Atten Percept Psychophys (2014) 76:49–63 DOI 10.3758/s13414-013-0575-1

The influence of cognitive load on spatial search performance Kate A. Longstaffe & Bruce M. Hood & Iain D. Gilchrist

Published online: 30 October 2013 # Psychonomic Society, Inc. 2013

Abstract During search, executive function enables individuals to direct attention to potential targets, remember locations visited, and inhibit distracting information. In the present study, we investigated these executive processes in large-scale search. In our tasks, participants searched a room containing an array of illuminated locations embedded in the floor. The participants’ task was to press the switches at the illuminated locations on the floor so as to locate a target that changed color when pressed. The perceptual salience of the search locations was manipulated by having some locations flashing and some static. Participants were more likely to search at flashing locations, even when they were explicitly informed that the target was equally likely to be at any location. In large-scale search, attention was captured by the perceptual salience of the flashing lights, leading to a bias to explore these targets. Despite this failure of inhibition, participants were able to restrict returns to previously visited locations, a measure of spatial memory performance. Participants were more able to inhibit exploration to flashing locations when they were not required to remember which locations had previously been visited. A concurrent digit-span memory task further disrupted inhibition during search, as did a concurrent auditory attention task. These experiments extend a load theory of attention to large-scale search, which relies on egocentric representations of space. High cognitive load on working memory leads to increased distractor interference, providing evidence for distinct roles for the executive subprocesses of memory and inhibition during large-scale search.

K. A. Longstaffe (*) : B. M. Hood : I. D. Gilchrist School of Experimental Psychology, University of Bristol, 12a Priory Rd, Clifton, Bristol BS8 1TU, UK e-mail: [email protected]

Keywords Search . Executive function . Inhibition . Attention . Memory

Introduction Visual search has been the focus of a sustained research effort for over 30 years. The vast majority of studies in this area have focused on what we will call small-scale search. In a typical laboratory study in this tradition, a target is presented on a computer screen along with a number of distractor items, and the participant’s task is to find the target. This type of task had led to a number of important insights and well-developed theories (e.g., Treisman & Gelade, 1980; Wolfe, 1994). These computer-based laboratory tasks may be a good model for everyday small-scale search tasks, such as finding a file on the desktop of a computer screen, but the extent to which they will be useful in understanding larger-scale search tasks, such as finding a product on a supermarket shelf or locating a car in the parking lot, remains unclear. Despite this, very little research has focused on visual search beyond the small scale. Over the last 10 years, we have conducted a program of work investigating large-scale search and its similarities and differences with more traditional, small-scale search (Smith, Hood, & Gilchrist, 2005, 2008, 2010; Pellicano, Smith, Cristino, Hood, Briscoe and Gilchrist, 2011). The present series of experiments is part of that program, focusing on the role of visual salience in large-scale search. In small-scale visual search, salient items tend to attract, or even capture, attention (Theeuwes, 1994; Yantis & Jonides, 1984), and this can impair performance on the primary task (Ludwig & Gilchrist, 2002). Further work has shown that this attentional capture, which typically involved a saccadic eye movement to the salient item, can be modulated by top-down control (e.g., Ludwig & Gilchrist, 2002, 2003). As a result, the effect of visual salience on search has been used as a test bed

50

for the extent to which top-down or bottom-up control dominates in search: a central topic for the study of visual search, and of cognition more generally. There are good reasons to think that saliency may function differently in large-scale than in small-scale search. For smallscale search, it has been proposed that saliency is encoded within the oculomotor system, which suggests that salience is coded in retinotopic coordinates (Fecteau & Munoz, 2006; Itti & Koch, 2000). In large-scale search, the head and body are constantly moving, and as a result, the visual input is constantly disrupted by head movements or body rotation in space. These movements create visual transients in a retinotopic representation, and visual transients are stimuli that are known to be particularly salient (Theeuwes, 1994). As a result, a retinotopic representation would be full of salient events, which would lead to high levels of interference in large-scale search. One possibility is that large-scale search does not rely on retinotopic representations at all. Retinotopic representations of space are less useful to guide the process of finding the target in large-scale search because they are disrupted by head and body movements. In contrast, allocentric representation gives a stable framework for coding where a participant has been in a search (A. D. Smith, Hood, & Gilchrist, 2005, 2008, 2010). If distraction by salience were encoded in retinotopic coordinates, it would be less disruptive in large-scale search, because large-scale search has to rely on egocentric or allocentric representations of the search space (A. D. Smith et al., 2010). Large-scale search will depend on the use of landmarks for orientation and navigation in the search space (Miller & Carlson, 2011). In this context, salient features might become even more important in large-scale search as distinct landmark anchors. The suggestion that salient landmarks may play an important role in larger-scale tasks has come from single-unit recordings in the hippocampus (O’Keefe, 1976; O’Keefe & Nadel, 1978). In addition, hippocampal cell firing patterns during movement suggest that key elements, including saliency, may be encoded differently in stationary 2-D versus large-scale 3-D search (e.g., Burgess, Barry, & O’Keefe, 2007; Igloi, Doeller, Berthoz, Rondi-Reig, & Burgess, 2010). In this context, salient items become crucial potential landmarks to build robust representations of the environment. It would appear that this process would be unique to largescale rather than small-scale search. The series of experiments reported here were motivated by the central role of salience in theories of small-scale visual search and the potential reasons to suspect that salience may play a different role in small- and large-scale search. To characterize the effect of salience in large-scale search we investigated the effect of top-down processes—working memory and concurred task demands—on the effect of salience on large-scale search.

Atten Percept Psychophys (2014) 76:49–63

In our large-scale search laboratory, search is conducted in a room that has an array of light-emitting diode (LED) illuminated switches embedded in the floor. Participants search the array by activating the switch at each location to discover a target switch that changes color when pressed. In this set of experiments, we manipulated saliency by flashing half of the potential target locations to examine whether this visual event would capture attention, and we measured this by looking for a bias to press flashing over nonflashing locations. To study the top-down modulation of this saliency effect, we systematically manipulated task demands that required different components of memory and attention. Studies from Spelke and colleagues (Lee & Spelke, 2008; Wang & Spelke, 2000) have indicated that the mechanisms guiding navigation are conserved across species, and proposed that humans’ sophisticated search strategies arose via an ability to hold more elements within working memory (Cheng & Newcombe, 2005; Spelke, Lee, & Izard, 2010). To analyze the effect of working memory (WM) during large-scale search, in Experiments 2a and 2b we examined the impact on the saliency effect of reducing the memory load on participants in the large-scale search task. We predicted that this manipulation would reduce the tendency to press the more salient visual targets because of reduced memory load. Conversely, we predicted that increasing memory load would lead to less capacity to inhibit responses to visually salient events. In Experiment 3a, we examined the effect of additional, nonvisual memory tasks on participant responses to static and flashing target locations. Participants completed the large-scale search task while simultaneously remembering a sequence of digits during each trial. This paradigm was adapted from smallscale visual search studies investigating cognitive load (e.g., de Fockert & Bremner, 2011). Following Lavie’s load theory of attention (Lavie, 2005, 2010; Lavie, Hirst, de Fockert, & Viding, 2004; Macdonald & Lavie, 2008), we predicted that high load on processes of cognitive control, such as WM, would increase distractor interference. In Experiment 3a we used a digit span memory task in conjunction with the largescale search task, to investigate the application of this load theory of attention to large-scale search. Work investigating memory load has considered the interaction between memory and attention (Cowan, 2001, 2011). Small-scale visual search studies have investigated memory load in a variety of paradigms; a popular one includes the addition of an auditory stimulus (de Fockert & Bremner, 2011; Shams, Kamitani, & Shimojo, 2000, 2002). These findings have shown that an auditory attentional load can influence visual processing. Work by Shams and colleagues has shown this effect to be robust, even with visually salient stimuli such as flashing lights (Shams et al., 2002). In Experiment 4, we used a concurrent tone detection task to test whether an auditory dual task would also affect the tendency to respond to the locations with flashing lights in the foraging task.

