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THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 1994, 47A (3) 699-739

A Framework for Understanding the Allocation of Attention in Location-precued Discrimination MaryLou Cheal, Don R. Lyon, and Lawrence R. Gottlob University of Dayton Research Institute, Higley, Arizona, U.S. A . The effects of attention on visual perception are assessed in the locationprecuing paradigm. First, we present a review of some current metaphors for attention and relevant data. Then, a framework is suggested that provides an interpretation of the temporal sequence of external and assumed internal processes within a location-cuing trial. Cases when a precue correctly indicates the target location (valid trials) are compared to cases when the precue directs attention to the wrong location (invalid trials) with the cue location either at fixation or peripheral to the target location. Several specific hypotheses are suggested; these concern decrements in performance on invalid trials and effects of the location of a precue. For the most part, these hypotheses are supported by data in the literature and in some new studies. A gradient-filter metaphor for attention, which includes a synthesis of ideas from the gradient model and the attention gate model, is more consistent with the data than is a spotlight metaphor.

Recently, there has been considerable interest in how attention is allocated to stimuli in the visual field (Bashinski & Bacharach, 1980; Briand & Klein, 1987; Cave & Wolfe, 1990; Cheal& Lyon, 1992; Cheal, Lyon, & Hubbard, 1991; Duncan & Humphreys, 1989; Eriksen & Collins, 1969; Eriksen & Murphy, 1987; Henderson, 1991; Jonides, 1980, 1981; Kahneman & Treisman, 1984; LaBerge & Brown, 1989; Muller & Rabbitt, 1989a, 1989b; Posner, 1980; Reeves & Sperling, 1986; Tsal, 1989). Allocation of attention has been studied in many different paradigms. A particularly useful paraRequests for reprints should be sent to MaryLou Cheal, University of Dayton Research Institute, P.O. Box 2020, Higley, A Z 85236-2020, U.S.A.; FAX: (602) 988-3556; E-MAIL: CHEAL@HRLBAN 1.AIRCREW. ASU. EDU. We appreciate the technical assistance and/or computer programming provided by D. Bolin. M. McConnon, S. Riedler, and C. Vrana. The manuscript profited from the helpful comments of G.W. Humphreys and H. Muller. The research was funded by the Air Force Office of Scientific Research (Life Sciences Task 2313T3) and the Air Force Human Resources Laboratory (Contracts F-33615-87-C-0012 and F-33615-90-C-OOO5).

@ 1994 l h c Experimental Psychology Society


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digm is location-cuing, in which a target location is cued prior to onset of the target. It has been shown that such precueing improves the ability to detect (Posner, 1980) or discriminate (Eriksen & Collins, 1969) various types of stimuli. This improvement can be shown with both simple luminance changes and with more complex stimuli (Cheal & Lyon, 1992). Although accuracy of discrimination is measured in our research, measures have also included reaction time (RT) for detection or discrimination (i.e. Eriksen & Hoffman, 1972; Posner, 1980) and accuracy for same/different judgements (Muller & Findlay, 1988; Muller & Rabbitt, 1989a, 1989b). There is convincing evidence that attention to a target location actually improves sensory information processing rather than only changing decision criteria (Downing, 1988; Hawkins, Hillyard, Luck, Mouloua, Downing, & Woodward, 1990; Mangun, Hansen, & Hillyard, 1987).

Metaphors for Attention There are several metaphors for how attention may be allocated to one or more locations in the visual field even in the absence of eye movements. These include the spotlight model of attention, in which attention is thought to move across the visual field in an analogue manner (Posner, Snyder, & Davidson, 1980; Shulman, Remington, & McLean, 1979; Tsal, 1983); the zoom lens model, which is similar but allows a variable size gradient of attention centred at the focus of attention (Eriksen & St. James, 1986); the gradient model of attention, in which the size and shape of the concentration of attention can vary with the task demands (LaBerge & Brown, 1989); and the attention gating model, in which a signal allows the opening of a gate in an appropriate channel (Reeves & Sperling, 1986). Attentional mechanisms implied by these metaphors are summarized in Table 1. Below, we will illustrate some different accounts of location-cuing that arise from the assumption of different metaphors of how attention is allocated. These illustrations suggest that none of the current metaphors leads naturally to explanations of all aspects of the use of attention in locationcuing. Spotlight. First, let us discuss the spotlight metaphor. Some time ago, it was suggested that attention moves through space like a spotlight, with time to relocate increasing with the distance to be moved (Posner et al., 1980). If one assumes a diamond-shaped display with possible target locations at 3, 6 , 9 , and 12 o’clock, according to the spotlight metaphor, attention might be concentrated at fixation prior to the onset of a peripheral cue. If, on the other hand, one assumes a variable (zoom) lens, attention may be spread evenly across the field. After a cue appears, the peak of attention focus would move directly to the cued location. If attention is


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TABLE 1 Metaphors for Attention Metaphor

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location expectation and position analyser act on a filter, which in conjunction with feature register provides input to shape identifier

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opens attention gate to initiate a flow of information

Gradient-Filter

multiple filters that vary in permeability to allow variation in the flow of information in such a way as to produce a gradient

"Based on Posner et al., 1984. hBased on Eriksen and St. James, 1986. 'Based on LaBerge and Brown, 1989. "Based on Reeves & Sperling, 1986.

like a spotlight or a zoom lens, it could be concentrated in a narrow area that falls steeply outside the target location with a peak of resources concentrated within 1" (Eriksen & Hoffman, 1972). If the cue indicates an incorrect location, at the time that the target appears attention would disengage from the cued location, move across the visual field to the target, and engage attention at the target location, with a subsequent concentration of attentional resources (e.g. Posner, Walker, Friedrich, & Rafal, 1984). Although the spotlight metaphor has been widely used, it is inconsistent with a growing body of data. Some early data supported this metaphor (Shulman et al., 1979; Tsal, 1983), but these studies have been criticized (Eriksen & Murphy, 1987; Yantis, 1988). Additional studies failed to support an analogue movement of attention (Cheal & Lyon, 1989; Driver & Baylis, 1989; Eriksen & Webb, 1989; Hughes & Zimba, 1985; Kwak, Dagenbach, & Egeth, 1991; LaBerge & Brown, 1986; 1989; Murphy & Eriksen, 1987; Nakayama & Mackeben, 1989; Reinitz, 1990; Remington & Pierce, 1984; Rizzolatti, Riggio, Dascola, & Umilta, 1987; Shulman, Wilson, & Sheehy, 1985).

Gradient. According to another metaphor, attention may be distributed in a gradient around the cued location, or it may form an irregular field with multiple peaks of different intensities (heights). Although LaBerge and Brown (1989) derived their gradient theory from experiments with simple linear arrangement of characters, it is not necessary to assume that a gradient will always be unimodal. Others have found evidence for the spread of attention in a ring (Egly & Homa, 1984; Juola, Bouwhuis,


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Cooper, & Warner, 1991), between two locations (Bonnel, Stein, & Bertucci, 1992; Castiello & Umilta, 1992), or to irregular subsets of squares of a grid (Podgorny & Shepard, 1983). It appears that attention may not always be narrowly focused, and the shape of this attentional distribution may vary with the task demands as perceived by the observer (Henderson & Macquistan, 1993; Humphreys, 1981; LaBerge, 1983; LaBerge & Brown, 1989). Attention Gating. The attention gating model proposed by Sperling and Reeves (1980; Reeves & Sperling, 1986) is based on data in the RSVP (rapid serial visual presentation) attention shift paradigm in which there are two visual streams of characters. Eyes are fixated between the two streams. Attention is first directed to one stream (of letters) until a particular character functioning as a cue appears, and then it is directed to the other stream (of digits) in order to report the first digits perceived. They propose that when the cue appears, a gate is opened to the digit stream. A model of the temporal properties of this gate accounts for the order in which items in the digit stream are perceived. Although the attention-gating model is not derived from location-cuing experiments, the time course that is reported (Sperling & Reeves, 1980; Weichselgartner & Sperling, 1987) is consistent with the time course reported for location-cuing (Cheal & Lyon, 1991a). In the RSVP work, when the two sets of characters are presented in the same stream, the character that is first reported from the second stream occurs about 100 msec after the cue. This time interval is the same as the optimal SOA after a peripheral cue (cue and target near the same location). In distinction, when the two sets of characters are presented in different streams, the first reported character occurs 300-400 msec after the cue. This interval is similar to the interval needed for optimal performance with our central cue condition (cue and target in different locations). Thus, the gating metaphor captures important temporal characteristics of attention, and these characteristics may be the same in both paradigms. Gradient Filter. The attention-gating model explores the characteristics of a single attention gate in the RSVP task. However, understanding the location-cueing task may require that we consider simultaneous operations at different locations. This may be done by combining the temporal properties of the gating model with the idea of an attention gradient (similar to that of LaBerge & Brown, 1989). This combination leads to a metaphor based on the concept of semi-permeable filters.' 'Although there are similarities between this metaphor and an irrigation metaphor (Klein & Briand, 1985). conceptually they are different. The main difference is that in the irrigation

metaphor, attention is flowing through the system, whereas in the gradient-filter, attention changes the permeability of the filters; this change allows more information to flow in the system.


