Visual Search for Conjunctions of Motion and Form - University of

VISUAL COGNITION, 1999, 6 (3/4), 385 –408

Visual Sear ch for Conjunctions of Motion and Form: The Efficiency of Attention to Static ver sus Moving Items Depends on Practice Hermann J. Müller and Adrian von Mühlenen Birkbeck College, University of London, UK Visual search for some motion–form conjunctions can be performed in parallel. Yet, if the target is easy to discriminate from the nontargets (target line tilted 45° from the vertical), search can be easier for a moving than for a stationary target. Driver and McLeod (1992; Berger & McLeod, 1996) took this asymmetry to argue that gross aspects of form discrimination are performed within a motion filter that represents only the moving items, whereas discrimination of stationary items (and all fine discrimination) relies on a static formsystem. However, recent (unsuccessful) attempts to replicate the asymmetry (Müller & Found, 1996; Müller & Max well, 1994) suggested that it may occur only early during task performance, due to participants having difficulty keeping the moving items out of the search for a stationary target (but not vice versa). This was confirmed by the present study, which investigated the effects of practice on search among the moving and stationary subset of items. The results suggest that attention to the stationary subset is difficultinitially because participants cannot efficiently compensate for the natural bias of the motion filter to pass the moving items (rather than filter them out). This ability improves with practice. Thus, there is no fixed limit to performance with stationary targets and, consequently, no need to assume that any form discrimination is performed within the motion filter.

Recently, there has been a controversy about how humans search for conjunctions defined by motion and form (Berger & McLeod, 1996; Driver & McLeod; Müller & Max well, 1994; Müller & Found, 1996). All available data agree that search for motion–form conjunctions, such as an upward-moving line tilted 45° Requests for reprints should be sent to H.J. Müller, Institut für Allgemeine Psychologie, Universität Leipzig, Seebrurgstraße 14/20, D-04103 Leipzig, Germany; Email: mueller@ psychologie.uni-leipzig.de This research was supported by Science and Engineering Research Council grant GR/H/54966 and a Royal Society research grant to H.J. Müller, and by a S wiss National Fund fellowship to A. von Mühlenen. We would like to thank Jon Driver, Jim Enns, and one anonymous reviewer for their helpful comments on an earlier draft of this paper.

Ó 1999 Psychology Press Ltd

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from the vertical amongst upward-moving vertical bars and stationary 45° tilted lines (see Figure 1), can be performed in parallel. However, what is controversial is Driver and McLeod’ s (1992) finding that the search rates are dependent on both the form discriminability of the target (relative to the nontargets within its set) and whether the target is a member of the moving or the stationary items (see also Berger & McLeod, 1996). In particular, they reported an asymmetry reversal (i.e. cross-over interaction) such that search for a salient tilted line target (45° from the vertical amongst vertical lines) was easier when it was present in the moving set of items rather than the stationary set. Conversely, when a finer discrimination was required to detect the target (9° tilt), search was easier when it was present is the stationary set rather than the moving set. The explanation proposed by Driver and McLeod (1992) expands on their original notion of a motion filter (McLeod, Driver, & Crisp, 1988). According to their account, two components of the visual system are involved in the search of displays with moving items: The motion filter and the stationary form system. The motion filter is specialized for segregation moving from stationary items; it represents only moving stimuli, but has a relatively poor representation of their orientation and form. Conversely, the stationary form system has a precise representation of stimulus orientation and supports accurate discrimination between all forms; but it is poor at filtering by movement, representing moving items to some extent as well as stationary items. The asymmetry reversal can then be explained as follows. Detection of a moving conjunction target can be accomplished efficiently within the motion

FIG. 1.

Example display of search for a moving line target tilted 45°, from the vertical.