Atten Percept Psychophys (2014) 76:49–63

51

Large-scale search differs from small-scale search because it may rely on different representations and processes to optimize performance. Salience is a factor in spatial representations and affects processing landmarks. The experimental series that we present here examines how saliency may influence the cognitive mechanisms of attention and memory during a large-scale search task.

General method Apparatus

Fig. 2 Close up of a search location

The apparatus for the present experiments had been used in two previously reported studies (Pellicano et al., 2011; A. D. Smith et al., 2010). The large-scale search laboratory space is a 4 × 4 m room, with 49 possible target locations embedded in the floor. These locations consist of a circular stainless steel button (2.5-cm diameter) surrounded by a ring of (6-cm diameter) of light-emitting diodes (LEDs), as can be seen in Figs. 1 and 2. The target locations are arranged in a “honeycomb” pattern forming a concentric octagonal structure, with side length 1.14 m and total area 6.28 m2. The center of each light is 38 cm from the center of the light directly to the left or right of it, and 50 cm from the nearest light in a circular trajectory. From the starting position, a participant of average height (1.7 m) would be 2.8 m from the farthest target, viewing it at an angle of approximately 31.2 deg. The closest potential target would be 0.38 m away, at a viewing angle of 12.6 deg. Depending on head and body orientation, at times not all potential search locations will be within the viewing angle. Because of the scale of the room, it is necessary for participants to walk through the room in order to examine the possible search locations. The remaining floor is carpeted in a featureless grayscale pattern.

In the present series of experiments, we manipulated the salience of potential target locations by including both static (always on) and flashing LED lights. Flashing lights alternate at slightly different frequencies so that the entire display does not flash at the same time. Flashing lights alternate at less than 2 Hz to avoid any possible risk of epileptic seizure (Bancaud et al., 1981). The room is lit by four dimmable ceiling lights, which are obscured by semiopaque material. To eliminate obvious landmarks, a circular curtain rail with dark blue featureless curtains is hung without apparent breaks. During testing sessions, the entrance to the room was sealed with curtains so that all sides of the room appeared identical to participants. Throughout testing, the experimenter was seated in the adjoining room, which contained the computers that powered and controlled the stimulus array. A closed-circuit television camera was used to observe participants. The location and timing of each buttonpress was recorded.

Fig. 1 Individual searching within the large-scale search laboratory

Design In each trial, 20 of the possible 49 search locations were illuminated. The 20 random locations were selected in order to avoid the presence of any strong possible spatial pattern or landmarks to guide search, and in addition the task would become too physically exhausting if all locations are lit. Each of the 20 search locations contained a switch surrounded by a ring of green illuminated LEDs. Two blocks of 40 trials were presented. Locations were randomly assigned in Block 1 and then replicated across the vertical axis of the room in Block 2 to eliminate any effect of location bias. The order of the two possible arrays was counterbalanced across participants. Figure 3 illustrates the experimental array for Block 1. Targets were discovered by depressing the switch in the center of the ring of green LEDs. The LEDs at the location containing the target turned from green to red when the switch was depressed. If a nonilluminated location was pressed, the location remained unlit. Participants began each trial at a start

52

Atten Percept Psychophys (2014) 76:49–63

Start Location

Analysis Two main dependent variables were analyzed. First, the mean number of buttonpresses per trial to flashing buttons was compared to the mean number of buttonpresses per trial to static buttons. Second, we analyzed the number of revisits to previously examined locations within each trial. Unlike in conventional visual search studies, reaction times were not analyzed as a dependent variable in this paradigm. Reaction times were dependent on the speed at which the participant navigated the room by moving from location to location; this measure, then, would be more predictive of physical fitness than search efficiency. Evaluating the number of buttons pressed allowed us a more accurate analysis of search optimality, which, due to the physical exertion required in this task, was defined as the path that minimized path length.

Fig. 3 Sample large-scale search room light array

Experiment 1 position defined by an orange LED on the edge of the room, as is indicated in Fig. 3. Once the start light had been pressed, participants walked through the room, pressing other green illuminated locations until they located the target. Each trial featured only one target. Participants were given up to 2 min to search on each trial. No participant in Experiments 1–3 reached this maximum search time. Two of the participants were excluded from Experiment 4 for reaching this maximum time on 20 consecutive trials. The total testing time was approximately 45 min for each participant.

In this experiment, we examined how participants’ search behavior would be affected by the salience of search locations in large-scale search. We predicted that their attention would be captured by salient flashing targets and that they would explore these locations more frequently. Targets were equally distributed in the flashing and static search locations, and participants were explicitly informed that the target was equally likely to be found at any search location. Method

Procedure The experimenter explained the task to participants in the laboratory. They were required to stand facing the display and press the switch at the orange starting location to begin each trial. Participants were told that the aim of the experiment was to analyze how they searched for the target, that a target was always present in the display, and that they should walk through the room at their own pace, pressing the switches at the green illuminated locations to locate the target. All participants were explicitly told that the target was equally likely to be at a flashing or a nonflashing location. The experimenter then left the room and sealed the curtain before the participant commenced the first trial. Participants were aware that all trials were observed via a CCTV camera in order for the experimenter to trigger the next trial. Individuals explored the illuminated locations by pressing the respective switches, until they located the target. Following detection of the target, the starting position was again illuminated, and the next trial began. After the first block of 40 trials, participants exited the laboratory for a 5-min break before completing Block 2. At the end of the experiment, all participants were fully debriefed.

A group of 20 undergraduates (ten female, ten male) from the University of Bristol participated for course credit. All of the participants were physically able to move at ease within the search space, and they were instructed to examine search locations using only their dominant hand (17 right-handed and three left-handed). Individuals were between 18 and 25 years of age (mean age = 19.72, SD = 1.25). Participants pressed switches at the illuminated green locations in order to locate a target that turned red when pressed. Upon completing the search task, all participants completed the Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999). Results Participants’ mean buttonpresses per trial were significantly greater to flashing lights (M = 5.71, SD = 1.24) than to static lights (M = 4.78, SD = 1.11), F (1, 18) = 91.53, p < .001, η 2p = .828. Inspection of the individual participant behavior revealed that this difference was present in all participants. An example path of a typical participant’s route through the room on one particular trial can be seen in Fig. 4.

Atten Percept Psychophys (2014) 76:49–63

Fig. 4 Sample pathway through room for one participant. Bright green represents flashing locations, and darker green static locations

To analyze any changes in participant behavior over time, the 80 trials were split into four quartiles—that is, the first 20 trials in Quartile 1, the second 20 trials in Quartile 2, and so forth. The results were analyzed in a repeated measures 2 (buttonpresses) × 4 (quartiles) analysis of variance (ANOVA). We found no interaction between quartiles for buttonpresses, F (1, 3) = 1.25, p = .298. This result is illustrated in Fig. 5. This lack of interaction showed no significant effect of time on buttonpresses—that is, no evidence that participants learned to modify their behavior across time. To ensure that the results did not arise from a predisposition to press flashing lights at the beginning of each trial, we examined participant behavior in the first 25 % of buttonpresses in each trial. The mean buttonpresses in the first 25 % of each trial were significantly greater to flashing (M = 1.18, SD = 0.61) than to static (M = 0.67 SD = 0.38) lights, F(1, 18) = 53.42, p = < .001, η 2p = .738. To compare buttonpresses within a trial, the first 25 % of buttonpresses were compared to the

53

remaining 75 % in each trial. A repeated measures ANOVA revealed no interaction, F(1, 18) < 1, p = .63, η 2p = .012, indicating that participant behavior in the first portion of each trial was comparable to behavior throughout the experiment. The mean revisits per trial were relatively low (M = 0.29, SD = 1.28), reflecting only M = 3.17 % (SD = 3.07) of the mean total buttonpresses. Mean revisits per trial occurred significantly more often to flashing than to static lights, F (1, 18) = 6.07, p = .023, η 2p = .242. We found no main effect of gender on mean buttonpresses to flashing and static lights, F(1, 18) = 0.94, p = .337, and also no main effect of gender on total revisits made, t(1, 18) = 1.67, p = .075. Likewise, no main effect of WASI scores was apparent on the mean buttonpresses per trial to flashing or to static lights, F(1, 18) = 1.27, p = .275, and no correlation between WASI total score and total revisits made, r(19) = .28, p = .240.