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The amount of attention at each location may regulate the permeability of the filter that controls the amount of information that can flow from the display. During fixation, attention may be spread across the visual field, with peaks at each possible target location and at fixation (Figure 1A). Some attention would be expected at fixation, because of instructions to fixate the eyes. Likewise, some attention may be allocated to each possible target location, in order to perceive the cue when it appears. Thus, prior to the cue, it may be possible for various small amounts of information to filter through many variously permeable attention filters and result in a gradient of information flow. This gradient is not necessarily symmetrical around any one location, and we make no assumptions about its shape. Each filter in this gradient is analogous to a hypothetical polarizing filter that varies in density across the filter to allow gradients of light intensity to pass. The shape of the gradient of attention may change over time. When the peripheral cue appears, the filter near the cued location begins to increase in permeability, which allows for greater flow of information at the cued location, with little, if any, change in flow rate at the other three possible target locations (Figure 1B). Therefore, there may be some resources available at the non-target locations. As the duration of the SOA increases, more attention will be allocated to the cued location, and the amount of attention at fixation and at the most distant location may decrease somewhat (Figure 1C). In this case, when the target appears at a non-cued location (on invalid trials), there will be some attention already focused on the target location, and the additional time needed to discriminate the target will vary with a number of factors, such as the type, size, and luminance of the target. Changing permeability may allow more information to flow at some locations and less at others. Where there is an overall capacity limitation, increasing information flow at one location may require a decrease in information flow at other locations. Conversely, decreasing information flow at one location would allow an increase at other locations. Increasing the permeability of the filter may correspond to facilitation of performance with precuing (evidence for such facilitation was cited above). On the other hand, increasing the opaqueness of the filter may correspond to inhibition, for which there is also considerable evidence in the literature (Neumann & DeSchepper, 1992; Tipper, 1992; Tipper, Weaver, Kirkpatrick, & Lewis, 1991). This is similar to excitation and inhibition in the neural model of LaBerge, Carter, and Brown (1992). The rates of information flow that result from the variably permeable attention filter may be represented by the various heights of the curves in Figures 1B and 1C. Note that although Reeves and Sperling PO :late that one gate closes as another opens, there i.. inherent reason that one filter must close completely before another can open, provided the system is below maximum capacity. The gradient filter is differentiated from other I

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FIG. 1. Three-dimensional plots of hypothetical distributions of attention as an irregular gradient field for a visual display with four possible target locations at the 3, 6, 9, and 12 o’clock positions. The cue is to be presented peripheral to the right (3 o’clock) target location. A: During fixation, with the assumption that attention forms an irregular gradient field that has peaks of information flow at each possible target location and at fixation. B: Shortly after the cue appears, with the assumption of a gradient field from the cued location. C: After a longer SOA, when the flow of information is greatest at the cued location. The flow of information at the furthest possible target location (at 9 o’clock) may decrease somewhat.

metaphors in that various filters become more or less permeable and are not dependent on movement (as in the spotlight metaphor) or on the simultaneous opening and closing of individual gates. The concepts of a gradient filter are illustrated in Figure 2. The surfaces at the bottom of the drawings represent the display during the SOA interval (after the cue has turned off) and before (Figure 2A) and after (Figure 2B) the onset of the target. I n the second surface from the bottom, a gradient of attention filters is used to illustrate a two-dimensional filtering function


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across the field. The density of each location on the gradient represents the permeability of each attention filter at the corresponding location on the display surface. The lighter circles represent filters with greater permeability. Thus, permeability can vary from closed (black) to completely open (white). The potential rate of flow of information at each of these locations increases with the permeability of that filter. This is represented by the height of each curve in the third surface of the figure (counting from the bottom). The top surface is a representation of the resultant percept. The field of attention filters, the possible rate of information flow, and other processes leading to the percept can all be affected by higher-order processes (top-down information). In Figure 2A, there is more information flow at the cued location due to the cue that has disappeared. Smaller potential amounts of information flow are shown at the other possible target locations. In Figure 2B, after the target appears, information from the target can flow through the open filter at the target location, and the

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Long SOA Prior to target

Valid target onset

FIG. 2. The gradient-filter metaphor of attention. Information from the display passes through a field of attention filters that has an irregular gradient. This gradient allows differences in the flow rate of information across the different spatial locations, which results in changes in the percept. A : After the cue has been turned off, and attention has had time to focus on the cued location. B: After the target appears. (See full details in text.)


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target is perceived quickly with an increased probability of correct discrimination. The above observations are consistent with the suggestion that the shape of an attention field is determined by the task, the stimulus, and the individual observer (Pressey, 1992). It is also clear that the amount and distribution of attention may vary between trials, and perhaps over time within a trial; it may be affected by abrupt onsets (Yantis & Jones, 1991); and it may vary with prior knowledge of the probability that the cue indicates the target location (Gottlob, Cheal, & Lyon, 1992; Jonides, 1980). It would be expected, of course, that there is a limit on the total amount of attention available at any one point in time. In our formulation, that amount would correspond to the maximum total permeability of the attention filter at that moment.*

Framework for Attention In order to improve our understanding of the use of attention in the location-cuing paradigm, we recently introduced a framework for the ordinal sequence of events that occurs within a trial (Cheal & Lyon, 1992). This framework applies to a location-precued forced-choice discrimination. It is based on a large number of trials collected from each observer, which allows enough data to provide precise time courses of attention effects under different conditions. It is not a theory or a model, but, rather, a heuristic device to help visualize hypotheses about underlying processes. We have tried to minimize the number of assumptions that are inherent in any attempt to understand processes that cannot be assessed directly. Where we do not have sufficient knowledge to choose between certain possibilities, we have used the simpler possibility. For this reason, no absolute time courses are implied, but, rather, only the order of the events is represented. The framework will be presented for: (1) peripheral cue, valid trial condition; (2) peripheral cue, invalid trial condition; (3) central cue, valid trial condition; and (4) central cue, invalid trial condition. Although much of the framework draws on previous results, some new experiments will be presented where appropriate for testing hypotheses suggested by the framework.

Peripheral Cue, Valid Trials. First, consider the condition in which the cue is located peripherally and is always valid. In the variation of locationcuing used in our laboratory, the interval between the onset of the precue and the onset of the target (stimulus onset asynchrony-SOA) is varied 'Our gradient-filter metaphor incorporates ideas that have much in common with those of Pan and Eriksen (1993).



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from simultaneous presentation (0 SOA) to 200 or more msec. Observers make a 2-alternative or a 4-alternative forced-choice discrimination. Controls have been included to minimize any alerting function of the cue, cue-target masking (including some trials with post-target cues), and continued processing of the targets (for thorough discussions of possible artifacts, see Cheal, 1993; Cheal & Lyon, 1989; Cheal et al., 1991; and Lyon, 1990). In this task, there is a reliable improvement between 17-msec SOA and an asymptotic score that occurs at 100-msec SOA or more. On the left side of Figure 3, computer screens for a four-alternative forcedchoice discrimination of Ts of different orientations are depicted in the order presented. To the right are the events that we propose are occurring during each trial. These events were based on the assumption that when the cue appears, the permeability of the attention filter increases and initiates a flow of information at the cued location. Referring to Figure 3: (1) When the fixation screen appears, observers fixate and continue to fixate throughout the trial. (2a) The next screen contains a cue at one of four possible locations. (2b) A variable amount of time is needed in order to detect the cue. (3) Once the cue is detected, the attention filters are “automatically” opened at the target location, which increases permeability and allows a greater flow of information at

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that location. Permeability can continue to increase during the variable interval, and also after target presentation; after long SOAs, however, permeability may begin to decrease as depicted towards the end of line 3. (4) The target screen is presented for a brief duration, with a character at each of the four possible target locations. Note that there are no stimuli on the screen other than the four target-like characters in order to reduce interference. The target is the character in the location that was cued. Visual information about the target begins to be acquired. Data limitation due to size, luminance, target duration, etc. are the same for all SOAs. (5a) The target screen is followed by a mask in order to minimize further visual processing. (5b) Discrimination of which target is in the cued location occurs before the decision as to the appropriate response and to the making of that response. Only one response is required in order to reduce memory load. The cues are large enough so that they can be seen readily on most trials by practised observers. Note that there are no speed constraints on the response, because accuracy, and not RT, is measured. Imagine that Lines 4,5a, and 5b in Figure 3 move together laterally: left for short SOAs and right for long SOAs. The amount of lateral movement is determined by the SOA. Now compare this framework with data collected in the location-cuing paradigm. The curve for accuracy of two-alternative forced-choice discrimination of Ts with a peripheral cue as a function of SOA shows that there is a very rapid increase in accuracy from 17-msec SOA to about 100-msec SOA (left graph in Figure 4). If the target appears before the attention filter has opened at the target location (the target directly follows the cue), accuracy is close to chance. If the target appears after a longer SOA (as illustrated in Figure 3, where Lines 4,5a, and 5b have shifted to the right), accuracy is improved considerably for many targets (such as Ts, oblique Ts, and curves versus a n g l e s x h e a l & Lyon, 1992). This improvement may be due to the increased permeability of the attention filter, as shown by the rise in Line 3 of Figure 3. Other types of targets result in less improvement with increases in SOA (lines of different orientations and colours, centre and right graphs of Figure 4, respectively). Note that the points on each curve shown in Figure 4 only represent different SOAs. Having previously argued that SOA effects are not principally due to alerting effects or masking effects in this paradigm, let us assume that the only components of the task that can differ as a function of SOA are attention processes. The flow of information due to changing permeability of the filter (represented in line 3 of Figure 3) may be dependent on multiple factors, which are discussed elsewhere in this paper. Data for different types of targets are consistent with the statement that “attention affects encoding rate, rather than the onset of processing” (Reinitz, 1990, p. 502). Note that the onset of changes due to SOA begins


