The spatial distribution of attention during covert visual orienting

Acta Psychologica North-Holland

225

75 (1990) 225-242

THE SPATIAL DISTRIBUTION OF ATTENTION COVERT VISUAL ORIENTING *

DURING

Peter A. MCCORMICK University of Waterloo, Canada

Raymond KLEIN Dalhousie University, Halifax, Canada Accepted

February

1990

Many studies of covert orienting of visual attention in response to informative pre-cues have focused on the spatial distribution of improved or impaired performance. One can find at least four different models in the literature, each describing a different distribution: the fixed gradient spotlight; the zoom lens spotlight; the hemifield activation hypothesis; and the flexible allocation of resources model. In previous work examining procedural details that might have led to the formulation of the hemifield activation hypothesis, it was postulated (Klein and McCormick 1989) that under conditions of uncertainty about which of two locations to attend, an observer may focus attention on a visual channel (i.e., midlocation placement of a fixed gradient spotlight) that is spatially intermediate. The present experiment was designed to distinguish among the four models of attentional distribution, and to test the midlocation placement strategy. Our findings show support for midlocation placement, demonstrate evidence against flexible allocation and hemifield activation, but could not differentiate between fixed gradient and zoom lens variants of the spotlight model.

Many cognitive studies on visual orienting in response to pre-cues have focussed on the spatial distribution of performance changes (e.g., costs and benefits). Four frameworks describing spatial properties of visual attention have evolved from this literature, which differ in their descriptions of the spatial selectivity of directed visual attention. Several researchers (Crick 1984; Remmington and Pierce 1984; Treisman and Schmidt 1982) have used a spotlight metaphor describing * This research was supported by a grant from the Natural Sciences and Engineering Research Council of Canada to R. Klein. Requests for reprints should be sent to R. Klein, Dept. of Psychology, Dalhousie University, Halifax, Nova Scotia, Canada, B3H 451.

OOOl-6918/90/$03.50

0 1990 - Elsevier Science Publishers

B.V. (North-Holland)

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directed visual attention as a spatially restrictive phenomenon which ‘enhances the efficiency of detection of events within its beam’ (Posner et al. 1980: 172). One important feature of this framework is that subjects cannot attend to non-adjacent locations without attending to all intermediate positions. That is, the metaphorical beam cannot be split. A performance gradient from costs in reaction time (RT) outside the ‘beam’ to benefits in RT within the beam has been observed in several experiments (Downing and Pinker 1985; Shulman et al. 1985; 1986). Some proponents of this framework (e.g., Downing and Pinker 1985; Klein and McCormick 1989) hypothesize that the size and shape of the attended region is not under strategic control by the subject, but instead is a function of retinal eccentricity and the type of discrimination required. This hypothesis will be referred to as the ‘fixed gradient spotlight’ which follows nicely from the notion that subjects are allocating attention to a visuo-spatial channel with fixed properties (see, for example, Ball and Sekuler 1980). In contrast, Eriksen and his colleagues (Eriksen and Hoffman 1972; Eriksen and Rohrbaugh 1970) and LaBerge (1983), while adopting the unified view of attentional distribution, consider that the size of the attended region is under strategic control. Eriksen and St. James (1986), for example, have provided evidence suggesting that the attentional beam can be likened to a zoom lens. That is, the attentional focus is variable and the resources available fixed. This model implies that as the attentional focus is narrowed the attentional resources concentrate on a small area and as the attentional focus is widened the attentional resources diffuse over a wider area. Hughes and Zimba (1985) have proposed that directed visual attention is not so spatially restrictive as a spotlight metaphor might suggest. Their claim is that visual attention is uniformly distributed within the hemifield containing the cued location (henceforth the hemifield activation hypothesis). More recently (Hughes and Zimba 1987), they modified their position and contend that visual attention is evenly distributed within attended quadrants of the visual field. Some researchers shun the notion that attention must be spatially unified (e.g., Egly and Homa 1984; Podgorny and Shepard 1983; Shaw and Shaw 1977) and have proposed a much more flexible system. Within this framework subjects, given sufficient practice, should be able to adjust visual attention to accommodate any configuration of cued and uncued locations. Of course, within this view attending to