Discussion Participants were more likely to search at flashing locations, even when explicitly informed that the target was equally likely to be located at any location. Our interpretation is that participants’ attention was captured by the perceptual salience of flashing lights, leading to a bias to explore these targets. These results mirror findings from visual search literature, which have shown that individuals automatically attend, and move their eyes to a salient, but task irrelevant visual event (Ludwig & Gilchrist, 2002; Theewes, 1994; Yantis & Jonides, 1984). Studies have shown that both abrupt onset and increased luminance capturing attention via reflective saccades whereas changes in other dimensions, such as color, do not (Irwin, Colcombe, Kramer, & Hahn, 2000). In a largescale but less experimentally defined setting than this present study, Rosetti and colleagues found that 9- to 11-year-olds in an Easter egg search task on a soccer field were influenced by

Fig. 5 Mean buttonpresses per person per trial to flashing and static search locations for each quartile in Experiment 1. Error bars represent corrected standard errors

54

varying conspicuity of the targets (Rosetti, Pacheco-Cobos, Larralde, & Hudson, 2010). Participant reaction to salient targets is preserved from visual search to large-scale search settings, implying that similar mechanisms of attention and inhibition may be operating in both searches. This is further examined in Experiments 2, 3, and 4. Although search optimality can be measured in many ways, the most energy efficient strategy would be to parallel the Euclidean-Travelling Salesman Problem (E-TSP), which involves selecting a defined shortest distance route, and pressing all the buttons along this optimal route, avoiding revisits until the target is found (MacGregor & Ormerod, 1996). Studies have shown humans are able to find reliable optimal solutions to the E-TSP when planning routes with less than 60 locations (van Rooij, Schactman, Kadlec, & Stege, 2006). Utilizing this optimal search strategy, participants would find the target location at an average of ten inspections of the 20 possible search locations, which is close to the average total buttonpresses of 11.15. Furthermore, participants would equally distribute their examinations between flashing and static locations, as each potential location along the path is equally likely to be either flashing or static. An example of the distribution of flashing and static targets is reflected in Fig. 4. This model of optimality would minimize path length, thus minimizing effort exerted. Virtual reality studies have shown limiting effort is a key element of human search behavior (Ruddle & Lessels, 2006). Given the effort involved in large-scale search, it is of note that participants did not modulate their search behavior over 80 successive trials in which the target was equally distributed between flashing and static locations. In the present study, participants were relatively efficient at monitoring locations previously visited as reflected in the very low revisit frequencies. However, they had a strong tendency to explore the salient locations more frequently. Effort has significant consequences if participants were to adopt a search strategy of preferentially pressing all flashing lights. Since the flashing lights were equally and randomly distributed throughout the room on each trial, this search strategy would require passing throughout the room twice on many trials. The mean buttonpresses (11.15) reflected approximately half of the 20 possible search locations, indicating that participants did not adopt this strategy. The analysis of the first 25 % of trials showed us that participants pressed both flashing and static lights in the first 25 % of trials, which helped rule out the possibility that the main effect was derived from an a priori strategy to press flashing lights first. Rather, participants seem to have proceeded throughout the room on a somewhat optimal path, but then became distracted by flashing lights and deviated from the shortest possible path length. Since an optimal search would limit effort and limit path length, it would equally distribute buttonpresses between static and flashing lights. The analysis of quartiles showed us that

Atten Percept Psychophys (2014) 76:49–63

participants did not learn to equally distribute search between flashing and static lights along an optimal path length. An inability to do this may represent a failure of the allocation of attention (e.g., Kane, Bleckley, Conway, & Engle, 2001; Yantis & Jonides, 1990). Revisits to locations previously explored represent a failure of spatial WM. Participants’ low level of total revisits indicates that their spatial WM was, in general, functioning well in monitoring previously examined locations. This disparity in successful search strategy may indicate that inhibition and spatial WM function separately during large-scale search. To check that performance on our search task was not simply a reflection of general intelligence, participants completed the WASI after the large-scale search task, as an individual measure of general cognitive ability. However, neither participants’ tendency for increased exploration of salient flashing locations nor their revisit rate was significantly correlated to total WASI score. These results indicated that general intelligence may not be indicative of spatial WM or perceptual inhibition on our large-scale search task. This may be because the WASI focuses on verbal and perceptual IQ, distinct from the “visuospatial sketchpad” (Baddeley, 2002). Many limitations affected the use of the WASI in this experiment, including sample size and time restrictions. For these reasons, WASI performance was not examined in the subsequent experiments here. We were interested to see any broad correlational affects between domain-general executive function and performance on this task, and will pursue this in future work, perhaps with tasks such as a stop-signal or Stroop task, which may better correlate with inhibition, or with tasks such as a number–letter discrimination task, which may correlate with attention shifting (Friedman et al., 2006). Work by Friedman and Miyake has suggested that the WAIS-III yields a measure of general intelligence and WM, but that it may not correlate with measures of inhibition or attention shifting (Friedman et al., 2006). Given participants’ low level of revisits and high level of inhibitory failure, it is possible that they were directing executive resources to monitor previously visited locations and to avoid effortful revisits. This may have left them with fewer executive resources to control their perceptual attention, and thus could account for the increased exploration of flashing lights. To determine whether an interaction between spatial WM and perceptual attention was driving the results of Experiment 1, in Experiment 2a participants were no longer required to monitor previously visited locations.

Experiment 2a Following the finding that participants were, overall, able to monitor previously examined locations, Experiment 2a was designed to assess participants’ sensitivity to flashing lights

Atten Percept Psychophys (2014) 76:49–63

when remembering locations previously visited was no longer necessary. In this experiment, once participants had searched a possible target location, the light extinguished, eliminating the possibility of revisits to previously examined locations. Targets were dispersed equally under flashing and static lights, and participants were explicitly informed that the target was equally likely to be found at any illuminated search location. Method A group of 20 undergraduates (17 female, three male) from the University of Bristol participated for course credit. All participants were physically able to move at ease within the search space, and were instructed to examine search locations using only their dominant hand (16 right-handed and four lefthanded). Individuals were between 18 and 20 years of age (mean age = 19.72, SD = 0.89). Participants searched illuminated green locations for the target light, which turned red when the switch was activated. The target was equally likely to be at any illuminated location, and participants were explicitly told this. In this experiment, when participants pressed the switch of a light that was not the target, the light extinguished and was no longer a possible search location. Results Participants’ mean buttonpresses per trial were significantly greater to flashing (M = 5.59, SD = 1.00) than to static (M = 5.18, SD = 0.92) lights, F (1, 18) = 37.37, p < .001, η 2p = .663. A 2 (lights) × 4 (quartiles) repeated measures ANOVA revealed no interaction between quartiles for types of buttonpresses, F(1, 3) = 1.395, p = .254. A 2 (buttonpresses) × 2 (experiments) ANOVA revealed a significant interaction between Experiment 1 and Experiment 2a, F(1, 39) = 5.13, p < .001, η 2p = .333. This interaction indicated a change between participants’ preferences for flashing lights between Experiments 1 and 2. These results suggest that participants explore flashing locations significantly less when they are not required to remember previous locations. Discussion When participants were no longer required to remember previously visited locations, they explored flashing and static locations more equally. Eliminating the need to monitor locations previously visited removed a memory load from the experiment, effectively simplifying the task. Participants were subsequently more able to inhibit the salient flashing lights and to explore the flashing and static locations more equally. This indicates that reducing the memory load affects the level of attention toward salient targets during large-scale search.