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FIG. 4. Proportion correct as a function of cue-target SOA for three different types of targets in two-alternative forced-choice discriminations. The left graph (redrawn from Cheal et al., 1991) provides data for sideways Ts presented for three different target durations: 50, 67, and 83 msec. The centre graph provides data for slanted lines presented for three different target durations: 17, 33, and 50 msec. (redrawn from Cheal et al., 1991). The right graph provides data for three colour discriminations: red versus green, red versus yellow, and green versus yellow. Note that the green and yellow used were more similar to one another than were the other colour comparisons. Colour data are redrawn from Cheal and Lyon (1992).

at least by 33 msec for all types of targets (Figure 4). Within the context of the gradient-filter metaphor, the different precue effects for different targets reflect differences in the rate of information flow needed for discrimination. Therefore, extraction of the maximum information from the target requires less permeability for targets such as colours and line orientations than for line arrangement targets. With peripheral cues, the attention filter can only remain fully open at the target location for a finite time. After about 100-150 msec, accuracy may decrease, even though the target will always eventually be presented at the cued location. The small decrease in accuracy with long SOAs (left graph in Figure 4) corresponds to the decrease in the permeability of the attention filter shown to the right in Line 3, Figure 3, if Lines 4,5a, and 5b are moved to the far right. The amount of decrease at long SOAs is quite variable across observers. There are at least three possible explanations for this decrease: (1) attention may begin to decrease at the cued location and increase elsewhere after a finite time, even though all trials are validly cued; (2) the decrease in accuracy at long SOAs may be due to inhibitory processes (Posner & Cohen, 1984; Posner, Rafal, Choate, & Vaughan, 1985); and (3) the decrease may be due to the transient nature of the response to a peripheral cue (Muller & Findlay, 1988; Nakayama & Mackeben, 1989). These possibilities are explored further in the discussion.


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Peripheral Cue, Invalid Trials. An examination of the framework for invalid trials may be helpful in understanding why miscuing might affect performance even for discriminations that do not require focal attention. In the case of an invalid trial with a peripheral cue, when the majority of the trials are valid, the attention filter will begin to open at the cued location at the onset of the cue (Line 3, Figure 5B). This assumption, of course, must be qualified with all the reservations suggested for Ts in the peripheral cue, valid trial condition. When the target appears (Line 4a, Figure 5B), the observer must detect the target at a non-cued location (Line 4b, Figure 5B). Although the time is variable (as shown by the slope of onset), detection is done rapidly when the non-targets differ from the possible targets by a simple feature (colour, line orientation). Once the target is detected, the attention filter at the cued location will close (Line 4c, Figure 5B). The exact rate of closure is not known, but we hypothesize that, following a brief delay, the onset of closure reflects variability in target detection time. However, inasmuch as the allocation of attention to the cued location is never tested on invalid trials, it is possible that there is little or no change in the flow of information at the cued location after the target is detected elsewhere. Now the attention filter at the target location will begin to open gradually (Line 4d, Figure 5B). The onset of target detection, filter closure at the cued location, and filter opening at the target location (Lines 4a through 5b) are all linked to the onset of the target. Therefore, the amount of time available for discrimination of the target is limited to the target duration and is the same for all SOAs. It appears that the redirection of attention to a new location after the target is detected may mean that a longer target duration is needed for target identification. In essence, the time needed for allocation of attention is subtracted from the target duration, so that these trials are equivalent to a shorter target duration. Similar to a shorter target duration, miscuing would limit the amount of information available from the target and should affect all types of targets when they are presented for brief durations. The illustration of the gradient-filter metaphor (Figures 1 and 2) shows that there is some attention at non-cued locations when the target appears (Figure 2B). If one postulates that different targets require different amounts of attention for accurate discrimination, then for some targets there may be enough attention already at the target location (Figure 2A) so that discrimination is above chance at 0-msec SOA, even with a brief target duration. This is illustrated by data for Ts and for line orientation discrimination shown in the left and centre graphs of Figure 6. With the short target duration (a mean of 26 msec), although the target may have been masked before enough attention was concentrated to make an easy discrimination, accuracy for invalid trials was well above chance proportion correct (0.25) at all SOAs. Even so, there was an advantage for the cued


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location over the non-cued locations. The better accuracy for invalid trials for lines (mean = 0.43) than for Ts (mean = 0.39, p < O.OOl), even though the Ts were presented for a slightly longer mean duration (28 msec), may be due to different amounts of attention concentration needed for discrimination of the two types of targets. This suggestion warrants further testing. The reasoning from the framework suggests that there should be poorer performance on all invalid trials in comparison to valid trials for the same conditions, provided the target is presented for a very b r i d duration. If the target were presented for a longer duration, then there might be enough time for attention to be more fully allocated to the target, and we would expect less decrement on invalid trials in relation to valid trials.

Central Cue, Valid Trials. There has been considerable interest in the differences in performance when the target location is indicated by a cue at fixation in comparison to a cue near the target location (Cheal & Lyon, 1991a; 1991b; Jonides, 1981; Muller & Rabbitt, 1989a). In contrast to the


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FIG. 6. Proportion correct as a function of cue-target SOA for three different types of targets in conditions where cues were 80% valid and 20% invalid, randomized within blocks of trials. The left graph (redrawn from Cheal et al., 1991) provides data for four-alternative forced-choice discrimination of Ts. The centre graph (redrawn from Cheal et al., 1991) provides data for four-alternative forced-choice discrimination of lines (LI). The right graph provides data from Experiment 1 for discrimination of red from blue. Note that chance in the right graph is 0.5, whereas it is 0.25 for the other two target types.

rapid increase in accuracy with SOA as found with a peripheral cue, the improvement in accuracy with SOA is much slower with a central cue. In addition, with a central cue, accuracy continues to improve as SOAs increase in length (at least up to 300-msec SOA), whereas with a peripheral cue accuracy begins to decline after 100-150-msec SOA (Figure 7). Now let us consider how this framework could be used to interpret performance when target location is indicated by a central cue (Figure 5C). The fixation bar ends when the arrow cue takes its place; however, eye monitoring has shown that fixation is held throughout the trial (Cheal & Lyon, 1991a). In distinction to a peripheral cue trial, there would be an additional element-interpretation of the symbolic cue (Line 2b, Figure 5C). The time needed to interpret the cue would be expected to be longer and more variable than with a peripheral cue, which would result in slower and more variable increase in permeability of the attention filter (Line 3, Figure 5C). Thus, the time course of effects of attention could be delayed in comparison to the time course of attention effects when a peripheral cue is used. The time needed to interpret cues needs to be tested.

Central Cue, Invalid Trials. The last condition to be addressed by the framework (central and peripheral cues on valid and invalid trials) has not been tested in our location-cuing paradigm. On invalid trials, there should be a decrement in comparison to valid trials due to determination of the target location (Figure 5D) as found for the peripheral cue, invalid trial



713

FIG. 7. Proportion correct as a function of cue-target SOA for four-alternative forcedchoice discrimination of Ts on trials when the cue was located 1" peripheral to the target location (peripheral) and when the cue was located at fixation (central). The data in the left graph (redrawn from Cheal & Lyon, 1991a) were collected within one experiment, with each observer tested on both conditions, counterbalanced for order. Because long SOAs were used, eye movement was monitored on all trials with an infrared monitor (EYE-TRAC Model 210, Applied Science Laboratories), and any trials with an eye movement in excess of 2"-3" were discarded from analysis. (Data in the right graph are redrawn from Cheal & Lyon, 1991b.)

condition (Figure 6). Again a variable duration would be required for interpretation of the cue (Line 2b, Figure 5D). This would result in a delay in changing the permeability of the attention filter at the cued location (Line 3, Figure 5D). However, when the target screen is presented (Line 4a, Figure 5D), it would be apparent that attention had been allocated to the wrong location (depicted as an abrupt reduction in filter permeability, but the assumption of an abrupt change is not necessary to our hypotheses). Once the actual target location was known (Line 4b, Figure 5D), the attention filter would close at the cued location (Line 4c, Figure 5D), and the attention filter at the target location could begin to open (Line 4d, Figure 5D). These ideas and others suggested by this framework deserve an empirical test.

EXPERIMENTS The framework suggests three separate questions to be addressed empirically: (1) Will there be a decrement on invalid trials for all types of targets? (2) Does it take longer to interpret a central cue than a peripheral cue? (3) Will there be a difference on invalid trials as a function of cue type or SOA?