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noncontiguous locations without attending to intermediate positions should present no difficulty. ’ The present experiment was designed to evaluate these frameworks by extending a paradigm used by Posner et al. (1980) and modified by Kiefer and Siple (1987). In an attempt to evaluate the possibility of a flexible visual attention system, Kiefer and Siple examined whether or not the attentional focus could be divided between two nonadjacent locations. Their major finding was that subjects could not successfully attend to two cued locations unless they were adjacent. In one experiment, when two cued locations had an uncued location in between them, RT to detect the target at this midlocation was significantly faster than RT to detect the target at either of the cued locations. However, Kiefer and Siple only used four possible target locations (two on each side of fixation) so when the two locations were non-adjacent they were also in opposite hemifields. Because attention falls off sharply from the point of fixation (Downing and Pinker 1985), it is difficult to determine whether or not the midlocation effects in Kiefer and Siple are due to midlocation placement of the attentional focus or some artifact of the steep gradient of attention near fixation. More importantly, in both Posner et al. (1980) and Kiefer and Siple (1987) the inability to ‘split the beam’ may have been due to a difficulty in attending to locations in different directions from fixation. It may be the case that subjects can split the attentional focus if the to-be-attended locations are in the same hemifield (i.e., in the same direction). In the present experiment three locations were used on each side of fixation in order to examine performance at a location midway between two cued locations, all within the same hemifield (see fig. 1). Single location cues indicated the most likely location at which the target could occur. Each location was cued equally often by a single location t Proponents of each framework have usually considered that the facilitation in performance occurs because of sensitivity changes at the visual encoding stage. However, within the flexible framework, Duncan (1980) and Sperling (1984) have suggested that enhanced performance is due to lowered criteria for making responses to stimuli at cued locations. While we prefer the encoding/sensitivity explanation as outlined by Posner et al. (1980), it must be recognized that this issue has not been completely resolved (see, for example, Mtiller and Findlay’s (1987) critique of Bashinski and Bacharach (1980) and the results of Downing (1988) which, on the other hand, may not be subject to some of their criticisms). From the perspective of the present study, however, the encoding versus criterion issue is superfluous to the question at hand which involves the spatial structure of the induced changes in performance (for a more complete discussion on this topic see Shulman et al. 1986: fn. 3).

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of a trial display. The numbers 1.3 in the center indicate to the subject t.hat Fig. 1. Photograph locations 1 and 3 are the most likely locations for the luminance increment. Other cues used were single location cues (1.1, 2.2, . , 6.6) and neutral cues ( + + ). Locations 1 and 6 are referred to as far locations, locations 2 and 5 as midlocations and locations 3 and 4 as near locations.

cue. Double location cues indicated that the target was most likely to occur at one of two locations. The location positioned in the far periphery and the location positioned nearest to the fovea served as double locations and were cued together for the same number of trials as each single location cue. In addition, a neutral cue (indicated by two plus signs) informed the subject that all locations were equally likely to be the target location. The nine cues and corresponding target probabilities are presented in fig. 2. The predictions made by each of the attentional frameworks are presented below. The fixed gradient spotlight hypothesis suggests that within hemifield gradients should occur for each single cued location. That is, RT should be faster when the target occurs at the cued location than when it occurs at another location within the cued hemifield (e.g., Shulman et al. 1986). For the double cue two possibilities exist. First, subjects could alternate between attending the far location on some trials and the near location on other trials. Performance on the (double) cued locations would then be a mixture of (single) cued and uncued perfor-

P.A. McCormick,

R. Klein / Spatial distributions Single

VI

location

229

cues

F]

Double

location

cues

mm

Neutral

10

Fig. 2. Cues and corresponding The shading of a dot represents

cue

Target

Probabilities

Hi;

target probabilities. Each panel represents a different cue type. the probability of the target occurring at that location. These dots did not occur in the actual experiment.

mantes. Because RT to the near and far location under double cuing involves attending to the correct location on half the trials, it should be faster than (single) cuing the near or far location and targeting the corresponding (uncued) far or near location. This latter condition presumably involves attending to only incorrect locations and never the targeted location. For example, in fig. 2 the (double) cue ‘1 . 3’ would result in faster RT at location 1 or 3 than RT to location 3 with the single location cue ‘1 . 1’ or RT to location 1 with the the single location cue ‘3. 3’. The switching strategy would also predict high variance in the RT distributions at the two cued locations because RT would be a mixture of attended and unattended trials. This variance prediction also helps to discriminate a switching strategy from a flexible attention system. Finally, a mixed strategy would result in slower RT to detect the stimulus at the midlocation than when that location had been singly cued. The second possibility for the double cued condition is that subjects could decide to attend to a visuo-spatial channel corresponding to the

