changes (Figure 8c), when magnitude of the luminance change was held constant. Search efficiency for the former was higher than it was for the latter. Once ATTENTIONAL CAPTURE 29 again, these results reinforce the idea that the number of features that change may not be as important as which features it is that are changing. Polarity reversals carry with them important object-relevant information. For instance, information derived from polarity reversals can be used in texture segregation (Beck, Sutter, & Ivry, 1987), and visual grouping (e.g., Gilchrist, Humphreys, Riddoch, & Neumann, 1997). Beck, Sutter and Ivry (1987) demonstrated a compelling interaction between polarity reversals and contrast through texture manipulations. The results they obtained were similar to those reported here. Using two or more regions of texture that differed in the relations between light and dark elements (e.g., vertical vs. diagonal organization by color), they found that in the absence of a polarity reversal between regions, a large contrast difference was required for texture segregation (see Figures 9b and 9c). In contrast, when a polarity reversal between regions was present, a much smaller contrast difference was necessary (see Figure 9a). Aks and Enns (1992) reported a similar interaction between polarity reversals and contrast. They found faster search times for a 180° rotated target among distractors when there was a contrast-polarity reversal with respect to the background, compared to when there was no such difference. The authors suggested that interactions between polarity and contrast are important not only for spatial organization but also for segmentation of a visual array. There was evidence in the present study to support a search bias towards decreases in luminance compared to increases in luminance. Search times for targets that decreased in luminance were faster than for increases. A possible explanation for this result arises from evidence that within the visual system, there is a division in labor between ganglion cells that respond to brightness (e.g., on-center, off-surround), and ATTENTIONAL CAPTURE 30 those that respond to darkness (e.g., off-center, on-surround). Experimental evidence for separate brightness and darkness systems is provided by adaptation studies, for instance, which reveal that the visual system can be adapted separately to black stimuli and to white stimuli (Burton, Nagshineh, & Ruddock, 1977; DeValois, 1977). Given such evidence of a separate dark and light system, it is possible that one system is more sensitive to changes in luminance than the other. The position taken here, that new objects are of greater importance than the number or strength of features, is consistent with current object-based theories of attention (e.g., Duncan, 1984; Kanwisher & Driver, 1992). Using a simple, yet elegant research design, Baylis and Driver (1993) reported that visual discriminations were easier when they involved only one object rather than two. Evidence for this two-object cost was obtained even when all other factors were equal in the one and two object conditions, including the discrimination that had to be made. This was possible by manipulating the perceptual set of observers in a figure-ground design. That is, they manipulated which part of the stimulus the observer was to view as the figure and which was the ground. Results like the two-object cost observed by Baylis and Driver have been used to emphasize the importance of objects to attention. Further evidence that the perception of objects is important in attention comes from the developmental literature. Rose, Jankowski, and Senior (1997), for instance, reported that infants as young as 12 months can recognize old objects even when those objects have been presented in a visually impoverished or degraded format, but not when these stimuli are scrambled. These results suggest that infants may be able to fill in gaps ATTENTIONAL CAPTURE 31 when object completion is possible, and further, that there might be an innate predisposition to assemble visual stimuli into objects. It was suggested in the opening of this paper that if the methodology used here for comparing new objects with luminance changes was successful, it could be used to reevaluate other kinds of feature