714


General Method Observers. Young men and women (under 30 years of age, with normal or corrected-to-normal vision) were paid to participate. Observers were tested on many thousands of trials for Experiments 1 and 3, and some observers participated in more than one experiment. Thus, observers were highly experienced, although not aware of the purpose of the study. Apparatus. Two IBM-XTs with enhanced colour monitors (60-Hz frame rate) were used, with the stipulation that all trials for any one experiment be conducted using the same equipment. Phosphors were P-22-B, P-22-G, and P-22-R, all with decay to 10% in less than 1 msec. Luminance (measured with a Spot Spectrascan) was 68 c d m 2 for one monitor (Experiments 1A & 3) and 53 cd/m2 for the other (Experiments 1B & 2). For the luminance experiment, 53 cd/m2was defined as dim and 122 cd/m2as bright. With the exception of coloured stimuli, white pixels were presented on a dark grey background. A video camera was used to watch for eye movements with the monitor located in an adjacent room for Experiments 1 and 3. Trained observers seldom make eye movements, because saccades can result in visual suppression of the briefly presented targets. A chidhead rest was used to maintain the eyes of observers at approximately 37 cm from the monitor. Room lights were on. Statistical Analyses. Data from location-cuing experiments (Experiments 1 and 3) were analysed by the log-linear model of cross-classified categorical data (Fienberg, 1980; Grizzle, Starmer, & Koch, 1969). This analysis is appropriate for data with a dichotomous response (correct/ incorrect). The model is a generalized linear model that provides information similar to factor effects and interactions of the analysis of variance, without the necessity of meeting the assumptions of parametric tests. Tests of significance are provided by chi-square tests of partial association, with the emphasis on interactions with the dichotomous response. This is a logit type of log-linear analysis. The analyses were performed using the BMDP-4F hierarchical log-linear analysis program (Dixon, 1988). Because this is a fixed effects model, separate analyses for each observer were computed after computation of the complete data set. The complete data set analysed all of the variables used in a particular experiment. Additional partial log linear analyses were used to aid understanding of numerous interactions. In some cases, an improvement score was computed by subtracting the proportion correct at 17-msec SOA from the mean proportion correct for SOAs equal to or greater than 100 msec.



715

EXPERIMENT 1 Prior research has shown a decrement on invalid trials compared to valid trials when the targets are Ts or lines of different orientation (Figure 6, left and centre). However, it is not known whether a simple colour or luminance discrimination will result in the same pattern of results. Therefore, these stimuli were used to test the three hypotheses arising from the framework: (1) There will be a decrement in accuracy for all targets on invalid trials in comparison t o valid trials if short target durations are used, because there is insufficient time for attention to be allocated to the target location on invalid trials. (2) Longer target durations will lead to less decrement on invalid trials, because the attention filter can continue to open during target presentation. (3) There should be little or no difference in accuracy as a function of SOA on invalid trials, because the SOA is completed before the target appears, and therefore allocation of attention to the target does not occur during the SOA interval. In this experiment, three different colour discriminations were tested. Because the equipment we used did not allow separate control of luminance and of hue, using more than one colour comparison allowed the comparison of targets of varying hue and luminance and variations in the target durations required in order to maintain scores above chance but below ceiling. In Experiment 1A, a two-alternative forced-choice discrimination of colours was used to test the three hypotheses; in Experiment l B , a twoalternative forced-choice discrimination of luminance was used to test the first and third hypotheses. In addition, in Experiment 1B there was a condition without characters at non-target locations in order to determine the effect of non-targets on the pattern of responses in this paradigm. Earlier work has shown differences due to distractors in same/different judgements (Muller & Findlay, 1988). Note that evaluation of the third hypothesis requires a large amount of data on invalid trials, and therefore these experiments, as well as others we have conducted that include invalid trials, use a very large total number of trials.

Method Procedure. Experiment 1A. Three observers (two men and one woman, 19-25 years of age), all with good vision, were tested on a total of 30,264 trials in a two-alternative forced-choice discrimination of red versus blue. Two of the observers were also tested on another 9984 trials each for another colour discrimination (one with green vs. yellow and one with cyan vs. yellow), counterbalanced with red/blue for order of colour condition across sessions (4 blocks of 104 trials of each condition were


716


tested in each one-hour session). The target was either a red (green or cyan) or a blue (yellow) plus sign (0.9O x 0.9"). The cue was a white square (0.8') that was presented for one screen refresh (17 msec). As can be seen in Table 2, green and yellow were more similar to one another in hue than were the red and blue, but both were fairly similar in luminance. Cyan and yellow were different in hue and were more similar in luminance. It was necessary to vary the mean target durations because of differences in discrimination difficulty: mean duration for all trials was 94.5 msec for greenfyellow, in comparison to 16.9 msec for redhlue and 17.4 msec for cyan/yellow. On 80% of the trials, the cue indicated the correct location; on the other 20% of the trials, the target could be at any of the three non-cued locations. No neutral cues were used, because of the difficulties involved in interpretation of neutral cue data (Jonides & Mack, 1984). Thirteen SOAs were used, from 0 msec to 234 msec. They were randomized within blocks, as were valid and invalid trials. A white plus sign was used for the non-targets. The colour difference between targets and nontargets allowed a "preattentive" discrimination of the target from the nontargets. Non-targets were used so that the target would not automatically call attention, as it might if it were the only sudden onset (Yantis & Jonides, 1984). The target screen was masked by four outlines of the plus sign, each line segment of which was half of one colour and half of the other. The masks were placed at each of the four locations: 6" right, left, up, and down from fixation. Experiment ZB. For luminance targets, three observers (two men, 19 and 20 years of age, and one 25-year-old woman) were tested on a total of 59,902 trials. The target was the same plus sign (0.9" x 0.9") used for colour, but in this case varied in luminance. All other aspects of the trials were the same as those in the colour discrimination task. All observers in TABLE 2 Characteristics of Colour Stimuli

Colour

UI

V'

Luminance (cdlm')

White Red Blue Green Yellow Cyan

0.18

0.45 0.50 0.24 0.55 0.55 0.44

82.68 19.20 16.5X 68.21 76.45 74.72

0.32 0.18 0.14 0.18 0.15

Note: Numbers represent Uniform Chromaticity Space.

CIE

1976



717

this experiment were tested on two conditions, one with 0 s as non-targets (again chosen because they can be differentiated preattentively from the target) and one in which there were no non-targets. Target duration was the same for the two conditions: a mean of 40 msec. Four blocks of trials (104 trials per block) were presented for each condition (counterbalanced for order) in each session.

Results Experiment ZA. For all of the colour comparisons, invalid trials resulted in poorer accuracy than did valid trials. The data for the red versw blue discrimination were analysed for the three observers together (right graph of Figure 6) and separately. There were significantly better performances with valid cues than with invalid ones, significant effects of SOA, and of Validity x SOA interactions in each case (Table 3). There was a significant increase in accuracy with SOA for valid trials (improvement score = 0.07), and a small decrease with SOA for invalid trials (improvement score = -0.002). There were also significant effects of Validity, SOA, and Validity X SOA interaction for the data for the cyanlyeflow and the greenlyeflow comparisons (Figure 8; Table 3). However, examination of Figure 8 indicates that there were differences in the results with different colours. Both the within-observer analysis of cyan/yellow compared to redblue and the within-observer comparison between green/yellow and redblue revealed significant differences

1.o

'25

1 CT

0

SM

100

200

100

200

CUE-TARGET SOA (marc)

FIG. 8. Proportion correct as a function of cue-target SOA in Experiment 1 for twoalternative forced-choice discrimination of colours on trials when the cue correctly indicated the target location (valid) or when it cued the wrong location (invalid) for within-observer comparison of two different colour discriminations. Valid trials are shown in solid symbols, and invalid trials are open symbols. Left graph: data for Observer CT on red/blue (squares) and cyan/yellow (circles). Right graph: data for Observer SM on redblue (squares) and greenfyellow (triangles).


718

CHEAL, LYON, GOTTLOB TABLE 3

x2 (Hi-Log Linear Analysis) for Experiment 1A ~~

Individual Observers Effect

df

3 Observers

cw

SM

CT

1 12 12

3368.6" 274.1d 203.5d

796.3d 149.5" 93. ld

753.1d 141.8" 92.1"

2196.4d 49.7d 74.1"

RedlBlue

Validity SOA Validity

X

SOA

In valid Trials

Valid Trials

12

SOA ~~

459.3d

22.3" ~

~

~

CyanlYellow

Validity SOA

Validity x SOA ~

1 12 12

~~

973.9d 70.2* 49.8d

1 1

12 12 1 12

~

C Y v s . RB

GYvs. RB

290.5" 2964.7" 103.7d 95.3" 213.3" 28.6h

4.6' 719.2d 115.1d 89.8d 137.4" 26.9h

Vaiid Trials

Colour SOA

Colour x SOA

116.3d 24.9" 24 6"

~

Across Colours

Colour Validity SOA Validity x SOA Validity x Colour Three-way interaction

GreenlYellow

1 12 12

497.0d 176.0" 27.gh

Invalid Trials

Valid Trials

0.4 21 .o 19.0

58.7" 192.8" 56.7d

Invalid Trials

77.@ 17.6 16.3

"p < 0.05, "p < 0.01, ' p < 0.001, ' p < O.OOO1.

between colours (Figure 8, CT and SM, respectively). Recall that order of testing was counterbalanced daily, so that any practice effects should be similar for the two conditions. In order to understand the significant interactions of Validity with SOA and Colour and the three-way interaction, analyses were computed for the colour comparisons for valid and invalid trials separately (Table 3). The cyan/yellow and red/blue colour targets were presented for the same duration (mean of 17.4 msec) for observer CT, yet on valid trials performance



71 9

was better for red/blue (squares in Figure 8) than for cyan/yellow. In contrast, there were no significant differences between scores on invalid trials. However, although changes in the difficulty of the discrimination did not affect invalid scores, caution is advisable in interpretation because performance was at chance level. There were poorer scores for green/yellow (triangles in Figure 8) than for red/blue on valid trials (even though there was a 78-msec target duration advantage for green/yellow over redblue). However, the pattern of results differed from that of cyan/yellow on invalid trials. Even though target durations were longer, which indicated that the discrimination was more difficult, performance on invalid trials was better for greedyellow than for red/blue (Table 3). There were no significant effects of SOA on any of the invalid trials in these analyses.