55

Experiment 2b In Experiment 2a, nontarget search locations extinguished once they were pressed. As these nontarget locations extinguished, the set size was reduced. To verify that this reducing set size did not account for the reduced tendency to press flashing locations, in Experiment 2b we examined participants’ bias to salient flashing lights in an experiment with blocks in which the set size was 6, 12, 18, or 24 search locations. Method A group of 20 undergraduates (15 female, five male) from the University of Bristol participated for course credit. All participants were physically able to move at ease within the search space and were instructed to examine search locations using only their dominant hand (18 right-handed and two lefthanded). Individuals were between 18 and 25 years of age (mean age = 21.32, SD = 2.15). The four possible blocks had 6, 12, 18, or 24 search locations per block, each containing 24 trials. The order of blocks was counterbalanced across participants. As in Experiments 1 and 2a, the locations of flashing and static lights were equally distributed in each trial, and flashing locations were randomly allocated across trials, to eliminate location effects. After activating the start switch, participants activated the switches at subsequent illuminated locations until they found the target, which turned red when pressed. Nontarget lights remained illuminated during the search. Although the set size varied, the search locations remained spread out across the same area of the room. Results An overall significant difference emerged between the number of buttonpresses to flashing and nonflashing lights, F (1, 19) = 113.45, p < .001, η 2p = .577. In a 2 (buttonpresses) × 4 (set size) ANOVA, we examined any effect of set size on buttonpresses and found no significant interaction, F (1, 3) = 2.54, p = .063, η 2p = .091. To further examine differences in search behavior within the different set sizes, the slopes of mean buttonpresses to flashing and static lights across different set sizes were analyzed. We observed no interaction of set size with buttonpresses to flashing and static lights, F(1, 3) = 1.003, p = .329, η 2p = .050. The slopes for the mean buttonpresses to flashing and to static lights were 3.25 buttonpresses/set size and 3.13 buttonpresses/set size, respectively. A similar analysis of the intercepts of mean buttonpresses to flashing and static lights revealed a significant main effect, F(1, 19) = 36.548 p < .001, η 2p = .658. These results indicate a main effect of differences between the overall buttonpresses to flashing and static lights,

56

but that this effect was not modulated by an interaction with set size. These results are illustrated in Fig. 6. Discussion We found no significant difference in participants’ preferences for flashing lights or revisit rates when the set size was reduced. This indicates that the attentional capture of the flashing lights was not contingent on set size, and therefore that the reducing set size in Experiment 2a, as lights extinguished, did not explain the results. In Experiment 2a, when lights extinguished and participants no longer needed to remember previous locations, a significant effect was apparent on the ability to inhibit salient targets during search. The magnitude of the flashing effect was decreased when participants no longer had to monitor previously visited locations, but was not decreased when the task was made simpler in another way, by reducing set size (Exp. 2b). The results of Experiment 2b therefore reinforce the idea that the reduced memory load of not remembering previously visited locations is what drives the effect of reduced attentional capture by flashing lights. Experiment 2a illustrates that reducing memory load reduces explorations to salient flashing lights. In Experiment 3a, we added a concurrent memory task, to examine the effect of an additional memory load on search behavior.

Experiment 3a In Experiment 3a, we examined the effect of a concurrent memory load on spatial WM and inhibition during search. Participants completed the same task as in Experiment 1, but

Atten Percept Psychophys (2014) 76:49–63

they were also required to remember a five-digit number during each trial. This paradigm was adapted from visual search studies investigating attention load, in which participants judged the lengths of lines while remembering varying digit spans (de Fockert & Bremner, 2011). The load theory of attention predicts that high load on processes of cognitive control, such as WM, leads to increased distractor interference (Lavie, Hirst, de Fockert & Viding, 2004). Method A group of 20 undergraduates (14 female, six male) from the University of Bristol participated for course credit. All participants were physically able to move at ease within the search space and were instructed to examine search locations using only their dominant hand (16 right-handed and four lefthanded). Individuals were between 18 and 20 years of age (mean age = 19.5, SD = 0.66). When participants were positioned opposite the start location and were ready to commence the trial, the experimenter announced a five-digit number for the participant to remember. Once participants located the target of each trial, they repeated the number back to the experimenter. Errors were defined in three ways. An order error occurred when all five digits were correct, but two or more digits were in an incorrect sequence. A substitution error involved replacing one digit with a digit that was not otherwise present in the sequence. A complete error involved replacing two or more digits with an incorrect digit that was not otherwise present in the sequence. As in previous experiments, targets were equally distributed throughout the flashing and static targets, and participants were explicitly informed of this.

Fig. 6 Mean buttonpresses per person per trial to flashing and static search locations for each set size in Experiment 2b. Error bars represent corrected standard errors.

Atten Percept Psychophys (2014) 76:49–63

Results Participants’ mean buttonpresses per trial were significantly greater to flashing lights (M = 6.15, SD = 0.98) than to static lights (M = 4.76, SD = 0.78), F(1, 18) = 294.44, p < .001, η 2p = .939. As before, we found no interaction effect of quartile for buttonpresses, F (1, 3) < 1, p = .551. To investigate the effect of memory load on performance, we compared Experiments 1–3. The mean buttonpresses per trial to flashing and static locations were analyzed in a 2 (buttonpresses) × 2 (experiment) ANOVA. We observed an interaction of experiment with mean buttonpresses—that is, a greater difference between mean buttonpresses to flashing lights in Experiment 3a than in Experiment 1: F (1, 39) = 9.78, p = .003, η 2p = .205. Overall, participants made very few memory errors, averaging 93 % accuracy, although accuracy in remembering the five digits was widely variable across participants, with a range of 0–17 total errors. Participants’ mean total error for all 80 trials was 5.81 (SD = 4.73). The total errors per participant across all 80 trials were further defined as order errors (M = 2.25, SD = 2.05), substitution errors (M = 1.75, SD = 1.77), or complete errors (M = 1.81, SD = 2.74). Since the data were variable and some individuals made no errors, a median split was done on the mean total memory errors in order to divide participants into two groups of high and low total memory errors. To analyze any effect of memory errors on buttonpresses, the mean buttonpresses to flashing and static lights across these two groups were analyzed in a repeated measures 2 (lights) × 2 (median split memory errors) ANOVA. We found no main effect of memory errors for mean buttonpresses to flashing lights, F < 1. Participants’ mean revisits per trial (M = 1.17, SD = 1.24) were increased from Experiment 1, accounting here for M = 9.28 % of the total buttonpresses, as compared to M = 3.17 % in the previous experiment. Participants again made significantly more revisits to flashing than to static locations, F(1, 18) = 13.59, p = .002, η 2p = .417. Due to the high level of variability in the data, including trials with no revisits, a Mann–Whitney U test was conducted between the mean revisit rates in Experiments 3 and 1. The test showed a significant difference: U(19) = 67, p < .001, z = –3.599, r = –.56. The mean rank for Experiment 1 was 13.9, whereas the mean rank for Experiment 3a was 27.2. Discussion Participants made significantly more revisits in Experiment 3, during which they maintained a concurrent memory load, than in Experiment 1. Monitoring previously visited locations requires visuospatial WM, so this modulating effect of a WM load was expected. More interestingly, participants’ tendency to explore salient flashing locations also increased when they