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filter when the form discrimination required is easy. But when the discrimination is hard, the stationary form system (that has a poor representation of moving items) has to come into play. In contrast, detection of a stationary target is comparatively inefficient when the discrimination is easy because the stationary form system has difficulty keeping moving items out of the search. But when the discrimination is hard, this difficulty is outweighed by the system’s accurate representation of form. On the basis of neuropsychological evidence, McLeod, Heywood, Driver, and Zihl (1989) suggested that the motion filter resides in cortical area MT (or V5) where neurones are known to be especially responsive to motion, but only broadly orientation tuned. However, work by Müller and Max well (1994) has cast doubt on the existence of Driver and McLeod’ s (1992) asymmetry reversal. Müller and Max well performed a number of experiments comparing targets of different discriminability (including 45° and 9° tilted lines), but found no evidence of an asymmetry between moving and stationary search in terms of positive RT function slopes, and hence no asymmetry reversal (cross-over interaction). One possible cause for the discrepancy between the two studies was the horizontal display density, which was lower in Müller and Max well’s experiments. This parameter was systematically investigated by Berger and McLeod (1996) and Müller and Found (1996). Whereas Berger and McLeod reported an asymmetry reversal with high-density displays (as used by Driver and McLeod), but not low-density displays (as used by Müller and Max well), Müller and Found failed to find any effect due to varying display density. Thus, it remains an open question why McLeod and his colleagues have consistently found an asymmetry reversal (at least with high display densities), whereas we have failed to observe such a pattern. One other difference between the two sets of studies was that our participants were highly practised: All participants were familiarized with the task in a pre-experimental session of some 480 to 960 trials; moreover, the majority of the participants took part in several motion–form conjunction search experiments. In contrast, the participants of McLeod and his colleagues received 50 (Berger & McLeod) to 100 (Driver & McLeod) practice trials under each (blocked) set size condition, which were immediately followed by 50 experimental trials. It is possible, therefore, that our participants performed optimally under conditions that provided difficulty for the relatively unpractised participants of McLeod et al.— in particular: Search for stationary 45° targets and moving 9° targets at high display densities and all search conditions at low densities (see Figure 2). At low densities, Berger and McLeod’ s participants exhibited search RT function slopes that were twice as steep, on average, as those of the participants of Müller and Found— so their participants found the low density conditions rather difficult. In fact, Berger and McLeod increased the display duration from 2150msec (high density) to 4000msec (low density), presumably so that their participants could complete searching the 9° target displays within the time

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FIG. 2. Positive search RT function slopes for moving and stationary 45° and 9°, tilted targets in the studies of Müller and Found (1996), and of Driver and McLeod (1992) and Berger and McLeod (1996). The slopes for (a) high and (b) low horizontal display densities (i.e. small, 12.8° and large, 20.8° display extensions), are given.

available. At high densities, the participants of McLeod and his colleagues performed more efficiently, achieving search rates within a broadly similar range to our participants. Yet there were two conditions which they found harder than our participants: (1) Their main difficulty was with stationary 45° targets (12msec/item search slopes on target present trials) relative to moving 45° targets (4msec/item); our participants showed fast search rates in both conditions (5.5msec/item, on average). (2) The participants of McLeod and his colleagues experienced difficulty with moving 9° targets (22msec/item) relative to stationary 9° targets (15msec/item); again, our participants performed relatively efficiently in both conditions (17.5msec/item, on average). We hypothesize (following Müller & Found, 1996) that the cross-over interaction, especially the advantage for moving over stationary 45° targets, found by McLeod and his colleagues is characteristic of task performance during early stages of practice. In particular, early on, participants show poor performance with stationary 45° targets — because they have difficulty keeping the moving nontargets (whatever their orientation) out of the search of the stationary items. The difficulty arises because of an inherent bias of the motion system to pass, rather than filter out, the moving items. This bias can be compensated for by a top-down influence that requires practice to become fully effective. The finding of an initial disadvantage with stationary 45° targets that disappeared during the course of practice on the task, would be at odds with Driver and McLeod’ s (1992) account. Their account (implicitly) assumes that there is a fundamental limit to performance with such targets: Since stationary items