Experiment ZB. For the luminance data, there was poorer accuracy on invalid trials than on valid trials for all observers, together and separately, and a significant effect of SOA for two of the observers (Table 4). In addition, there was a significant interaction of non-targets (presence/ absence) with validity for two of the observers (the third observer failed to reach significance, p = 0.051 in the predicted direction) and with SOA for one observer. This analysis shows that the effect of validity was greater for trials with than for trials without non-targets (Figure 9). There was also a significant SOA x Validity interaction for two of the observers, but not for the third.

WITH NONTARGETS

WITHOUT NONTARGETS

z

- 'd

InvmWd

n .25

0

100

200

0

100

200

CUE-TARGETSOA (msrc)

FIG. 9. Proportion correct as a function of cue-target SOA in Experiment 2 for twoalternative forced-choice discrimination of luminance on trials when the cue correctly indicated the target location (valid) or when it cued the wrong location (invalid) for conditions when there were non-targets at three locations (left) and when the other three locations were blank (right).


720

CHEAL, LYON, GOTTLOB TABLE 4

x2 (Hi-Log Linear Analysis) for

Experiment 1B


df

3 Observers

cc

cw

SM

Validity

1 12 1 12 12 12

215.2d 46Sd 0.5 30.0d 23.1" 36.4'

35.0d 16.3 1.4 3.8 7.6 16.9

114.3d 40.9d 1.7 4.3" 28.7h 34.9'

74.7d 33.5' 1.4 28.gd 7.8 37.3'

SOA

Non-targets Non-targets x Val Non-targets X SOA SOA x Val

Valid Trials SOA

Non-targets Non-targets x SOA

SOA

12 1 12

12

65.6d 4.1" 32.1h

Invalid Trials

17.8 26.0d 14.1

With Non-targets

Without Non-targets

52.8d

44.7d

For valid trials, there was a small effect of non-targets. The Non-target x SOA interaction resulted from an increase in accuracy with SOA when there were non-targets present and a slight increase followed by a decrease in accuracy with SOA when there were no non-targets. On invalid trials, there was no significant effect of SOA, nor any interaction of SOA and Non-targets. Thus, the main effect of Non-targets was to increase performance slightly on valid trials (overall means: with non-targets = 0.67, without non-targets = 0.65), but to decrease accuracy on invalid trials (with non-targets = 0.57, without non-targets = 0.61). To summarize, in both Experiment 1A and 1B there was a decrement on invalid trials in comparison to valid trials. In addition, on invalid trials, longer target durations led to less decrement, and there was little or no difference in accuracy as a function of SOA.

EXPERIMENT 2 To test the idea that a central cue requires longer to interpret than a peripheral cue, a study was conducted in which the location of the cue was varied. Cues were presented as in Experiment 1 , but no target appeared. RT to respond to a cue that indicated the location of a possible target was



721

recorded. In order to further generalize our findings, the arrow cues could appear at fixation (O"), at lo,or at 7",with the assumption that if a target were to appear, it would be at 6".Using these cue locations provided a direct test of cue locations used by Warner, Juola, and Koshino (1990). They found no difference between effects of central and peripheral cues in a location-cuing task. We suggested that their results were due to differences in the location of the central cues (Cheal & Lyon, 1991b), in that their central cue was not at fixation but was adjacent to it. Thus, observers could quickly determine the target location by the location of the cues, as they can with peripheral cues.

Method We tested 12 volunteers who work in the laboratory (9 men and 3 women, 19-64 years of age, with vision adequate for focus at 38 cm). The cue was an arrow (0.8"), presented for 16.7msec (as it is in the location-cuing task), followed by a blank screen. Observers responded on the computer keypad to indicate whether the arrow cue pointed towards a target location above, below, to right, or to left of fixation. In all cases, the arrow pointed to a target location. However, for cues at 1" and 7", it was not necessary for the observer to see the direction of the arrow, because the location alone indicated the correct implied target location. Each observer was tested on 96 trials for each of the three cue locations; trials with errors were repeated at the end of the block. Observers were instructed to be accurate, but to respond as quickly as possible.

Results Reaction time to respond correctly to a cue at fixation (mean of median RTs = 414 msec) was significantly longer than to either a central cue next to fixation (at 1"; 365 msec) or a peripheral cue (at 7";355 msec), overall ANOVA: F(2, 22) = 24.69, p < 0.0001; least-square difference for multiple comparisons: 0" vs. lo, t(l1) = 5 . 4 5 , ~< 0.0001; 0" vs. 7",(11) = 6.57,p < 0.0001.There was no significant difference between 1" and 7" conditions (t(l1) = 1.12, p = 0.27). Errors were few: 5.8% for the peripheral cue condition, and 5.7% for either central cue conditions. In order to determine whether RTs for central and peripheral cues varied considerably in distribution, histograms of the raw scores and cumulative graphs for the 0" and the 7" cue conditions were plotted. Although the variability was slightly greater for the central cue condition (SD = 44 msec) than for the peripheral cue condition (SD = 36 msec), the shape of the cumulative curves did not differ greatly (Figure 10). Primarily the central cue distribution was shifted to the right. The difference of approximately 60 msec between the peripheral cue and the cue at fixation does not account for all of the difference in the


722


500 800 REACTIONTIME (mtec)

lo00

1

w

FIG. 10. Cumulative distribution of RTs to indicate the location that was cued by central (at fixation) or peripheral cues in Experiment 2.

time course of the peripheral and central cue curves (peaks of 100 and 300 msec for peripheral and central cue, respectively, in the left graph of Figure 7). One cannot account for the more gradual effects of the central cue by proposing that cue interpretation time is much more variable for the central cue, inasmuch as the cumulative distributions (Figure 10) are very similar (except for a shift in means). Therefore, it appears that there may be an additional process needed to explain the slower onset of the precueing effects of the central cue. It is also of interest that the time to interpret the cue did not vary significantly between 1” and 7” cues. These data add additional support to our interpretation of the data of Warner et al. (1990), which was that their off-centre central cue could be “read” by location alone. Thus, the fact that they did not find the same differences between central and peripheral cues, as have other researchers (Cheal & Lyon, 1991a, 1991b; Jonides, 1981; Miiller & Rabbitt, 1989a), may be due, at least in part, to the placement of their central cue next to fixation.

EXPERIMENT 3 Three hypotheses suggested by the framework were tested in a comparison of valid and invalid trials with either a central cue or a peripheral cue (Figure 5D). These hypotheses were: (1) valid trials will differ due to the cue location, as they do when all trials are valid (Figure 7); (2) a short target duration may be too brief for successful discrimination of the target once attention has arrived at the correct location, resulting in poorer

723


FRAMEWORK FOR AlTENTlON

accuracy on invalid trials with either peripheral or central cues than on valid trials; and (3) there will be little difference on invalid trials as a function of cue type or SOA.

Procedure Three men (20-21 years of age) were tested in 24 sessions each (832 trials per session) in a four-alternative forced-choice discrimination of Ts. The Ts were constructed so that the same size of mask was used as for Experiments 1 and 2. In other words, if the four orientations of Ts were superimposed, they would form a plus sign the same size as the characters used in Experiments 1 and 2 (0.9O X 0.9"). They were presented at 6", for a mean target duration of 39 msec. For 4 blocks of each session (104 trials per block), the arrow cue was located at fixation (central cue), and for an additional 4 blocks of each session, the arrow cue was at 7" and pointed to the target location at 6". The two conditions were counterbalanced for order across sessions for each observer. In each condition, 80% of the trials were validly cued, and 20% were miscued. At the three locations without a target, 0 s were presented. All variables except the location of the cue were randomized within blocks.

ResuIts The data for valid trials clearly replicated our previous data for peripheral and central cues (compare Figure 11A with Figure 7) and provided new data for invalid trials in each cue condition. There was a decrement for I.o

3

A

I

8.

Ea

8 .75 0

z

Pa 0

B

.so

n .25 -

1

I 100

100

200

300

200

300

CUE-TARGET SOA (mrec)

FIG. 1 1 . A: Proportion correct as a function of cue-target SOA for four-alternative forcedchoice discrimination of Ts (Experiment 3 ) in conditions where cues were 80% valid (solid symbols) and 20% invalid (open symbols), randomized within blocks of trials. The cue was located 1" peripheral to the target location (peripheral; squares) or at fixation (central; triangles). B: Total attention resources (phi) as a function of SOA for the data in A.