changes (e.g., motion) in the context of new objects. Hillstrom and Yantis (1994), for instance, reported that although motion could be used to direct attention when it was predictive of the target location, it only captured attention when it segregated an object from its background, thus giving the impression of a new object. Thomas and Luck (1998), on the other hand, reported that motion singletons captured attention (Exp. 3), and further that they did so equally well to new objects. Such discrepancies in conclusions with how motion and new objects compare in capturing attention might be resolved if we could first evaluate the saliency of motion, in much the same way that new objects and luminance changes were equated in the present study. One way to do this would be to first manipulate the visibility of motion by means of increasing the correlation between the direction and speed of the moving dots defining the figure. In other words, by manipulating the motion coherence of the dots, visibility of the moving figure can be increased (more motion coherence) or decreased (less motion coherence). Using the search efficiency standard for new objects (Experiment 1), it would then be easy to evaluate the amount of motion coherence needed for search efficiency of motion to equal the search efficiency of a new object at some contrast level. That is, a direct comparison would be possible between the contributions of new objects and motion to the setting of attentional priority. ATTENTIONAL CAPTURE 32 Table 1 Proportion of errors for new and old targets in Experiment la Target High Contrast Low Contrast Display Size Display Size 4 8 4 8 New .006 .028 .026 .009 Old .020 .022 .024 .023 ATTENTIONAL CAPTURE 33 Table 2 Proportion of errors for old targets only in Experiment la Target Onset High Contrast Onset Low Contrast Display Size Display Size 4 8 4 8 High Contrast .024 .019 .018 .024 Low Contrast .027 .031 .016 .015 ATTENTIONAL CAPTURE 34 Table 3 Proportion of errors for new and old targets in Experiment lb Target High Contrast Low Contrast Display Size Display Size 4 8 4 8 New .012 .040 .039 .055 Old .008 .025 .038 .032 ATTENTIONAL CAPTURE 35 Table 4 Proportion of errors for old targets only in Experiment lb Target Onset High Contrast Onset Low Contrast Display Size Display Size 4 8 4 8 High Contrast 1)08 1)27 MS .023 Low Contrast .042 .040 .030 .025 ATTENTIONAL CAPTURE 36 Table 5 Proportion of errors for targets in Experiment 2 Target Luminance Change No Change Display Size 4 1)22 70T9 8 .013 .024 ATTENTIONAL CAPTURE Table 6 Search efficiency ratios for figure eight to letter transitions in Experiment 2 Transition Search Efficiency Ratio A to B JS B to A 1.39 BtoC 15.28 CtoB 1.28 C to D .26 D to C .59 AtoC 11.37 CtoA 3.10 B to D 3.42 D to B 2.11 A to D 6.02 D to A 1.73 ATTENTIONAL CAPTURE 38 Figure Captions Figure 1: Diagram depicting how different areas of the visual system are activated by the presence of object features and how this activation contributes to the making of a saliency map. The lower portion of the diagram represents the stimulus sequence used in the experiment while the upper portion is a schematic representation of two pathways in the visual system where activation is expected to occur. Each fdled square represents activation. Increased levels of activation are indicated by higher spatial frequencies within the squares. Figure 2: The four item contrast and luminance values used in each Experiment. Item contrast is given in Michelson units. Luminance values are expressed in cd/m and are shown above the respective bars. Figure 3: Example of stimulus sequence in Experiment 1. Figure 4: Mean correct RT for new and old targets in Experiment la as a function of target contrast, and display size (left panel). Mean correct RT for old targets only as a function of target contrast, onset contrast, and display size (right panel). Search slopes (in ms/item) are indicated next to each line. Figure 5: Mean correct RT for new and old targets in Experiment lb as a function of target contrast, and display size (left panel). Mean correct RT for old targets only as a function of target contrast, onset contrast, and display size (right panel). Figure 6: Search efficiency ratios for targets in Experiment 1 as a function of item contrast. Figure 7: Mean correct RT for targets in Experiment 2 as a function of change type, and display size. ATTENTIONAL CAPTURE 39 Figure 8: Search efficiency ratios for targets in Experiment 2 and Experiment 1. (A) Search efficiency for magnitude of luminance change. (B) Search efficiency for polarity change. (C) Search efficiency for contrast change (D) Search efficiency for a simultaneous polarity and contrast change. Figure 9: Example of the importance of polarity reversals in texture segregation, taken from Beck, Sutter and Ivry (1987). (A) Polarity reversals lead to spontaneous texture segregation. (B and C) Absence of polarity reversal results in more difficult texture segregation. ATTENTIONAL CAPTURE Figure 1 40 ATTENTIONAL CAPTURE Figure 2 41 Item Contrast (Michelson) o o i—i 1—. to K3 L>J LA LA o LA O LA o LA o LA o 1 1 1... 1 1 1 | 1 | 1 ATTENTIONAL CAPTURE Figure 3 42 U U E H + P B H • U B B + B B • B Display (until response) Preview (1000 ms) ATTENTIONAL CAPTURE Figure 4 43 ATTENTIONAL CAPTURE Figure 5 44 Mean correct RT in ms Lh CT\ ~ J 00 \Q 0 | _ i U\ LA U\ LSi LS, LSI LSx <? Q . q> . <? P Q . Q ATTENTIONAL CAPTURE Figure 6 45 0 .2 .4 .6 Item Contrast (Michelson) ATTENTIONAL CAPTURE Figure 7 ATTENTIONAL CAPTURE Figure 8 Search Efficiency Ratio 4^ o V CO o tr s» £3 OQ CD CD 3 Search Efficiency Ratio 4^ a> ATTENTIONAL CAPTURE Figure 9 4 8 • m * » m * • m m a m • m • a m a a m a • * m a m a m a a • •m • a m • a m n m * • * a • a • m m * • m m 9 m a • a a • a • m m • • a n B a a • a a • • • a w • a • a m a • a a a * • • m • a • * • a m a * a a • • • a • w • * m m • a a • • i • m • m • * • m n * • * a m a * a s • • a m m • » a a ••-a 9 • • • a • a • m a a • • • a a • m • a • * • m • m • m a a a • a • a • m • m • m • m • a 1 • a a a a B a a 4 * ; V • a a a c a 4 a 3 a a <J • - a s a 9 a 1 a a a « A • a a a a B a i a i a «; B lit 1 a a a a 8 a « a <i a a it * • a a a a a a B t a 3 a a a a 8 a a a a • a • a a s • « £ a X a 9 a a a a a « a ra a * a •« a a a a a a » a a a rf Tl a •3 a • a •5 a J a ii • • * • a a a a s a a 4 • a a t a a a 3 a a ATTENTIONAL CAPTURE 49 References Aks, J.D., & Enns, J.T. (1992). Visual search for direction of shading is influenced by apparent depth. Perception & Psychophysics. 52, 63-74. Baylis, G.C., & Driver, J. (1993). Visual attention and objects: Evidence for hierarchical coding of location. Journal of Experimental Psychology: Human Perception and Performance. 19. 451-470. Beck, J., Sutter, A., & Ivry, R. (1987). Spatial frequency channels and perceptual grouping in texture segregation. Computer Vision. Graphics, and Image Processing, 37. 299-325. Burton, G.J., Nagshineh, S., & Ruddock, K.H. (1977). Processing by the human visual system of the light and dark contrast components of the retinal image. Biological Cybernetics. 27. 189-197. DeValois, K.K. (1977). Independence of black and white: Phase specific adaptation. Vision Research. 17. 209-215. Duncan, J. (1984). Selective attention and the organization of visual information. Journal of Experimental Psychology: General, 113, 501-517. Duncan, J. & Humphreys, G. (1989). 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Psychological Science. 9. 379-385. Thomas, S.J., & Luck, S.J. (1998). Multiple pathways to the automatic capture of attention. Manuscript submitted for publication. Todd, S., & Kramer, A.F. (1994). Attentional misguidance in visual search. Perception & Psychophysics. 56. 198-210. Warner, C.B., Juola, J.F., & Koshino, H. (1990). Voluntary allocation versus automatic capture of visual attention. Perception & Psychophysics, 48,243-251. Yantis, S. (1993). Stimulus-driven attentional capture. Current Directions in Psychological Science. 2. 156-161. ATTENTIONAL CAPTURE 52 Yantis, S., & Hillstrom, A.P. (1994). Stimulus-driven attentional capture: Evidence from equiluminant visual objects. Journal of Experimental Psychology: Human Perception and Performance, 20, 95-107. Yantis, S. & Jones, E. (1991). Mechanisms of attentional selection: Temporally modulated priority tags. Perception & Psychophysics, 50, 166-178. Yantis, S. & Jonides, J. (1984). Abrupt visual onsets and selective attention: Evidence from visual search. Journal of Experimental Psychology: Human Perception and Performance, 10, 601 -621. 

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