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are not represented within the motion filter, their orientation cannot be discriminated by this system (which is assumed to be sensitive to gross aspects of visual form); rather, performance depends on the stationary form system, which is always prone to interference from the moving items. However, if there were no fundamental limit to performance with stationary 45° targets, there would be no need to assume that any form discrimination is performed within the motion system. Note that the finding of the reversed asymmetry with 9° targets, with moving conjunction search being harder than stationary conjunction search, is not controversial. The disadvantage for moving 9° targets could simply be due to the discrimination of such targets from vertical lines being particularly affected by the noisy representation of the moving items’ form, that is loss of visual quality — due to reduced luminance contrast, retinal smearing, etc — relative to stationary 9° targets. Although the quality of moving 45° targets is likewise reduced, this should have less effect on discriminability given the large orienta1 tion difference to the vertical.

EXPERIM ENT 1 Experiment 1 sought to address the discrepancy between the studies of McLeod and colleagues and Müller and colleagues by systematically examining the effects of practice on search for moving and stationary 45° targets, using a display density similar to that at which McLeod and his colleagues observed an advantage for moving over stationary conjunction search. Separate groups of participants searched for either a moving or a stationary target for a total of eight experimental sessions conducted on two consecutive days. If experience with the task (practice) is the crucial factor for the discrepant findings between the two sets of studies, it was expected that stationary conjunction search would be more difficult (in terms of search RT function slopes and error rates) than moving conjunction search early on, but that the performance indices would converge during the course of practice.

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The feature search data of Driver and McLeod (1992) under the 45 ° and 9° conditions are consistent with this account. For stationary search, the search rates increased from 0[1] to 2[6] msec/item; for moving search, the slopes were steeper and increased from 1[3] to 4[12] (positive[negative] responses). The interaction is apparent in both the positive and negative responses. Note that the feature search data probably underestimate the magnitude of the interaction because there is no need to filter by motion (or its absence). In fact, the saliency of the target may be increased by computing the orientation contrast to all display items, rather than merely a subset of (moving or stationary) items (e.g. Cave & Wolfe, 1990; Wolfe, 1994). See also Experiments 2 and 3 in von Mühlenen & Müller (submitted) for a demonstration of a visual quality loss for items moving at a speed above 1.5° per second.

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M ethod Participants . Fourteen participants, recruited from the Birkbeck Psychology subject panel, took part in the Experiment 1. None of them had any prior experience with laboratory visual search tasks. Eight of the participants were males and six females; their ages ranged from 19 to 35 years; all had normal or corrected-to-normal vision; their payment was £4 per hour. Pairs of participants matched for sex and (as well as possible) age were formed, and members of each pair were allocated randomly to the moving or stationary search conditions. Apparatus. Stimuli were presented on a Tektronix 608 monitor cathode ray tube (CRT) with a (very fast-decay) P15 phosphor. The CRT was driven by a DELL 433/M computer through an Interactive Electronics Systems oscilloscopic point plotter (Finley, 1985). The laboratory was dimly illuminated to prevent reflections on the CRT, and the brightness of the stimuli was adjusted to ensure that moving items did not leave a visible persistence trail. Participants used a hand-sized response keypad to initiate blocks of trials and to make responses during trials. Participants viewed the display from a distance of 33cm (with head position maintained through the use of a chin rest). Stimuli. The total screen area was 22.3° × 16.2° of visual angle, and the maximum display area (with 24-item displays) was 12.8° × 11.3° (giving a horizontal display density of 1.88 items/degree). Displays contained either 8, 16, or 24 line stimuli (set size) half of which were moving and half stationary. The size of the stimuli was 0.62°. The moving items moved upwards at a constant speed of 2.8°/sec. The position of the moving items was incremented every 10msec, giving the impression of smooth continuous movement. Displays were presented for a maximum time of 2550msec unless terminated by a participant’s response. The display was subdivided into 24 (equally wide) adjacent vertical tracks. Stationary and moving items were placed on randomly interleaved tracks, and displays were dense in the sense that items occupied directly neighbouring tracks. When the number of items in the display was 24 (the maximum set size), each track contained a stimulus (with stationary and moving items randomly distributed across tracks). When the set size was less than 24 items, the stimuli were placed randomly in adjacent tracks (i.e. each item was flanked by two other items except the two marginal items to the left and right). With set sizes less than 24 items, the left-most stimulus position was varied randomly within a range that permitted all the items to be displayed. The vertical positioning of stimuli was determined randomly within in the constraints that the stationary items were spread over the full vertical display extension and the moving items