724


invalid trials compared to the valid trials for all three observers, for each observer separately, and in either condition (Table 5). There were significant main effects of SOA and significant SOA X Cue Validity and Cue Type x Validity interactions, and the 3-way interaction for all three observers and for each observer separately. Cue Type and SOA X Cue Type interaction were significant for two of the observers, but not for the third. Although there were significant differences between observers, the data for the three observers indicated the same general pattern of results. Because of the significant interactions, it was useful to compute separate analyses of the valid and invalid trials and of the cue types. On valid trials, there were increases in accuracy with SOA and better performance with peripheral cues than with central cues (Figure 11A). There was also a more rapid rise in accuracy with SOA for peripheral cues than for central cues, as shown by the SOA x Cue Type interaction. For the invalid trials,the apparent similarity of the shapes for the two curves (Figure 11A) was not consistent across observers. The invalid trial TABLE 5

x2 (Hi-Log Linear Analysis) for

Experiment 3


df

3 Observers

cc

JG

CT

Validity

1 12 1 2 12 12 1 12

1399.0" 531.8* 294.7* 1220.7" 264.3" 188.3" 201.9* 87.2*

73.4" 59.1d 9.9h

1376.7* 289.6* 1012.6"

414.8" 315.2" 0.5

20.6 29Sh 5.7" 22.9"

298.8" 118.2" 275.2* 89.0"

85.4" 99.7* 42.7d 26.5h

SOA Cue Observers SOA x Cue Validity x SOA Validity x Cue Three-way interaction

Valid

SOA Cue

12 1 12

SOA x Cue

Validity

SOA Validity X SOA Valid trials: SOA Invalid trials: SOA "p < 0.05, 'p

1 12 12 12 12

692.0" 491 .7" 327.2*

Invalid

33.2' 30.4" 17.9

Central

Peripheral

240.8" 285.4'' 77.9 348.5* 14.5

1365.4* 503.8d 203.5" 670.8* 36.6'

< 0.01. ' p < 0.001, ' p < 0.0001.



725

curves were noisier than the valid trial curves, as there were only onefourth as many trials. There was, however, a significant decrease in accuracy with SOA, but no significant Cue Type x SOA interaction (p > 0.10). The analysis by cue type indicated that there were strong effects of Validity, SOA, and Validity X SOA interactions for both central and peripheral cues. Further analyses to understand these interactions revealed that there were strong effects of SOA for valid trials with both cues, and a decrease in accuracy with SOA for peripherally cued invalid trials. However, there was no significant effect of SOA for centrally cued invalid trials. Interpretation is difficult, because there was no SOA X Cue Type interaction for invalid trials, but there was an SOA effect for peripheral but not for central cues on invalid trials. To explore further the use of attention in these tasks, the effects of each type of cue on total attention resources over the entire display were examined. In the location-cuing experiments, it is usually assumed that improved accuracy with SOA is a function of greater attention resource investment. A method of relating accuracy to resources was formalized in optimal search theory (Koopman, 1956, 1957), which was developed to prescribe the optimal distribution of effort in naval search tasks involving a small target in a large area. Although Koopman (1956) developed this method for detection tasks, it has been applied equally well to discrimination tasks (Shaw & Shaw, 1977). It is possible to use the analysis described in Appendix 1 on these data because all variables were tested within observers and the short target duration was constant across conditions for each observer. Although, on a given trial, the target appeared at only one location, the target appeared at both cued locations and non-cued locations on different trials for each condition. Figure 11B shows the total capacity, called phi, by SOA for the peripheral cue and the central cue conditions. This figure shows the mean for the three observers. The relative amounts of phi for the two conditions were not consistent across observers. However, for the peripheral cue phi peaked and then dropped at long SOAs for each observer. In contrast, for the central cue condition, phi continued to increase at long SOAs for each observer. Implications of these results are discussed below.

DISCUSSION The framework proposed (Figure 5) provides a working hypothesis as to how attention is incorporated in the location-cuing task. This framework facilitates the interpretation of data collected under many different conditions. Many of the hypotheses arisingfrom the framework were supported, either by published data or from the results of experiments presented here.


726


In speculating about how our temporal framework fits a theory of the spatial characteristics of attention, we have combined a gradient view of the allocation of attention (LaBerge & Brown, 1989) with the timing aspects of a gating filter (Reeves & Sperling, 1986) and have suggested that the opacity of this gradient filter need not be evenly distributed across the visual display.

Effects of Cue Validity The data from Experiment 1 support the first hypothesis suggested by the framework-that accuracy will be lower on invalid trials than on valid trials for ail types of targets, provided a short target duration is used. There was a strong effect of cue validity on (1) discriminations between two colours with small differences in hue (green vs. yellow), (2) discriminations between two colours that differed greatly (red vs. blue or cyan vs. yellow), and (3) discriminations in luminance. Thus, even for “feature” targets, such as colour and luminance, where there were small effects of precuing, accuracy was poorer on invalid than on valid trials. Very short target durations were used in these experiments, which implies that these targets may require little time for allocation of attention. Directing attention away from the target may have effects similar to shortening the target duration. Nevertheless, as target durations were so brief, “shortening” them further seemed to have large effects. These data also support the second hypothesis generated by the framework-that there will be less decrement on invalid trials if a longer target duration is used (right graph in Figure 8). This is true even if the task is more difficult (i.e. poorer performance on valid trials). With a longer target duration, there is more time for changing the permeability of the attention filter to increase the flow of information necessary for discrimination of the target, so that performance is improved for invalid trials. Furthermore, the data add support to the conclusion that the amount of improvement with SOA, thought to indicate the need for attention, is little affected by differences in discrimination difficulty due to different target durations. The third hypothesis-that there will be little or no difference in accuracy on invalid trials as a function of SOA-was also supported. There was no increase in accuracy with SOA in any of the invalid conditions and little or no decrease with SOA. Although there was a small decrease in accuracy on invalid trials for the red/blue discrimination, this change only represented a drop from O-msec SOA because there was no significant change from 17-msec to 234-msec SOA, ~ ~ ( 1 = 1 )17.1, p = 0.10. It is not possible to come to firm conclusions based on the data at O-msec SOA because we have not been able to rule out some cueharget interaction when they are presented simultaneously (Cheal, 1993; Lyon, 1990).



727

An effect that was not anticipated was the decrease in accuracy with long SOAs for peripherally cued invalid trials in Experiment 3. This may be related to a hypothesized decrease in capacity at long SOAs with a peripheral cue (as shown in Figure 11B). Our data support the suggestion of the framework that on valid trials the attention filter begins to open at cue onset, but for invalid trials, the attention filter at the target location can only begin to open after target onset. Therefore, because the cue-target interval is completed before the target appears at a non-cued location, performance at non-cued target locations should vary little as a function of SOA. It would be interesting if we could compare our results for invalid trials with existing R T data. Unfortunately, there is a problem in trying to compare the flat curve for invalid trials in accuracy data with RT data (e.g. Posner & Cohen, 1984, Figure 32.2). In their figure and others that are similar, invalid trial RTs decrease with SOA, because they are influenced by well-known alerting effects. However, the data in the two paradigms are consistent in that performance is poorer on invalid than on valid trials (for SOAs up to 200 msec in the R T data). This consistency could indicate that attention needs to be allocated to the target location when the cue is found to be invalid even for “feature” targets. Our data differ, however, from the data presented by Muller and Findlay (1988). They found substantial increases in accuracy with SOA on invalid trials when observers had to match a T with a previously presented T. Although they used much longer SOAs than we did, they found an increase in performance on invalid trials with SOAs as short as 75 msec. There were numerous other differences in the methodology between these two paradigms. One of the major differences may have been that Muller and Findlay (1988) used plus signs for the non-targets, whereas we used 0 s . As stated above, we chose the 0 s because they can be discriminated preattentively from the T , whereas the T and plus sign discrimination probably requires attentional resources. Other important differences were (1) the va1id:invalid proportions of 80:20 versus 5050; (2) discrimination rather than matching; and (3) large differences in timing. Suppose that the increase with SOA for invalid trials found by Muller and Findlay (1988) were due to the fact that their non-target character was more similar to the target than was ours. What would the framework predict when non-targets and targets are very similar? In that case, the line for target detection (Line 4b, Figure 5B) would have a delayed and more gradual rise, followed by a delayed and more gradual opening of the gradient filter. Now, if target durations were very brief, one would expect performance to be very poor. However, Muller and Findlay (1988) used longer target durations in order to obtain 75% overall accuracy. These longer durations would allow time for performance to improve as the filter continued to open during the target duration. But why would performance


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vary as a function of SOA? One possibility is that attention may have moved quickly to the cued location, but as the interval before the target appeared increased, attention may have begun to spread more widely across the field in anticipation of an invalid trial. This might happen when the probability of a valid trial is less. Thus, with longer SOAs there may have been more attention at each possible target location when the target appeared than with shorter SOAs. An effect that was not suggested by the framework is that there would be less effect of cue validity if no non-targets were present (Figure 9). However, this result is consistent with the effect of target duration. Here, again, accuracy should improve consistently across SOAs because the target is easier to find, which allows more of the target duration to be available for discrimination rather than for locating the target. On valid trials, the presence of non-targets in the display did not affect the small improvement over short SOAs. However, there was greater decrease at long SOAs for the condition without non-targets. This may be because with non-targets, the first indication of an invalid trial is the appearance of an 0 at the cued location. Without non-targets, the first indication of an invalid trial is the appearance of a target at a non-cued location. In this case, it may be that observers are more likely to move attention as the SOA increases in order to search for the target. Overall performance on invalid trials with non-targets was worse than when there were no non-targets in the display. Similar effects of non-targets have been found previously in a RT task, in a target location task, and on same/different matching judgements (Eriksen & Yeh, 1985; Muller & Findlay, 1988, Experiments 1 and 2; Muller & Rabbitt, 1989b). It is possible that when there are no non-targets in the display, less time is needed to detect the target on invalid trials (Muller & Rabbitt, 1989b). The appearance of the target would be the only sudden onset of luminance change in the display at that time, which could open the attention filter at the target automatically. Furthermore, the tendency to move attention as the SOA increases is likely to be greater when there are no non-targets present than when there are.