57

were searching with a concurrent memory load. This effect may be explained using Lavie’s perceptual load model of attention, in which cognitive control is needed to actively distinguish between targets and distractors (de Fockert, Rees, Frith, & Lavie, 2001). The model predicts that reducing the amount of executive control available to the task by loading WM will result in greater distractor processing (Lavie, 2005; Macdonald & Lavie, 2008). This may explain why participants attended preferentially to salient flashing targets during this task, and were ultimately distracted by the flashing locations and unable to conduct the most efficient search route possible. In a similar finding, de Fockert and Bremner (2011) showed that a concurrent digit memory task mediated levels of inattention blindness in a visual attention task. De Fockert and Bremner argued that WM is needed to direct a taskappropriate focus of attention. Therefore, when WM is consumed by a concurrent memory task, allocation of selective attention is disrupted. Although, ideally, a strictly visuospatial WM task would be utilized, it was not possible to concurrently conduct a standard spatial memory task, such as Corsi blocks or letter rotation, while a participant was searching within the room. The digit span task has been used concurrently in visual search on computer screen tasks, and in a confirmatory factor analysis, long- and short-term WM tasks have been shown to equally load executive function (Miyake, Friedman, Rettinger, Shah, & Hegarty, 2001). In this experiment, participants’ ability to equally direct attention between potential targets was impeded when their WM was occupied by concurrently maintaining a five-digit number. Comparison of individual differences did not show a significant relationship between memory errors and performance on this task. This may be due to the low rate of memory errors throughout. Studies have indicated that an auditory task can influence performance on a visual task, indicating that the auditory task may take cognitive precedence (Shams et al., 2002). This may explain why performance in optimal searching was impeded here but participants made very few memory errors. This disruption of attention allocation has been seen in other investigations of human sensorimotor control (Kingstone, Smilek, & Eastwood, 2008). Work in visual perception has shown that interference from another sensory modality disrupts allocation of visual attention (Macdonald & Lavie, 2011; Shams et al., 2002), so in a final experiment, we would examine search behavior while participants concurrently completed a task that required attention to an auditory secondary task.

Experiment 3b So far, we have used the results of Experiment 1 twice in across-experiment comparisons. To mitigate the risk of a

58

Type I error, we ran a further experiment that was an exact replication of Experiment 1. Results and discussion Participants’ mean buttonpresses per trial were significantly greater to flashing lights (M = 5.91, SD = 1.08) than to static lights (M = 4.67, SD = 0.75), F(1, 18) = 133.826, p < .001, η 2p = .876. As in Experiment 1, inspection of the individualparticipant behavior revealed that this difference was present in all participants. The means in this experiment were close to those in Experiment 1: flashing (5.71 vs. 5.91) and static (4.78 vs. 4.67), with a smaller difference between conditions overall in Experiment 1 than in this replication (0.93 vs. 1.24). However, a 2 (buttonpresses) × 2 (experiments) repeated measures ANOVA comparing flashing and static buttonpresses in Experiments 1 and 3b showed no interaction of buttonpresses and experiment, F(1, 39) = 2.54, p = .063, η 2p = .091. A 2 (buttonpresses) × 2 (experiments) repeated measures ANOVA comparing flashing and static buttonpresses in Experiments 2a and 3b did reveal a significant interaction, F(1, 39) = 67.89, p < .001, η 2p = .641. This same 2 × 2 ANOVA for Experiments 3a and 3b also showed a significant interaction, F(1, 39) = 8.27, p = .007, η 2p = .179. The difference between responses at flashing rather than static lights was somewhat larger in this replication than in Experiment 1, although this effect was not reliable. The comparisons of this experiment to Experiments 2a and 3a provide additional support for our previous conclusions.

Experiment 4 Following Experiment 3a, in which the level of cognitive load was manipulated by adding a concurrent memory task, in this experiment we added a concurrent attention task. Participants wore wireless headphones playing a soundtrack of 1000-Hz tones, and they were required to detect the occurrence of intermittent 1200-Hz tones while simultaneously completing the large-scale search task as in Experiment 1. Visual search studies have shown that visual attention can be altered by other sensory modalities, particularly pure tone auditory input (Shams et al., 2002). In Experiment 4, we aimed to determine whether allocating attention to a pitch difference in pure tones would influence the attention directed to salient potential targets in this large-scale search task. Method A group of 22 undergraduates (12 female, ten male) from the University of Bristol participated for course credit. Two participants were excluded because they were unable to

Atten Percept Psychophys (2014) 76:49–63

complete the task within the time limit. All participants were physically able to move at ease within the search space and were instructed to examine search locations using only their dominant hand (17 right-handed and three lefthanded). Individuals were between 18 and 24 years of age (mean age = 19.63, SD = 1.35). As in all previous experiments, the participants were explicitly informed that the target was equally likely to be at a flashing or a static location. Participants wore wireless headphones throughout the task, through which a consistent soundtrack of 300-ms, 1000-Hz pure tones was played. The tones had an average duration of 300 ms and were played at 1s intervals. At intervals varying from 6 to 9 s, the 1000-Hz tone was replaced by a 300-ms, 1200-Hz pure tone. Variations of this paradigm have been used widely throughout the auditory attention literature, since seminal work by Scharf, Quigley, Aoki, Peachey, and Reeves (1987). Participants were instructed to respond orally, indicating “tone change” when they perceived this change in tone. They otherwise completed the search task as in Experiment 1. Results Participants’ mean buttonpresses per trial once again were significantly greater to flashing (M = 6.32, SD = 0.98) than to static (M = 5.12, SD = 0.88) lights, F(1, 19) = 166.501, p < .001, η 2p = .898. We found no interaction between quartiles for buttonpresses, F (3, 17) = 0.708, p = .551. Participants were overall extremely accurate in reporting the tone difference, averaging 99 % accuracy. The mean buttonpresses to flashing and static lights across Experiments 1 and 4 were analyzed in a repeated measures 2 (buttonpresses) × 2 (experiments) ANOVA. An interaction was apparent between experiments, F (1, 39) = 4.39, p = .043, η 2p = .104. That is, across experiments the mean differences between the amounts of flashing and static lights pressed were significantly different, as is illustrated in Fig. 7. This ANOVA was then repeated, to compare Experiment 4 with the additional control in Experiment 3b, which also yielded a significant interaction, F (1, 39) = 11.41, p = .002, η 2p = .231. The participants’ total mean revisit rate was increased from Experiment 1 (3.17 %) and Experiment 3a (9.28 %), accounting for M = 11.57 % of the total buttonpresses. Participants made significantly more revisits to flashing than to static locations, F(1, 18) = 11.02, p = .004, η 2p = .367. Since the data were variable and there were many cases of zero revisits, nonparametric statistics were appropriate. A Mann–Whitney U test was conducted between mean revisits in Experiments 4 and 1. The test showed a significant difference, U (19) = 23, p < .001, z = –4.79, p < .001, r = –.76. The mean rank for Experiment 1 was 11.65, whereas the mean rank for Experiment 4 was 29.35.

Atten Percept Psychophys (2014) 76:49–63

59

Fig. 7 Mean buttonpresses per person per trial to flashing and static search locations for Experiments 1–4. Error bars represent corrected standard errors.

Discussion Participants explored flashing locations significantly more in Experiment 4, when they were searching for the target while concurrently performing to a tone detection task, than in Experiment 1, in which no concurrent task was performed. This finding again illustrates an effect of additional cognitive load on the direction of attention to salient events during largescale search. The participants in Experiment 4, who had an additional attentional load, responded to salient flashing target locations significantly more, although the target was equally likely to be at any flashing or static location. The participants in Experiment 4 also made significantly more revisits to previously examined locations than did those in Experiment 1, indicating that concurrently attending to a tone attention task influenced participants’ ability to remember locations previously visited. Studies have shown that when attention is occupied with search, participants are less able to ignore auditory distractors during a visual search task (Boot, Brockmole, & Simons, 2005). Evidence that a nonvisual task can modulate attention capture is consistent with recent evidence that the difficulty of a visual search task interacts with the processing of auditory distractors (Tellinghuisen & Nowak, 2003). If attending to an auditory task is defined as a perceptual load, studies have shown that perceptual load affects visual search (Santangelo, Finoia, Raffone, Belardinelli, & Spence, 2008). With regard to a load model, de Fockert, Rees, Frith, and Lavie (2001) found that a concurrent auditory task did not influence visual search, and suggested that attentional capacity was modality specific. However, in the de Fockert et al. study, the auditory task involved verbal WM. Studies have shown that verbal and nonverbal distractors influence

selective attention to different degrees (Dittrich & Stahl, 2012). The tone discrimination task used in Experiment 4 was based on purer acoustic stimuli that do not have the same processing constraints as speech, such as the onsets of consonants that words or numbers have. Participants were more accurate in attending to tone detection than in attending equally to salient versus nonsalient targets. This may suggest that the attention task posed less difficulty for the participants, or perhaps that the tones played through the headphones more directly captured their attention. Previous audio–visual auditory attention paradigm studies had established a strong effect of auditory input influencing visual attention (Macdonald & Lavie, 2011; Morein-Zamir, SotoFaraco, & Kingstone, 2003; Recanzone, 2003; Shams et al., 2002). The results of this experiment again support a load theory of attention, according to which additional cognitive load leads to greater distractor interference.