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were initially placed towards the lower half of the display (to prevent the uppermost items from scrolling off the display during a trial). In moving search, half the display items were stationary lines tilted 45° from the vertical; the other half consisted of (upwards) moving vertical lines except for one 45° line (the target) on target-present (positive) trials (see Figure 1); on target-absent (negative) trials, all moving items were vertical lines. In stationary search, the identities of the items in the (stationary) target and (moving) nontarget sets were reversed.

Design and Procedure. There was a total of 16 experimental conditions: Search condition (moving, stationary) × day (1, 2) × session (1, 2, 3, 4); each session consisted of 240 (recorded) trials, 40 trials for each target (absent, present) × set size (8, 16, 24 items) combination. Within a session, trials with or without a target and with varying numbers of items were presented in randomized order. Each participant performed eight searches for either moving or stationary targets (between-subject variable). These eight searches were carried out over two consecutive days, with four sessions per day and a 10-minute break between sessions. Before the first search, participants were given 10 unrecorded practice trials in the presence of the experimenter, to ensure that they understood the instruction. Further, sessions were subdivided into blocks of 40 trials, with a short break between blocks. Each block started with five unrecorded warming-up trials. Participants were instructed to make a target-present or -absent response as quickly and accurately as possible. When participants made an error on a trial, a computer-generated “beep” alerted them to the mistake.

Results Figures 3 and 4 present the (correct) mean RTs (Figure 3) and the error rates (Figure 4) for target-present and target-absent trials as a function of set size, separately for each session on day 1 and 2. Note that the individual participants’ RTs were not screened for outliers. RT Analysis

Figure 3 shows little difference in search RT function slopes (search rates) between stationary and moving search at all stages of practice. However, there was an overall increase in negative RTs with stationary search, which diminished during the course of practice. An ANOVA of the RT data, with main terms for search condition (moving, stationary), day (1, 2), session (1, 2, 3, 4), set size (8, 16, 24 items), and target (present, absent), revealed the following significant effects (amongst others): RTs increased with increasing display size; set size main effect: F (2, 24) = 52.37, p < .001; target-absent RTs were overall slower, and increased

FIG. 3 . Moving versus stationary search: Positive (target-present) and negative (target-absent) RTs as a function of set size, separately for each day and session (Experiment 1).

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FIG. 4 Moving versus stationary search: Target miss (target-present) and negative (target-absent) RTs as a function of set size, separately for each day and session (Experiment 1).

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more markedly with set size, than target-present RTs; target main effect: F (1, 12) = 33.83, p < .001; target × set size interaction: F (2, 24) = 43.82, p < .001. The slope of the positive RT function was 7.3msec item (averaged across sessions), that of the negative RT function 24.0msec/item. The shallow positive slope (< 10msec item) can be taken as indicating spatially parallel search of the 2 moving or stationary (target) set of items.

Practice Effects . RTs were overall faster on day 2 than on day 1; within a day, RTs decreased across sessions; day and session main effects: F (1, 12) = 43.24, p < .001; F(3, 36) = 28.26, p < .001. This decrease was more marked for negative than for positive RTs; target × session interaction: F (3, 36) = 4.24, p < .025. Importantly, the decrease was more pronounced for stationary than for moving search; search condition; search condition × session interaction: F(3, 36) = 3.37, p < .05, in particular for stationary absent RTs; search condition × session × target interaction: F (3, 36) = 2.69, p < .05. Figure 5 shows the slopes of the positive and negative RT functions across sessions, separately for each day. As can be seen, the slopes decreased across days; set size × day interaction: F(2, 24) = 4.38, p < .025, and sessions, mainly between the first and second session within a day; set size × session interaction: F (6, 72) = 6.45, p < .001. This effect was almost exclusively due to the de-

FIG. 5. Moving versus stationary search: Slopes of the positive and negative search RT functions across sessions, separately for each day (Experiment 1).