Evidence Re the Analogue Movement of Attention The above analysis of invalid trials is also relevant to the continuing controversy over how attention is moved through the visual field. The spotlight metaphor would predict that attention begins at fixation and moves to the cued location. Following an invalid cue, attention must then realign to the target location, which would result in a decrease in accuracy with SOA that corresponds to the increase found on valid trials. This is because the longer the SOA, the further the spotlight has moved towards the cued location and away from the non-cued location. However, this predicted



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decrease in accuracy on invalid trials was neither large nor consistent in the data reported here. Even so, these data do not rule out this prediction of the spotlight metaphor, because there was a small decrease in accuracy on invalid trials in some experiments. With a gradient-filter metaphor, there is no reason to expect a rapid decline on invalid trials as a function of SOA. This is because, according to this metaphor, when the actual location of the target is detected, an attention filter immediately begins to open at that location. The speed of this process does not depend on the amount of activity at the cued location as long as total capacity is not exceeded. On the other hand, if a capacity limitation is assumed, a decrease with SOA on invalid trials is also consistent with the gradient-filter metaphor. Thus, this aspect of the data does not differentiate between the two metaphors. However, there is considerable evidence against the spotlight metaphor in the literature. We addressed the hypothesis of whether attention moves in an analogue fashion in a study in which the time course of attention effects as a function of eccentricity was measured directly (Cheal & Lyon, 1989). We found that there was no difference in this time course for targets (Ts) at 2", 6", or lo", provided that the targets were scaled according to the cortical magnification factor so that they were equally discriminable. A difference in the time course would be predicted by any spotlight model in which attention moves at a fixed rate. This is a component of even quantitatively specified spotlight models such as the one that LaBerge and Brown (1989) compared to their gradient model. Our data clearly showed that there was no difference in the time course as a function of distance, but other researchers continue to find differences as a function of distance in measures interpreted to represent movement of attention. For instance, Pearson and Lane (1990) found a Distance X SOA interaction in one experiment and not in another. However, they did not correct for the difference in eccentricity (and thus the differences in visual sensitivity), and the displays in the two experiments differed. If one display were more difficult to discern, so that vision was closer to threshold, then the W-versus-0 discrimination could be more difficult at 8.2" than at 2", and performance would be poorer. In the other experiment, where the display was more discriminable, the eccentricity did not affect the discrimination. In another case, Egly and Homa (1991) found that decrements on invalid trials were associated with distance, even though they controlled for effects of eccentricity. In order to help determine why Egly and Homa's data should imply the opposite view of allocation of attention from ours, some of our data discussed above were reanalysed to determine whether there would be a cue location to target location distance effect. Our data were collected with methods similar to those of Egly and Homa. Major differences between the tasks for Cheal et al. versus Egly and Homa, respect-


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CHEAL,LYON, GOTTLOE

ively, were: (1) discrimination accuracy versus RT measures; (2) four target locations at points of an imaginary diamond versus four target locations at points of an imaginary square; (3) non-targets versus no non-targets at possible target locations that did not have a target; (4)targets 6" from fixation versus targets 4" from fixation; (5) va1id:invalid trials were 8020 versus 70:30; (6) various different targets versus "R" and "L" targets. After our further analysis, it was learned that in Experiment l A , above, (red/blue, cyan/yellow, green/yellow) and in Experiment 1B (luminance discrimination with and without non-targets), there were no differences between targets in locations adjacent to the cued location (8.5' distance) and targets opposite to the cued location (12" distance). However, in the data for four-alternative forced-choice discrimination of Ts and lines of different orientations (Figure 5 ) , accuracy for the targets that were 8.5" from the cued location was significantly better, proportion correct = 0.43, than accuracy for the targets that were 12" away, proportion correct = 0.39; ~ ' ( 1 )= 8.83, p < 0.01. There was also a significant effect of distance in Experiment 3, ~ ' ( 1 )= 3.97, p < 0.05, but the difference was in the opposite direction (8.5", 0.49; 12", 0.51). Thus, in our task, the results were not reliable. Even if only the data from 117-msec SOA were examined (the SOA used by Egly & Homa, Experiment l), the effect of an increase in accuracy with distance was found only for the data for Ts and lines in Figure 5, and not for the data for Ts in Figure 1 l A . One important aspect of the methods used in our laboratory is the use of non-targets at locations that do not contain a target. It is possible that in the Egly and Homa case, where there were no non-target characters, the sudden onset of the target automatically called attention to that location, so that no search for the target was needed. However, when there are distractors in the non-target locations and the target appears at a non-cued location, the target cannot elicit attention. If we postulate that search will traverse the shortest overall distance, then it might be assumed that search would move either clockwise or counterclockwise around the circle and proceed to one of the nearest two locations. According to this assumption, a spotlight should move the same distance on average to find the target at 8.5" as the one at 12". Consider that for every 8.5" target that is encountered first in a clockwise (or counterclockwise) search, there is a corresponding 8.5" target opposite to it on the circle that would be encountered after the 12" position had been searched. Thus, the average time to find the target should be the same for targets at 8.5" or 12" from the cued location. Assuming this path of the spotlight motion, longer RTs to the further target are not consistent with a spotlight model. Of course, other possible assumptions about the search path of the spotlight might predict a distance effect. Our point is that a spotlight model does not guarantee a distance effect in this context.



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Now, let us instead postulate a distribution of attention analogous to a gradient filter that has increased permeability at the cued location after the cue is detected (as illustrated in Figures 2B and 3A). After the target appears (Figure 3B), it could take the same amount of time for an attention filter to begin to open further at the target location for all three non-cued locations, but the permeability of the attention filter at the target location would vary with distance from the cued location at the time of target onset. Thus, there would be different amounts of information flow (Figure 2), depending on the location. Therefore, the greater the distance from the target, the less permeable the attention filter would be, and discrimination accuracy would be reduced. These effects would be seen as poorer performance at greater distances in our task and longer RTs at greater distances in the Egly and Homa task. Thus, both the gradient-filter and the spotlight metaphors could explain a distance effect when size of the display is either constant or is varied trial-by-trial. In contrast, when targets are presented at different eccentricities in different blocks of trials, there may be no differences on invalid trials as a function of distance (Remington & Pierce, 1984), because the gradient of attention may be determined for each experimental condition. There may be many factors that affect the gradient of attention, including interobserver differences in allocation strategy, which may account for the unreliability in the distance data. Note that the distance effect was found for discrimination of both Ts and line orientation. Thus, the lack of an effect with colour and luminance cannot simply be attributed to the use of “feature” targets. In addition, if the probability of the cue being correct is less than loo%, attention may be spread, with a narrower higher peak at the target location for high validity than for low validity conditions, and higher peaks at non-cued locations for low-validity than for high-validity conditions. The distance effect in our data may have been less reliable because there were 80% valid trials, whereas in Egly and Homa’s work there were 70% valid trials. Egly and Homa (1991) interpreted their results as being due to an analogue movement of attention, which, they said, occurs with discrimination but not with detection tasks. However, no analogue movement was found in other discrimination tasks (Cheal & Lyon, 1989; LaBerge & Brown, 1986; Murphy & Eriksen, 1987). Therefore the more parsimonious conclusion is that attention does not move through space like a moving spotlight, but that effects of distance can be found due to distance-related gradients in the field of attention filters. If there is no difference in the time needed to move attention dependent on distance, as our work (and others’) suggests, then the advantages of the spotlight metaphor are decreased, and alternative metaphors are more attractive.