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midlocation. Klein and McCormick (1989) have suggested that this strategy, which they call midlocation placement strategy, could have generated the hernifield data of Hughes and Zimba (1985, 1987). Although a midlocation placement strategy has rarely been discussed in the visuo-spatial orienting literature there is good evidence for such a mechanism in the detection of movement. Ball and Sekuler (1980) have demonstrated that in response to uncertainty about two directions of motion an observer appears to attend to a ‘channel’ that is sensitive to motion in a direction that is the vector average of the two cued directions. Klein and McCormick (1989) suggested that a similar strategy applies to the orienting of visual attention when uncertainty about two cued locations (within the same hemifield) occurs. That is, the observer will show a tendency to focus his or her attention on a location that lies midway between them. It should be pointed out that midlocation placement of the attentional focus is not a theory about the spatial distribution of visual attention per se, but a theory of where subjects attend to under conditions of uncertainty. In terms of the fixed gradient hypothesis, a midlocation placement strategy involves shifting a spotlight of fixed size to the midlocation. Under this view, the patterns observed by Downing and Pinker (1985) and Shulman et al. (1985; 1986) characterize the tuning functions for the visuo-spatial channels to which subjects attend. There are three predictions made by midlocation placement strategy in the context of the present experiment. First, like Kiefer and Siple (1987) we would expect to find faster RT to a location between two (double) cued locations than to either of the cued locations. Second, double-cuing two nonadjacent locations should result in a different performance pattern than (single) cuing either of these locations or the average of these two single cue conditions. This result would indicate that subjects were not alternating between attending to one or the other cued location on different trials. Third, if a fixed spotlight is being oriented to the midlocation then the double cue should yield a performance pattern which does not differ from the RT generated by a single cue to the midlocation. The zoom lens model differs only slightly from midlocation placement strategy in its predictions. As with the fixed gradient spotlight, within hemifield priming effects of single cues should occur. However, with the double cues subjects may expand the attentional focus in order to incorporate both cued locations. Thus, a zoom lens that incorporates

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two cued locations is quite likely to be centered at the midlocation. However, unlike the attentional gradient hypothesis a zoom lens would show no preference for the midlocation because attentional resources are thought to be evenly distributed (see Eriksen and St. James 1986: 239) hence all three locations within the cued hemifield would benefit from the double cue. In addition, since the zoom lens is supposedly operating with limited attentional resources, overall benefits should be reduced as resources are spread over more locations. Therefore the difference between cued locations and corresponding locations on the uncued side should be smaller for wider focuses of attention. The hemifield activation hypothesis predicts that for any cue uniform benefits in performance should occur in the cued hemifield and equivalent costs in performance should occur within the uncued hemifield. In addition, Hughes and Zimba (1985) have argued that it is articulation of the visual field (i.e. well marked locations) that mediates attentional gradient effects. The question of marking locations arises because Hughes and Zimba (1985, 1987) did not mark probe locations whereas proponents of the Attentional Gradient hypothesis (Shulman et al. 1985; 1986; Downing and Pinker 1985) used well articulated fields. Klein and McCormick (1989) found no evidence that marking target locations interacts in any way with the costs and benefits of cued versus probe locations. It does not seem unreasonable, though, to consider the possible effects of articulation on whichever framework best describes the results from this experiment, so whether the six locations were marked was implemented as a between-subjects condition. Finally, and as alluded to earlier, a flexible system predicts that performance should be related to the probability of stimulation assigned to the target location with no variation between the equiprobable cued or uncued locations. More specifically, for the double cue condition the midlocation should have a slower RT associated with it than either of the two (double) cued locations and should be no faster than any other uncued location. The present experiment had a slight procedural deviation from usual pre-cuing studies of attentional shifts. The time interval between the onset of the cue and the onset of the target (stimulus onset asynchrony or SOA) is usually held constant for a block. In the present experiment, subjects initiated both the cue and the target. They pressed a thumb switch to initiate the cue and when they felt they had successfully

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oriented their attention they would release the thumb switch, 200 ms later the target occurred. To insure that subjects were not guessing, anticipating or habituating their responses, catch trials were frequently used in which no target appeared. To insure that subjects were not shifting their gaze, eye movements were monitored throughout the experiment. The self-initiated times to shift were recorded for the various cue conditions.