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The relatively steep negative slope is then best interpreted in terms of parallel re-checking after failure to find a target, with the likelihoodof re-checking increasing as a function of the set size.

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crease in slopes (gain in search rates) on negative trials; set size × day × target interaction: F (2, 24) = 8.40, p < .005; set size × session × target interaction: F (6, 72) = 4.59, p < .005. The gain in negative search rates was approximately 4msec/item between day 1 and day 2, and approximately 6msec/item between session 1 and combined sessions 2 –4 within a day. These effects of practice on the search RT function slopes showed no difference between moving and stationary search. Interestingly, the slopes on positive trials were relatively unaffected by practice (i.e. they remained relatively constant at about 7msec/item over the course of the experiment). Error Analysis

An equivalent ANOVA of the error data revealed significant main effects of target and set size, F (1, 12) = 56.57, p < .001; F(2, 24) = 8.50, p < .005, and a target × set size interaction, F (2, 24) = 12.47, p < .001. More errors were made on target-present trials (misses) than on target-absent trials (false alarms); and error rates, in particular miss rates, increased with increasing set size. This conforms with the error pattern typically observed in search experiments.

Practice Effects . The day × session × target interaction approached significance, F (3, 36) = 2.78, p < .075, due to a tendency for a greater decrease in miss rates across sessions on day 1 than on day 2. Furthermore, there were significant interactions between search condition and target, F (1, 12) = 4.65, p < .05, and search condition and day, F(1, 12) = 4.89, p < .05. More misses overall were made with stationary search than with moving search (while there was no difference between false alarms). With stationary search, miss rates (and, to a lesser extent, false alarm rates) decreased between day 1 and day 2, while there was no decrease in error rates with moving search. These differential practice effects between the two search conditions have implications for the interpretation of the RT data. In particular, the fact that stationary positive RTs were not slower than moving positive RTs early on during practice can be attributed to a speed–accuracy trade-off (see later).

Discussion In summary, practice on the search task had a greater effect on negative than on positive RTs. Practice reduced both the overall negative RTs and the slopes of the negative RT functions. The only differential effect of practice between the two search conditions was a relative gain for the stationary negative RTs in comparison with the moving negative RTs, but this did not affect the slopes of the negative RT functions (which were very similar at all stages of practice). Restated, while the stationary and moving negative RT functions remained parallel to each other, the difference between them narrowed over the course of

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practice— from 137msec, on average, in the very first session (on day 1) to only 51msec in the very last session (on day 2). Although there was no significant difference in positive RTs to moving and stationary targets early on during task performance, participants missed approximately twice as many stationary targets as moving targets in the very first session. By the very last session, this difference in miss rates had disappeared. Compensating for the initially greater stationary miss rate would have produced a (relatively set size-independent) slowing of the positive RTs relative to the moving condition (because participants would have had to search longer to reduce the miss rate), and a further slowing (in addition to that observed) of the negative RTs. Thus, participants initially experienced greater difficulty (in terms of errors) with the stationary condition, but they had learnt to compensate for this difficulty by the end of the experiment.

EXPERIM ENT 2 The failure of Experiment 1 to find slower positive RTs and search rates for stationary than for moving search early on during practice may be attributable to a differential speed–accuracy trade-off, that is, raised miss rates in the stationary condition due to a tendency to make negative responses prematurely. If so, then equating the miss rates, and their increase across set size, between the stationary and moving conditions ought to produce less efficient search in the stationary condition early on during practice. Experiment 2 was designed to reinvestigate practice effects on stationary and moving search, while more effectively equating the (miss) errors in the two conditions. Errors were equated by keeping the set size constant throughout a block of trials (note that Driver & McLeod, 1992, and Berger & McLeod, 1996, blocked set size in their experiments), and by providing participants with a running counter of their errors during a trial block. Participants were instructed not to make more than two or three errors per block. Whenever they produced an erroneous response on given a trial, a computer-generated “beep” sounded and the number of errors made thus far in the current block was displayed on the monitor in a prisoner’s tally format. This information was provided to help participants adjust their speed–accuracy trade-off according to the number of errors already made (i.e. effectively to reduce the reaction speed and respond more cautiously when the number of errors made approached or exceeded the permitted number).