General discussion Successful spatial search requires individuals to utilize at least three executive functions: WM, attention, and inhibition, in order to remember previous locations, pay attention to important targets, and ignore salient but irrelevant information. Here we show that participants have a strong tendency to attend to salient events during large-scale search. Participants’ level of attention toward salient lights can be moderated by tasks designed to manipulate cognitive load, such as searching while simultaneously remembering digits. Findings from the present set of experiments extend those from previous studies of small-scale search (e.g., Lavie et al., 2004; Ludwig & Gilchrist, 2002) to large-scale search,

60

in which participants move through space. These results provide valuable additional evidence of how mechanisms of attention and memory function in spatial cognition. When moving through space, retinotopic or egocentric reference frames cannot be relied on, since head and eye movements disrupt the visual input (Foulsham & Kingstone, 2012). As a result, we might expect that salience would not capture attention in the same way in large- and in small-scale visual search. However, here we saw that participants in largescale search responded to salience in a fashion similar to oculomotor capture, suggesting that capture by salience is a ubiquitous property of visual processing. In Experiment 1, we found that participants had a strong bias for the perceptually salient flashing lights. Although the locations of possible search locations were fixed across trials, the locations of flashing lights changed in each trial, so that each location was equally likely to be a flashing or a static light across multiple trials. The bias seen here for participants to preferentially allocate attention to flashing lights was therefore not a location effect. In the past, attention capture effects have been interpreted as evidence for pure bottom-up processing (Theeuwes, 1991, 1992). However, in Experiments 2 and 3 here, WM load modulated the interference effect of the flashing lights, indicating a role of higher cognitive control on attentional capture in this context. This effect may indicate conditional automaticity, which may occur when individuals adopt an attentional set for that feature (Folk, Remington, & Johnston, 1992). Attention and WM have traditionally been regarded as two different constructs: the former selecting relevant information, and the latter remembering the information. Following this, visual search has been regarded as purely an attention task; however, some form of memory representation is required in order to remember the target search item (Wolfe, 1994). However, research by Kane et al. (2004) using antisaccade tasks has shown that visuospatial speed can predict performance on WM tasks, illustrating that visuospatial abilities and WM are not as separate as was initially suggested. Another line of evidence for the integration of the visuospatial domain and the central executive has come from work by Miyake et al. (2001), who proposed that spatial manipulation tasks require executive control to coordinate and monitor goals and subgoals. Latent-variable analysis highlighted spatial visualization and spatial relation tasks as the factors most highly correlated to executive function in a battery of tasks targeting spatial skills, WM, and short-term memory (Miyake et al., 2001). To examine the interactions between WM and large-scale search, in Experiment 2 we reduced the level of memory load, to examine the effect on attention to salient flashing locations. Once participants were not required to monitor previously visited locations, they were much better able to inhibit attention to salient events during search. Conversely, in

Atten Percept Psychophys (2014) 76:49–63

Experiments 3 and 4 participants attended more frequently to salient flashing lights while performing a concurrent attentional or memory task. A memory model would suggest that executive function actively monitors examined locations and employs memory resources to prevent revisits. Therefore, loading executive WM with a digit span WM task competes with memory resources, increasing revisits and leading to a less optimal search. Alternatively, executive function may work to identify objects and efficiently allocate attention (Kane et al., 2001; Peterson, Beck, & Wong, 2008). When executive functioning is engaged in another task, inhibitory mechanisms are unavailable to inhibit queued shifts of attention (Zingale & Kowler, 1987). Evidence from an antisaccade task (Kane, et al., 2001) suggests that executive function controls the disengagement of attention by inhibiting queued shifts of attention. Premature shifts of visual attention during a search task could lead to inadequate processing of items. If executive function is required in order to simultaneously identify both auditory stimuli and visual stimuli in the search task, as in Experiment 3, some items may not be identified. This effect is similar to inattentional blindness (Mack & Rock, 1998). Therefore, during a search task, revisits may occur because individuals fail to identify an item that they have examined, so they return to it. A WM load may lead to more revisits because, if attention is focused on a secondary WM task while a visual search task is completed, participants may examine an item but fail to identify it. Accordingly, revisits may not be due to a memory failure, but rather due to premature shifts of attention. Other evidence has suggested that tasks that load the central executive may influence visual search more than WM load does (Han & Kim, 2004). In these experiments, manipulating information in WM contrasted with simply holding items in WM; that is, dual tasks in which individuals simply had to remember an item produced no effect on search times, but when WM manipulations were required, a significant effect on visual search times emerged. Perhaps the central executive is separately implicated in manipulating spatial WM stimuli (E. E. Smith, Jonides, & Koeppe, 1996). In classic visual search studies, both Boot et al. (2005) and Lavie and de Fockert (2005) demonstrated that cognitive load increases capture induced by a color singleton. Further work examined whether increasing observers’ cognitive load influences the frequency and speed of oculomotor capture during scene viewing. In two experiments investigating the effects of stimulus onset or color change under cognitive load, the degree of oculomotor capture decreased as observers’ cognitive resources were reduced. Oculomotor capture during scene viewing is dependent on observers’ top-down selection mechanisms (Matsukura, Brockmole, Boot, & Henderson, 2011). Oculomotor capture observed during real-world scene viewing is not purely driven by a bottom-up selection

Atten Percept Psychophys (2014) 76:49–63

mechanism so it may not provide example of automatic selection. In a load model of attention, WM load influences selective attention and causes increased distractor interference during large-scale search. Evidence for this was shown in experiments in which WM load led to increased interference from singleton distractors in a visual search task (Olivers, Meijer, & Theeuwes, 2006). This may demonstrate memorydriven attention capture for content-specific representations. Selective attention may be guided by active visual memory representations, introducing a distinction between an active and accessory memory (Olivers, Peters, Houtkamp, & Roelfsema, 2011). It has been proposed that although WM can store roughly four items, observers can actively look for only one at a time, and therefore items in WM may compete for a place in the attentional template (Houtkamp & Roelfsema, 2009). In Experiment 3, individuals had many items competing, since they had to simultaneously locate the red target light, remember locations previously visited, and remember a five-digit number. This would contribute significant cognitive load to interrupt the accessory memory, disrupting selective attention, and thus potentially leading to attention capture by the salient flashing locations. This model of memory load and selective attention may not be incompatible with the proposal that the central executive functions to control a domain-general search process (e.g., Hills, Todd, & Goldstone, 2010). Since participants’ attention toward salient lights can be moderated by memory load, this may indicate a connection across many domains of executive functioning. A similar effect has been seen in studies of verbal fluency with a secondary WM load (Rosen & Engle, 1997), of cue validity (Newell, Rakow, Weston, & Shanks, 2004), and of social memory for acquaintances (Hills & Pachur, 2012). This body of work may indicate that WM span is related to general goal maintenance and long term memory retrieval, thus implicating a domain general search process (Hills & Pachur, 2012). The present experiments provide a new paradigm for the study of WM and attention shifting in a large-scale search task. The importance of moving toward real-world tasks to model navigation and sensorimotor control has been highlighted recently (see Kingstone et al., 2008; see also Humphreys et al., 2010; Ingram & Wolpert, 2011). Accordingly, Kingstone and colleagues examined the role of saliency on visual search in a real-world task in which participants located mailboxes. This work found that bottom-up saliency had little effect on search time, but influenced fixation and gaze coordination (Foulsham & Kingstone, 2012). Without a doubt, carefully controlled behavioral experiments have advanced our understanding of sensorimotor control; however, these tasks have typically been conducted in a lab environment, on a computer screen that requires little exploration of a large-scale space, and