Method Subjects Twenty-two university undergraduate students and one graduate student were paid $5 per hour for their participation. Subjects were randomly assigned to one of two groups: one group had marked target locations, the other group had only the fixation point marked. Both groups were instructed to respond as quickly and as accurately as possible and both groups were discouraged from making anticipatory responses. Subjects were informed of the probabilities of the possible events in a trial before each block. Apparatm The events of the experiment were controlled by a PDP 11/23 computer. A subject was seated facing a Tektronic 604 display oscilloscope with his or her head held steady by a chinrest located 35 cm from the display screen. Horizontal eye movements were monitored by an infrared cornea1 reflection device mounted on eyeglass frames and worn with a supporting strap. A response key was positioned on the table in front of the subject, a second response button was held in the subject’s (non-response) left hand and controlled by his or her thumb. The response button was used to initiate events of a trial and the response key was used for the detection response. Procedure Subjects began each block of trials with an eye position calibration procedure. Only calibrations that allowed detection of movements of at least one degree of visual angle were accepted. After the calibration, subjects were presented with the word ‘READY’ to indicate the computer was ready to begin. The experiment was initiated when the subject pressed the response button held in his or her non-response hand. The events of each trial were similar. Subjects were presented with a dot at fixation. They were instructed to fixate on the dot and then press and hold the thumb button. At this point, the subject was presented with a cue indicating the most likely location(s) for the target. The marked group was also presented with small dots marking each potential

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target location. When subjects had oriented their attention to the cued location(s) they released the button, 200 ms later a target (a larger, brighter dot) appeared at one of the locations. There were three possible target locations on each side of fixation (see figs. 1 and 2). These locations were at eccentricities of 2, 6, and 10 degrees of visual angle and were located on the same horizontal plane as the fixation stimulus. The cue was either two numbers (ranging from 1-6) or two plus signs presented at fixation. Number cues represented locations which were named 1-6 from left to right. In order for subjects to become accustomed to the numbering scheme, their first session involved a practice block in which the locations were labelled by the appropriate number positioned 2 degrees of visual angle below the location. The actual number subtended slightly less than 1 degree of visual angle. There were three types of cue the subject could receive. First, cues to attend a single location were represented by displaying the location number twice at fixation (e.g., ‘2. 2’). Second, there were two double location cues (‘1 .3’ and ‘4. 6’) indicating that attention should be divided evenly between two locations. Regarding double cues subjects were instructed to ‘try to attend to both locations’, and for all cues they were instructed ‘to attend, not by moving your eyes but by using your peripheral vision’, hence there was no suggestion of attending to the midlocation or moving eye fixation. Third, a neutral cue that gave no information about possible target locations (‘ + + ‘) indicated that the subject should divide attention between all possible locations. Catch trials (approximately 20% of all trials) were used in which no target was presented to insure subjects were not guessing or making anticipatory responses. When an eye movement was detected the events of the trial were terminated but the display continued for the usual trial duration. The subject was given feedback indicating that an eye error had occurred. These trials were counted, discarded and later replaced with a new trial. The total number of trials was 512 per block. Because of the long time (approximately 1 hour) needed to complete a block, two 2-minute rest periods divided the block into three parts. The number of trials for each combination of cue and target stimuli remained constant over each block and are presented in table 1. Cue validity, excluding catch trials, was approximately 70%. For double location cues this probability was shared between the cued locations (i.e. 35% each).

Results

and Discussion

Only subjects who showed a cued versus uncued hemifield advantage in RT were included in the analysis. Without this difference there is no reason to assume that the subject was orienting his or her attention. The criterion for inclusion involved two conditions: (1) The average RT for the singly cued location had to be faster than for the corresponding locations on the uncued side, and (2) this had to be true for at least two of the three possible locations. This criterion for inclusion left seven subjects in the marked and nine subjects in the unmarked conditions. Subjects found the multiple task requirements (i.e, aligning attention while maintaining fixation) difficult. Some reported adopting deliberate strategies such as ignoring the cues to help avoid shifts in

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Table 1

Number of trials per block for each cue by location combination. Location of target 1