M ethod The method of Experiment 2 was basically the same as that of Experiment 1, with the following differences.

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Participants . Twenty new participants, recruited from the Birkbeck Psychology subject panel, took part in Experiment 1. Some of them had prior experience with laboratory visual search tasks, but not with search for motion–form conjunction targets. Twelve of the participants were males and eight females; their ages ranged from 16 to 50 years; all had normal or corrected-to-normal vision; their payment was £4 per hour. Pairs of participants matched for sex and (as well as possible) age were formed, and members of each pair were allocated randomly to the moving or stationary search conditions. Design and Procedure. There was a total of eight experimental conditions: Search condition (moving, stationary) × session (1, 2, 3, 4); each session consisted of 240 (recorded) trials, 40 trials for each target (absent, present) × set size (8, 16, 24 items) combination. Each session consisted of six blocks of 40 trials. Set size was kept constant throughout a block (6, 16, or 24 items), while the order of target-absent and -present trials was varied randomly within a block. The various set size conditions were also presented in randomized order. Each participant performed four searches for either moving or stationary targets (between-participant variable). All sessions were carried out in a single day, with 10-minute breaks between sessions. Before the first search, participants were given 10 unrecorded practice trials in the presence of the experimenter, to ensure that they understood the task and instruction. Participants were instructed to make a target-present or -absent response as quickly and accurately as possible, but not to make more than two to three mistakes during a block of trials. When participants made an error, a computer-generated “beep” was sounded and the number of errors made up to that trial during the current block was displayed on the monitor in a prisoner’ s tally format, for 2 seconds. Participants were encouraged to use this information to adjust their response speed and accuracy so as not to exceed the permitted number of errors. One unrecorded filler trial was inserted after each trial on which an error occurred.

Results Figure 6(a) presents the (correct) mean RTs (left-hand panels) and Figure 6(b) the error rates (right-hand panels) for target-present and target-absent trials as a function of set size, separately for each session. RT Analysis

Figure 6 shows a different effect of practice on search RT function slopes (search rates) between stationary and moving search. In particular, the search rates were slower initially and decreased more markedly with practice in the stationary condition.

FIG. 6. Moving versus stationary search: Positive (target-present) and negative (target-absent) RTs (a), and target miss and false alarm errors (b), as a function of set size, separately for each session (Experiment 2).

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An ANOVA of the RT data, with main terms for search condition (moving, stationary), session (1, 2, 3, 4), set size (8, 16, 24 items), and target (present, absent), revealed the following significant effects (amongst others): RTs increased with increasing display size; set size main effect: F (2, 36) = 39.26, p < .001; target-absent RTs were slower overall, and increased more markedly with set size, than target-present RTs; target main effect: F(1, 18) = 131.41, p < .001; target × set size interaction: F (2, 36) = 57.15, p < .001. The slope of the positive RT function was 6.3msec/item (averaged across sessions), that of the negative RT function 19.2msec/item. The shallow positive slope (msec/item) can be taken as indicating spatially parallel search of the moving or stationary (target) set of items.