61

subsequently may not be representative of the processes used in everyday life, particularly in navigation. The large-scale search laboratory provides a unique environment to evaluate sensorimotor control in a large scale, more ecologically valid and yet still experimentally defined and controlled setting. Previous work in inhibition during spatial exploration has focused on more conventional visual search. Studies in virtual reality have tended to focus on navigation and map integration, rather than measures of memory or attention during search (e.g., Gillner & Mallot, 1998). A notable exception to this is research by Hayhoe and colleagues, in which they used complex virtual environments such as driving simulations, to experimentally examine observations of complex behavior in natural search settings (Shinoda, Hayhoe, & Shrivastava, 2001). Work in these virtual environments has revealed that individuals selectively extract visual information for momentary task, which may have implications for levels of preattentive processing in real-world environments (Hayhoe et al., 2002). Large-scale search in a naturalistic setting involves many elements that are not captured by these experiments. In the real world, the richness of the environment provides a much greater variation of saliency. Individuals are able to successfully navigate while completing many other tasks, demonstrating that navigation does not dominate the central executive’s resources. The challenge for future studies will be to capture the complexity of these more real-world environments while continuing to have good experimental control.

References Baddeley, A. (2002). Fractionating the central executive. In D. Stuss & R. Knight (Eds.), Principles of frontal lobe function (pp. 246–260). New York, NY: Oxford University Press. Bancaud, J., Henriksen, O., Rubiodonnadieu, F., Seino, M., Dreifuss, F. E., & Penry, J. K. (1981). Proposal for revised clinical and electroencephalographic classification of epileptic seizures. Epilepsia, 22, 489–501. Boot, W. R., Brockmole, J. R., & Simons, D. J. (2005). Attention capture is modulated in dual-task situations. Psychonomic Bulletin & Review, 12, 662–668. doi:10.3758/BF03196755 Burgess, N., Barry, C., & O’Keefe, J. (2007). An oscillatory interference model of grid cell firing. Hippocampus, 17, 801–812. Cheng, K., & Newcombe, N. S. (2005). Is there a geometric module for spatial orientation? Squaring theory and evidence. Psychonomic Bulletin & Reviews, 12, 1–23. doi:10.3758/BF03196346 Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–114. doi:10.1017/S0140525X01003922. disc. 114–185. Cowan, N. (2011). The focus of attention as observed in visual working memory tasks: Making sense of competing claims. Neuropsychologia, 49, 1401–1406. doi:10.1016/j.neuropsychologia. 2011.01.035

62 de Fockert, J. W., & Bremner, A. J. (2011). Release of inattentional blindness by high working memory load: Elucidating the relationship between working memory and selective attention. Cognition, 121, 400–408. doi:10.1016/j.cognition.2011.08.016 de Fockert, J. W., Rees, G., Frith, C. D., & Lavie, N. (2001). The role of working memory in visual selective attention. Science, 291, 1803– 1806. doi:10.1126/science.1056496 Dittrich, K., & Stahl, C. (2012). Selective impairment of auditory selective attention under concurrent cognitive load. Journal of Experimental Psychology: Human Perception and Performance, 38, 618–627. Fecteau, J. H., & Munoz, D. P. (2006). Salience, relevance, and firing: A priority map for target selection. Trends in Cognitive Sciences, 10, 382–390. doi:10.1016/j.tics.2006.06.011 Folk, C. L., Remington, R. W., & Johnston, J. C. (1992). Involuntary covert orienting is contingent on attentional control settings. Journal of Experimental Psychology: Human Perception and Performance, 18, 1030–1044. doi:10.1037/0096-1523.18.4.1030 Foulsham, T., & Kingstone, A. (2012, March). Goal-driven and bottomup gaze in an active real-world search task. Paper presented at the 2012 Symposium on Eye Tracking Research and Applications, Santa Barbara, CA. Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., Defries, J. C., & Hewitt, J. K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17, 172–179. doi:10.1111/j. 1467-9280.2006.01681.x Gillner, S., & Mallot, H. A. (1998). Navigation and acquisition of spatial knowledge in a virtual maze. Journal of Cognitive Neuroscience, 10, 445–463. Han, S. H., & Kim, M. S. (2004). Visual search does not remain efficient when executive working memory is working. Psychological Science, 15, 623–628. Hayhoe, M. M., Ballard, D. H., Triesch, J., Shinoda, H., Aivar, P., & Sullivan, B. (2002, March). Vision in natural and virtual environments. In Proceedings of the 2002 Symposium on Eye Tracking Research and Applications (pp. 7–13). New York, NY: ACM Press. Hills, T. T., & Pachur, T. (2012). Dynamic search and working memory in social recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38, 218–228. doi:10.1037/a0025161 Hills, T. T., Todd, P. M., & Goldstone, R. L. (2010). The central executive as a search process: Priming exploration and exploitation across domains. Journal of Experimental Psychology: General, 139, 590–609. doi:10.1037/a0020666 Houtkamp, R., & Roelfsema, P. R. (2009). Matching of visual input to only one item at any one time. Psychological Research, 73, 317– 326. doi:10.1007/s00426-008-0157-3 Humphreys, G. W., Yoon, E. Y., Kumar, S., Lestou, V., Kitadono, K., Roberts, K. L., & Riddoch, M. J. (2010). The interaction of attention and action: From seeing action to acting on perception. British Journal of Psychology, 101, 185–206. doi:10.1348/000712609x458927 Igloi, K., Doeller, C. F., Berthoz, A., Rondi-Reig, L., & Burgess, N. (2010). Lateralized human hippocampal activity predicts navigation based on sequence or place memory. Proceedings of the National Academy of Sciences, 107, 14466–14471. Ingram, J. N., & Wolpert, D. M. (2011). Naturalistic approaches to sensorimotor control. Progress in Brain Research, 191, 3–29. Irwin, D. E., Colcombe, A. M., Kramer, A. F., & Hahn, S. (2000). Attentional and oculomotor capture by onset, luminance and color singletons. Vision Research, 40, 1443–1458. Itti, L., & Koch, C. (2000). A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40, 1489– 1506. doi:10.1016/S0042-6989(99)00163-7 Kane, M. J., Bleckley, M. K., Conway, A. R. A., & Engle, R. W. (2001). A controlled-attention view of working-memory capacity. Journal