2

3

4

5

6

Catch

34 3 3 3 3 3 17 3 6

3 34 3 3 3 3 3 3 6

3 3 34 3 3 3 17 3 6

3 3 3 34 3 3 3 17 6

3 3 3 3 34 3 3 3 6

3 3 3 3 3 34 3 17 6

10 10 10 10 10 10 10 10 10

Cued location

1 L

3 4 5 6 1+3 4i6 Neutrals

gaze which, because they were re-cycled, could keep the subject in the experiment indefinitely. It was anticipated that the criteria for inclusion would remove subjects who had adopted such strategies. RT falling three standard deviations above the mean or two standard deviations below the mean (z scores were calculated by cell) were considered aberrant and removed from the analysis. This resulted in the exclusion of only a small percentage of outliers. The percentage of anticipations or misses during a trial were all less than 0.5%. The false alarm rates (i.e., responses on catch trials) are presented in table A.1 of appendix A. An ANOVA performed on these data yielded only two significant effects. The first was a main effect of block, F(1, 14) = 7.52, p < 0.03, such that subjects made fewer false alarms in their final experimental session than in their first experimental session (4.63% versus 6.07%, respectively). The second significant effect was an interaction of the session variable with marking, F(1, 14) = 15.36, p < 0.01, reflecting a sharper decline in false alarm rates for the marked-locations group (from 9.05% in the first session to 3.09% in the second session) than for the unmarked-locations group (from 5.56% to 3.70%). (See table A.2 of appendix A.) The overall RT results, expressed in terms of cost and benefits, are presented in fig. 3. For the purpose of increasing the number of observations for each condition, the data represented in this figure are collapsed across the left and right variable. For example, cue location 1 - target location 6 occurs in the same cell as cue location 6 target location 1 (see fig. 2). The RT data were subjected to a mixed ANOVA with marked versus unmarked locations as a between-subjects variable and with cue (far, mid, near, neutral and double) and location (cued-side far, cued-side mid, cued-side near, uncued-side near, uncued-side mid, uncued-side far) as within-subjects variables. The mean RT and results of this ANOVA are presented in tables B.l and B.2 of appendix B. There was a significant effect of location, F(5, 70) = 19.60, p < 0.01. As can be seen from fig. 3, this effect is mainly due to the cue versus uncued side advantage, which should not be

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R. Klein / Spatial distributions Uncued

Uncued 30 E 20 G e m” 10

-10 B s CJ -20 -30

1

2

3

4

5

6

1

6

I

2

3

4

5

6

2

3

4

5

6

30 -l 2

20

2 m” 10 E

O

VI

_____-----

------I

----

-10 9

s

CJ -20 -30 (c) l-?-IL 1

2

3

4

5

Target Location

Target Location

Fig. 3. Results from the experiment expressed in terms of costs and benefits. The data have been organized into cued versus uncued sides. Each panel represents data under one of the four cuing conditions, the closed symbol(s) on each being the actual cued location(s). Moving from left to

right on the horizontal axis, the first three markers represent far, mid and near locations on the cued side, the last three markers represent the near, mid and far locations on the uncued side.

surprising considering that this was the criterion for being included in the analysis. More importantly, the interaction of cue with location was significant, F(20, 280) = 4.05, p < 0.01, while the main effect of marking and all interactions involving marking yielded Fs < 1. Because the marking variable showed no difference, the remaining analyses discussed here are collapsed across this variable. The failure of the marking variable to interact with the other variables may be attributable to the low salience of the single dot markers. Other studies of covert orienting of visual attention that marked locations (e.g., Downing and Pinker 1985) have used more salient markers, such as boxes around potential target locations, which produce a more articulated visual field (i.e., the locations are better defined). What is important in regards to the marking variable in