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Central versus Peripheral Cues, Valid Trials The framework above (Figure 5C) also provides some support for conjectures about differences in performance as a function of the location of a precue. A number of researchers found that a precue near a target location resulted in more rapid improvement in detection or discrimination in comparison to a precue at fixation (Cheal & Lyon, 1991a, 1991b; Jonides, 1981; Miiller & Findlay, 1988; Miiller & Rabbitt, 1989a). First, as expected, there was a more rapid onset of improvement with SOA for the condition with peripheral cues than for that with central cues. However, if the SOA were long enough, there was as much improvement with central cues as with peripheral ones. These results were replicated in another experiment (right graph of Figure 7 - C h e a l & Lyon, 1991b), consistent with other data in the literature (Hawkins et al., 1990). It might have been asserted that the allocation of attention prior to the cue could be different for central and peripheral cue conditions. Because it is necessary to “read” the central cue, attention may be concentrated at fixation (as in Figure 1B). However, the similarity of accuracy at 0-msec SOA for central and peripheral cue conditions (Figure 7) suggests that attention may be distributed the same way during initial fixation (as in Figure 2A). At least three different processes could be responsible for the differences between the central cue and peripheral cue curves in Figure 7. (1) As shown in Experiment 2, there is a difference within the first 60-msec SOA in the time needed to interpret the cue. This mean difference between interpretation of a central and a peripheral cue leaves unexplained another 100 msec or more in the time for performance to approach asymptote (Figure 11A; Cheal, 1993; Cheal & Lyon, 1991a, 1991b). (2) There may be a difference in the automaticity of responding to the cue that is shown by the different slopes up to 100-msec SOA. The more automatic elicitation of attention by a peripheral cue may result in a fast automatic or transient response, followed by the slower controlled or sustained component (Miiller & Findlay, 1988; Nakayama & Mackeben, 1989). The voluntary allocation of attention to a central cue would result only in a sustained component. The peripheral cue may result quickly in a maximum permeability of the attention filter (as in the second surface in Figure 2), whereas the central cue may take considerably longer. The more permeable the filter, the faster is the flow of information, which allows a better percept. (3) There may be a difference in inhibitory processes that results in the decrease in the slope for peripheral cue, valid trials at long SOAs. An example of such a process is inhibition of return (Posner & Cohen, 1984). Inhibition of return is considered to be a central nervous system inhibition that allows attention to be most usefully allocated. With a peripheral cue,


FRAMEWORK FOR AlTENTlON

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once asymptote is approached, performance may decline on valid trials, as it does in Figures 6, 7, 9, and 11A. This does not occur with a central cue, at least for SOAs up to 500 msec (Figure 7). Our analysis of total attention resources (Figure 11B) does not differentiate between explanations of inhibitory processes or a transient component of attention. Total resources (phi) declined at long SOAs for the peripheral cue, but not for the central cue. The decline observed in the peripheral cue condition cannot be explained by assuming that attention moved to a different location (or locations) when the target did not appear quickly, as merely moving the focus of attention should not affect total capacity. Inasmuch as the capacity decreased for those trials with a peripheral cue, it is more likely that the decrease in accuracy at long SOAs is a reflection of the end of a transient increase, or an inhibition, in the flow of information rather than a shift in attention. Of course, if some attention is drawn back to the fixation spot due to instructions to maintain fixation (as has been suggested-Nakayama & Mackeben, 1989), this would not be measured by our analysis. For the central cue condition, however, there is no decrease in capacity with SOA, which supports the supposition that a central cue elicits a controlled or sustained component of attention (Muller & Findlay, 1988; Nakayama & Mackeben, 1989) and no inhibitory processes (Posner & Cohen, 1984). In fact, accuracy tends to increase at long SOAs, consistent with the continuing voluntary allocation of attention.

Central versus Peripheral Cues, Invalid Trials Results of Experiment 3 also support our hypothesis that there will be a decrement on invalid trials in comparison to valid trials for both central and peripheral cues. Thus, as implied by the framework (Figure 5D), attention filters are only slightly permeable at non-cued locations at target onset (second surface in Figure 2). Even though the flow may continue to increase during the target duration, with short target durations the total amount of information flow will be less than at the cued location. Another hypothesis derived from the framework was that responses on invalid trials would not differ as a function of cue type or SOA. However, performance was significantly better for central than for peripheral cues. The observer knew which type of cue to anticipate, inasmuch as trials were blocked by cue type, which suggests the possibility that observers distributed attention differently in the two cue conditions prior to onset of the cue. However, an unequal initial attention distribution is unlikely, because there was approximately equal accuracy at each possible target location at O-msec SOA (Figure 11A). A second possible explanation for the central cue/peripheral cue difference on invalid trials is that although attention is initially distributed the


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same way for the two conditions, the slower opening of the gradient filter at the cued location with the central cue may be associated with slower closing at the non-cued locations. Therefore, when the target is detected, the filter is partially open, so that performance is better than with the peripheral cue. However, if the difference in closing at invalid locations is due solely to the difference in opening at valid locations, one would expect that at very long SOAs accuracy for central and peripheral cues would be the same on invalid trials at the same SOA at which the valid trials are equivalent (Figure 11A). That does not occur. A third possibility is that the filter at the cued location opens at different rates for central and peripheral cues for invalid trials, as is consistent with the data for valid trials (remember that valid and invalid trials do not differ until the target appears). However, if the filter at the cued location does not affect detection of the target, there should be no difference between central and peripheral cues on invalid trials. And, as stated above, if the filter opening does affect target detection, then the invalid lines should converge at long SOAs, but they do not. Therefore, the above possibilities are unlikely. However, it is possible that the centraYperipheral cue differences on invalid trials are somehow related to the total capacity of the filter. As shown in Figure l l B , capacity can be maintained from 83 msec up to 300 msec in the central cue condition, but not in the peripheral cue condition. An unexpected finding was that performance on invalid trials differed as a function of SOA. On further analyses, it was found that this effect was primarily due to an unexpected decrease after long SOAs on peripherally cued invalid trials. There are at least two non-exclusive possible explanations for this decrease. One is that after the onset of the cue, when the attention filter has become more permeable at the cued location, the attention filters close slightly at one or more non-cued locations. Therefore, as the SOA increases in duration, the flow of information at other locations may decrease. The rate of information flow at the non-cued locations may vary as a function of their distance from the cued location in agreement with an irregular gradient field spreading from the cued location (Figure 1B). Thus, after a long SOA, the peak of attention prior to target onset could be concentrated at the cued location, with the least amount at the furthest non-cued location (Figure 1C). A second possible explanation is that after very long SOAs there could be a decrease in the flow of information at the cued location (as suggested by a decrease in accuracy on valid trials) due to a general decrease in attention resources following a peripheral cue (Figure 11B). For either of these reasons, there may be a slower rate of flow of information at non-cued locations with long SOAs, so that in some cases performance falls on invalid trials. On invalid trials, for central cues, there was no obvious change in accuracy with SOA. As attention is voluntarily cued with a central cue,



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attention at the non-cued locations may continue with little change, and therefore there would be no decrease with SOA for invalid trials, and overall proportion correct would be higher for central cue, invalid trials than for peripheral cue, invalid trials. Of course, if longer SOAs were used, one might find changes with SOA for the central cue, invalid trials. It is possible that a decrease in accuracy on invalid trials occurs only at SOAs longer than those at which peak performance is found (and accuracy begins to decline) on valid trials (re Figure 11A).

CONCLUSIONS This paper presents a framework for understanding how attention facilitates responses during a location-cuing trial. The framework was used as a heuristic device for suggesting hypotheses about the role of attention in the location-cuing paradigm. New experiments presented here focus on some problematic findings concerning (1) the decrement in performance on invalid trials-ven for targets that seem to need little attention-and (2) the effects of the location of the precue. We have argued that there are several reasons (such as the gradient-filter metaphor) to prefer a gradient-like view of attention over a spotlight analogy. A key component of a spotlight metaphor is that attention moves through visual space, but our results and those of others argue against an analogue movement of attention. A gradient-filter analogy is consistent with more of the data. Its flexibility not only allows attention to be first in one place and then in another, but also a gradient can vary in size and shape and can grow rapidly or slowly (consistent with the rapid peripheral curves and the slower central curves in Figures 7 and 11A). A metaphor that combines the spatial aspects of a gradient with the temporal aspects of a variable permeability filter provides a better conceptualization of the role of attention in this paradigm. Furthermore, the framework suggests some additional questions that can be tested empirically.

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APPENDIX 1 Derivation of Phi (Total Resource Allocation) The relationship between effort (resources) expended and the detection probability can be described as: b(x) = 1 - e-*p(x)

(1)

where b(x) is the probability of detection at position x, and cp is the amount of resources applied to the task at position x (Koopman, 1957; Shaw & Shaw, 1977; Stone, 1975). Equation (1) is the familiar negative exponential or exponential saturation function. Loftus, Duncan, and Gehrig (1992) employ an analogous formula to relate discrimination performance to stimulus duration. Shaw and Shaw (1977) use Equation (1) to relate discrimination to resource allocation. Obtained accuracy can be related to resources expended by solving for cp: cp(x) = -In[l - b ( x ) ]

(2)

As obtained accuracy is equal to expected accuracy (probability of discrimination) for sufficiently large numbers of trials, Equation (2) enables us to determine the amount of resources (cp) applied to the discrimination task at a single location, given a value for obtained accuracy at that location. Accuracy for valid (cued location) and invalid (non-cued locations) trials can thus be converted to values for cp (allocated resource), and the cps can be summed over all four locations (cp for the cued location and 3 times cp for the three non-cued locations) to derive a value for total resource allocated, where

+

4

=

2%

(3)

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