Atten Percept Psychophys (2014) 76:49–63 of Experimental Psychology: General, 130, 169–183. doi:10.1037/ 0096-3445.130.2.169 Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne, T. W., & Engle, R. W. (2004). The generality of working memory capacity: A latent-variable approach to verbal and visuospatial memory span and reasoning. Journal of Experimental Psychology: General, 133, 189–217. doi:10.1037/0096-3445.133.2.189 Kingstone, A., Smilek, D., & Eastwood, J. D. (2008). Cognitive Ethology: A new approach for studying human cognition. British Journal of Psychology, 99, 317–340. doi:10.1348/000712607X251243 Lavie, N. (2005). Distracted and confused? Selective attention under load. Trends in Cognitive Sciences, 9, 75–82. doi:10.1016/j.tics. 2004.12.004 Lavie, N. (2010). Attention, distraction, and cognitive control under load. Current Directions in Psychological Science, 19, 143–148. doi:10. 1177/0963721410370295 Lavie, N., & de Fockert, J. W. (2005). The role of working memory in attentional capture. Psychonomic Bulletin & Review, 12, 669–674. Lavie, N., Hirst, A., de Fockert, J. W., & Viding, E. (2004). Load theory of selective attention and cognitive control. Journal of Experimental Psychology: General, 133, 339–354. doi:10.1037/0096-3445.133. 3.339 Lee, S. A., & Spelke, E. S. (2008). Children’s use of geometry for reorientation. Developmental Science, 11, 743–749. Ludwig, C. J. H., & Gilchrist, I. D. (2002). Measuring saccade curvature: A curve fitting approach. Behavior Research Methods, Instruments, & Computers, 34, 618–624. Ludwig, C. J. H., & Gilchrist, I. D. (2003). Goal-driven modulation of oculomotor capture. Perception & Psychophysics, 65, 1243–1251. doi:10.3758/BF03194849 Macdonald, J. S. P., & Lavie, N. (2008). Load induced blindness. Journal of Experimental Psychology: Human Perception and Performance, 34, 1078–1091. doi:10.1037/0096-1523.34.5.1078 Macdonald, J. S. P., & Lavie, N. (2011). Visual perceptual load induces inattentional deafness. Attention, Perception, & Psychophysics, 73, 1780–1789. doi:10.3758/s13414-011-0144-4 MacGregor, J. N., & Ormerod, T. C. (1996). Human performance on the traveling salesman problem. Perception & Psychophysics, 58, 527–539. Mack, A., & Rock, I. (1998). Inattentional blindness. Cambridge, MA: MIT Press. Matsukura, M., Brockmole, J. R., Boot, W. R., & Henderson, J. M. (2011). Oculomotor capture during real-world scene viewing depends on cognitive load. Vision Research, 51, 546–552. Miller, J., & Carlson, L. (2011). Selecting landmarks in novel environments. Psychonomic Bulletin & Review, 18, 184–191. doi: 10.3758/s13423-010-0038-9 Miyake, A., Friedman, N. P., Rettinger, D. A., Shah, P., & Hegarty, M. (2001). How are visuospatial working memory, executive functioning, and spatial abilities related? A latent-variable analysis. Journal of Experimental Psychology: General, 130, 621–640. doi: 10.1037/0096-3445.130.3.621 Morein-Zamir, S., Soto-Faraco, S., & Kingstone, A. (2003). The capture of vision by audition: Deconstructing temporal ventriloquism. Cognitive Brain Research, 17, 154–163. Newell, B. R., Rakow, T., Weston, N. J., & Shanks, D. R. (2004). Search strategies in decision making: The success of “success. Journal of Behavioral Decision Making, 17, 117–137. O’Keefe, J. (1976). Place units in hippocampus of freely moving rat. Experimental Neurology, 51, 78–109. O’Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map (Vol. 3). Oxford, UK: Oxford University Press, Clarendon Press. Olivers, C. N. L., Meijer, F., & Theeuwes, J. (2006). Feature-based memory-driven attentional capture: Visual working memory content affects visual attention. Journal of Experimental Psychology: Human Perception and Performance, 32, 1243–1265. doi:10. 1037/0096-1523.32.5.1243

Atten Percept Psychophys (2014) 76:49–63 Olivers, C. N. L., Peters, J., Houtcamp, R., & Roelfsema, P. R. (2011). Different states in visual working memory: When it guides attention and when it does not. Trends in Cognitive Sciences, 15, 327–334. doi:10.1016/j.tics.2011.05.004 Pellicano, E., Smith, A. D., Cristino, F., Hood, B. M., Briscoe, J., & Gilchrist, I. D. (2011). Children with autism are neither systematic nor optimal foragers. Proceedings of the National Academy of Sciences, 108, 421–426. doi:10.1073/pnas.1014076108 Peterson, M. S., Beck, M. R., & Wong, J. H. (2008). Were you paying attention to where you looked? The role of executive working memory in visual search. Psychonomic Bulletin & Review, 15, 372–377. doi:10.3758/PBR.15.2.372 Recanzone, G. H. (2003). Auditory influences on visual temporal rate perception. Journal of Neurophysiology, 89, 1078–1093. doi:10. 1152/jn.00706.2002 Rosen, V. M., & Engle, R. W. (1997). The role of working memory capacity in retrieval. Journal of Experimental Psychology: General, 126, 211–227. doi:10.1037/0096-3445.126.3.211 Rosetti, M. F., Pacheco-Cobos, L., Larralde, H., & Hudson, R. (2010). An experimental and theoretical model of children’s search behavior in relation to target conspicuity and spatial distribution. Physica A, 389, 5163–5172. Ruddle, R. A., & Lessels, S. (2006). For efficient navigational search, humans require full physical movement, but not a rich visual scene. Psychological Science, 17, 460–465. Santangelo, V., Finoia, P., Raffone, A., Belardinelli, M. O., & Spence, C. (2008). Perceptual load affects exogenous spatial orienting while working memory load does not. Experimental Brain Research, 184, 371–382. Scharf, B., Quigley, S., Aoki, C., Peachey, N., & Reeves, A. (1987). Focused auditory attention and frequency selectivity. Perception & Psychophysics, 42, 215–223. Shams, L., Kamitani, Y., & Shimojo, S. (2000). Illusions: What you see is what you hear. Nature, 408, 788. doi:10.1038/35048669 Shams, L., Kamitani, Y., & Shimojo, S. (2002). A visual illusion induced by sound. Cognitive Brain Research, 14, 147–152. doi:10.1016/ S0926-6410(02)00069-1 Shinoda, H., Hayhoe, M. M., & Shrivastava, A. (2001). What controls attention in natural environments? Vision Research, 41, 3535–3545. Smith, A. D., Gilchrist, I. D., & Hood, B. M. (2005). Children’s search behavior in large-scale space: Developmental components of exploration. Perception, 34, 1221–1229. Smith, A. D., Hood, B. M., & Gilchrist, I. D. (2008). Visual search and foraging compared in a large-scale search task. Cognitive Processing, 9, 121–126. doi:10.1007/s10339-007-0200-0

63 Smith, A. D., Hood, B. M., & Gilchrist, I. D. (2010). Probabilistic cueing in large-scale environmental search. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 605–618. doi: 10.1037/a0018280 Smith, E. E., Jonides, J., & Koeppe, R. A. (1996). Dissociating verbal and spatial working memory using PET. Cerebral Cortex, 6, 11–20. Spelke, E., Lee, S. A., & Izard, V. (2010). Beyond core knowledge: Natural geometry. Cognitive Science, 34, 863–884. doi:10.1111/j. 1551-6709.2010.01110.x Tellinghuisen, D. J., & Nowak, E. J. (2003). The inability to ignore auditory distractors as a function of visual task perceptual load. Perception & Psychophysics, 65, 817–828. Theeuwes, J. (1991). Exogenous and endogenous control of attention: The effect of visual onsets and offsets. Perception & Psychophysics, 49, 83–90. doi:10.3758/BF03211619 Theeuwes, J. (1992). Perceptual selectivity for color and form. Perception & Psychophysics, 51, 599–606. doi:10.3758/BF03211656 Theeuwes, J. (1994). Stimulus-driven capture and attentional set: Selective search for color and visual abrupt onsets. Journal of Experimental Psychology: Human Perception and Performance, 20, 799–806. doi:10.1037/0096-1523.20.4.799 Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97–136. doi:10.1016/00100285(80)90005-5 van Rooij, I., Schactman, A., Kadlec, H., & Stege, U. (2006). Perceptual or analytical processing? Evidence from children’s and adult’s performance on the Euclidean traveling salesperson problem. Journal of Problem Solving, 1, 44–73. Wang, R. F., & Spelke, E. S. (2000). Updating egocentric representations in human navigation. Cognition, 77, 215–250. Wechsler, D. (1999). Wechsler Abbreviated Scale of Intelligence (WASI). San Antonio, TX: Harcourt Assessment. Wolfe, J. M. (1994). Guided Search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1 , 202–238. doi:10.3758/ BF03200774 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. doi:10.1037/0096-1523.10.5.601 Yantis, S., & Jonides, J. (1990). Abrupt visual onsets and selective attention: Voluntary versus automatic allocation. Journal of Experimental Psychology: Human Perception and Performance, 16, 121–134. doi:10.1037/0096-1523.16.1.121 Zingale, C. M., & Kowler, E. (1987). Planning sequences of saccades. Vision Research, 27, 1327–1341.