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the present experiment is that in the unarticulated condition the cue and location variables still interact. RT on the cued and uncued side were subjected to separate ANOVAs with the neutral condition omitted. On the cued side there was an effect of cue, F(3, 45) = 2.90, p c 0.05, and an interaction of cue with location, F(6, 90) = 4.87, p < 0.01. There were no other significant effects. On the uncued side there was a main effect of location, F(2. 30) = 6.30, p < 0.01, such that RT slowed with increasing eccentricity (334 ms, 344 ms, 351 ms, for near. mid. and far locations). No other effects or interactions were significant. The results from the two separate ANOVAs indicate that attention is not uniformly distributed on either side. On the cued side the interaction between cue and location suggests that visual attention responds differently depending on the cued location. On the uncued side, the main effect of location without an interaction with cue suggests that once fixation is crossed a similar gradient of attention is observed regardless of the cued location. Both of these findings are contrary to predictions of the hemifield activation hypothesis. Double cuing differed significantly from cuing the far location, F(1, 15) = 4.98, p c 0.05, and the near location, F(1, 15) = 5.24, p < 0.05, but it did not differ from cuing the middle location (F < 1). These are exactly the results predicted by the midlocation placement hypothesis. It could be argued that the double cuing data are produced by a switching strategy (see p. 228-229) in which subjects attend to the far location on some trials and the near location on other trials. There are three results from the present experiment which argue against a switching strategy. First of all, our best estimate for what the RT data should look like under conditions of a switching strategy is the arithmetic mean of the (single) cue near and cue far conditions. An ANOVA comparing the average of cue near and cue far with the double cue condition revealed a significant main effect of cue, F(1. 15) = 8.32, p < 0.05, so the double cue does not seem to behave the same as the average of the two single cues. Secondly, and more specifically, RT to the middle location was faster with the double cue than with the average of the near and far cues, t(l5) = 2.50, p -C 0.01. Finally, the variances in RT for the single cued locations were compared with the variance in RT for the double cue condition. An alternating strategy would suggest that for the double cue condition variance should be higher than for the single cue conditions. This was not the case and in fact variance in the double cue condition was lower than the variance for the single cue conditions. The data thus far analyzed can also be used to address the notion of flexible attentional allocation in response to target probability (as might be implemented with criterion changes). A criterion setting framework would predict that performance would be consistent with the probability of stimulation at a particular location. Referring to fig. 3, it can be clearly seen that this is not the case. Uncued locations always have the same probability of stimulation yet performance tends to fall off with distance from the cued location. Moreover, in the double cue condition RT at the midlocation (see fig. 3d) should be very much slower than at either of the two cued locations. However, this is not the case, performance at the midlocation is as good as performance at either of the cued locations. We therefore consider Posner’s notion of an unsplittable beam (Posner et al. 1980) to be strongly supported by this finding.

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Table 2 Mean times [ms] taken to shift attention for marked and unmarked groups by cue type. Cued location

Group Marked Unmarked

1

2

3

4

5

6

Neu

1+3

4+6

471 533

515 581

517 554

512 587

497 516

497 547

500 571

516 570

473 582

What is unique about the present evidence is that a midlocation between two cued locations within the same hemifield has never been assessed before. Data representing the mean times subjects used to initiate their shifts of visual attention for marked and unmarked groups are presented in table 2. Analysis of variance did not yield any significant effects or interactions, in part due to high between-subjects variance. An unexpected finding in the present experiment is the relative flatness of the attentional gradient within the cued hemifield when the middle location is (singly) cued. There are two points which should be made in regard to this finding. First, Downing and Pinker (1985) suggested that there is a linkage between eccentricity of the cued location and steepness of the performance gradient. Their findings suggested that attending to locations further in the periphery produces a wider focal area than attending to locations closer to fixation - the former being characterized by a shallow gradient, the latter by a steeper gradient. This was obviously not the case in the present experiment, which may be a challenge for the Downing and Pinker proposal. Second, it had been stated as an explicit assumption of the midlocation placement hypothesis that subjects would orient a fixed gradient of attention and thus RT at the midlocation would be faster than at either of the two cued locations. This was not the case for double cuing nor was it the case for single cuing the midlocation. It would appear that in this experiment, it is impossible to determine whether a fixed gradient or a zoom lens was oriented. However, Shepard and Mtiller (1989) have provided evidence that may be directly relevant to this issue. Their study examined the spatial distribution of attention as a function of cue-target SOA using exogenous cues (i.e., peripheral, see Jonides 1981; Klein et al. 1987) and endogenous cues (i.e., central cues). More specifically, they addressed the issue as to whether attention is allocated to a spatial location by moving the metaphorical beam or by focusing a zoom lens. The general methodology and use of probe locations was similar to that used in the present study. Their results indicated that, for central cues, attention started out distributed over much of the visual field but gradually focused on the cued location with increasing SOA. At 500 ms, attention appeared to be fully focused on the cued location. In the present research, the average 500 ms self-initiated time to shift along with the 200 ms SOA should have allowed the subject enough time to focus on the midlocation in the (single) cue middle condition, yet this condition failed to show any within-hemifield gradients. It also appears that the attentional focus oriented in the double cue condition was similar in spatial extent as the attentional focus oriented in the cue middle condition. It may be the case that subjects focused a zoom lens of attention in the (single) cue middle condition as well as

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in the double cue condition. The notion of midlocation placement of a fixed gradient spotlight might be better described as a midlocation focusing of a zoom lens. The results of Shepard and Miiller are particularly important because much of the evidence for Eriksen’s zoom lens model has been generated under exogenous cuing conditions (Eriksen and Hoffman 1972; Eriksen and Rohrbaugh 1970; Eriksen and St. James 1986).

Summary and conclusions There are four important findings from the present experiment. The results strongly contradict the hemifield activation hypothesis. This should not be surprising since many previous studies have yielded evidence contrary to the notion of hemifield activation. Hughes and Zimba (1987) themselves have reformulated their position, although our findings are not consistent with their reformulation either. The implications are important in regards to blocking of locations such as the procedure used by Hughes and Zimba (1985, 1987) because subjects may not be attending to specific cued locations but instead may be attending to midlocations. The present experiment shows support for Posner’s and Kiefer and Siple’s (1987) evidence of an unsplittable beam of attention and strengthen their argument by demonstrating the predicted pattern of performance within a single hemifield. Our results are inconsistent with a criterion setting system that responds to relative probability. There is strong support for Klein and McCormick’s (1989) notion that under conditions of uncertainty about which of two locations to attend subjects may adopt a strategy of attending to a visuo-spatial channel that lies midway between the two (midlocation placement hypothesis), although some uncertainty remains as to whether a fixed gradient spotlight or a focusing of a zoom lens best describes the spatial distribution of the attended region.

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Appendix A: Mean false alarm rates and results of ANOVA

Table A.1 False alarm rates (%). Cuing condition

Marked locations Sessions 1 Sessions 2 Unmarked locations Sessions 1 Sessions 2

1

2

3

4

5

6

Neut

1.3

4.6

10.0 5.6

7.1 3.3

11.4 3.3

11.4 1.1

10.0 4.4

10.0 2.2

5.7 2.2

11.4 3.3

4.3 2.2

10.0 5.6

8.6 3.3

2.9 3.3

8.6 5.6

4.3 0.0

4.3 3.3

1.4 0.0

8.6 5.6

1.4 6.7

Table A.2 Mean squares, residuals and degrees of freedom from false alarms ANOVA. Source

df

Mean square

Marking Residual

1 14

1081.6 280.9

Session Marking by Session Residual

1 1 14

146.4 299.1 19.5

Cue condition Marking by Cue condition Residual

8 8 112

93.5 43.8 44.7

Session by Cue condition Marking by Session by Cue Residual

8 8 112

42.7 26.7 33.2

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Appendix B: Omnibus mean RT and ANOVA

Table B.l Mean RT from the omnibus

analysis.

Target location Cued side

Marked locations Cue far Cue middle Cue near Neutral cue Double cue Unmarked locations Cue far Cue mid Cue near Neutral cue Double cue

Table B.2 Mean squares,

Uncued Middle

Near

Near

Middle

Far

310.2 316.9 331.2 331.9 319.1

332.7 317.8 333.4 333.4 326.8

315.1 312.3 299.7 328.9 309.3

345.6 361.2 342.5 328.9 344.3

361.2 313.4 346.1 333.4 362.5

362.2 373.6 375.2 331.9 356.4

294.5 310.5 323.6 320.2 305.5

310.9 295.8 302.7 311.7 291.8

319.3 300.6 296.8 315.4 293.7

338.4 324.6 320.3 315.4 321.1

338.3 338.1 341.9 311.7 327.6

367.3 347.9 331.6 320.2 337.8

residuals

and degrees

of freedom

from the omnibus

df

Source Marking Residual Cue Marking Residual

by Cue

Target Location Marking by Target Residual

side

Far

Location

Target Location by Cue Marking by Target Location Residual

by Cue

ANOVA. Mean square

1 14

38892.3 85182.8

4 4 56

1668.0 634.1 835.3

5 5 70

23091.9 975.1 1178.4

20 20 280

2106.4 400.2 519.7

P.A. McCormick,

R. Klein / Spatial distributions

241

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