Practice Effects . RTs decreased across sessions; session main effect: F (3, 54) = 46.71, p < .001. This decrease was more marked for negative than for positive RTs; target × session interaction: F (3, 54) = 6.29, p < .001. Importantly, the decrease was more pronounced for stationary than for moving search; search condition × session interaction: F (3, 56) = 5.01, p < .005, in particular for stationary absent RTs; search condition × target interaction: F (3, 54) = 6.69, p < .001. Figure 7 shows the slopes of the positive and negative RT functions across sessions, separately for search condition. As can be seen, the slopes decreased from session 1 through session 2 to session 3; set size × session interaction: F (6, 108) = 5.26, p < .001. The decrease in slopes (gain in search rates) across sessions tended to be more marked on negative trails; set size × session × target interaction: F (6, 108) = 1.83, p < .100. Importantly, systematic effects of practice on the search RT function slopes were manifest only for stationary search, not for moving search; set size × search condition interaction: F (6, 108) = 2.49, p < .05: For stationary search, the slopes decreased from 22.3msec/item in session 1 through 18.2msec/item in session 2 to 14.3 and 13.7msec/item in sessions 3 and 4, respectively (for moving search, the slopes were 8.9, 9.5, 7.2, and 8.2msec/item in sessions 1, 2, 3, and 4, respectively). Furthermore, there was some tendency, in the stationary condition, for the gain in search rates across sessions to be more marked for negative than for positive responses, though the set size × session × search condition × target interaction failed to reach significance: F (6, 108) = 1.75, p = .116. Overall, the slope data for the first half of Experiment 2 (sessions 1 and 2 combined) agree with those of McLeod and his colleagues: There was an advantage for moving relative to stationary search in terms of the positive search rates, 4.4 versus 10.5msec/item (as well as the negative search rates, 14.0 versus 30.0msec/item). For the second half of the Experiment 2, the slope data agree with the findings of Müller and his colleagues: The positive search rates did not differ between moving and stationary search, 4.9 versus 5.7msec/item

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FIG. 7 . Moving versus stationary search: Slopes of the positive and negative search RT functions across sessions (Experiment 2).

(while there remained an advantage for moving over stationary search in terms 3 of the negative search rates, 10.7 versus 22.3msec/item). Error Analysis

An equivalent ANOVA of the error data revealed significant main effects of target and set size; F (1, 18) = 64.02, p < .001; F (2, 36) = 10.09, p < .001, and a target × set size interaction; F(2, 36) = 24.81, p < .001. More errors were made on target-present trials (misses: 5.2%) than on target-absent trials (false alarms: 3

Note that the differential practice effects on the search rates in the two conditions are not due to the participants performing the stationary search being “poorer” subjects generally than the participants performing the moving search. This can be concluded from the base RTs (the y-intercepts of the search RT functions), which were well matched between the two groups of participants: 574msec (averaged across sessions and positive and negative responses) for both the stationary and the moving group.

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1.5%); and error rates, in particular miss rates, increased with increasing set size (the miss rates were 3.3, 5.1, and 7.3%for 8-, 16-, and 24-item displays, respectively).

Practice Effects . There were no significant error effects involving session; main effect: F (3, 54) = 0.72, or search condition; main effect: F(1, 18) = 0.19. In particular, the session × set size × search condition interaction, which was significant in the RT data, was not significant in the error data; F (6, 108) = 0.98. Consequently, the marked improvement in search rates with increasing amount of practice in the stationary search condition (see previous RT analysis) cannot be attributed to a trade-off with response accuracy.

Discussion Experiment 2 showed that, when response accuracy is equated between the moving and stationary search conditions, performance early on during practice exhibits an advantage, in terms of positive search rates, for moving relative to stationary search (first half of experiment: 4.4 versus 10.5msec/item), just as reported by Driver and McLeod (1992) and Berger and McLeod (1996). However, the efficiency of stationary search improves during the course of practice, while that of moving search remains relatively constant, so that detecting a stationary target becomes as efficient as detecting a moving target (second half of experiment: 4.9 versus 5.7msec/item). The latter pattern agrees with Müller and Found (1996) and Müller and Max well (1994). In contrast to Experiment 1, in Experiment 2, the error rates were well equated between the two search conditions at all stages of practice, and the increase in miss rates across set size was held in bounds (target misses increased from 3.3 to 7.3% when set size increased from 8 to 24 items), due to two measures taken in Experiment 2: Presenting the various set size conditions in blocks of trials (rather than in randomized order within blocks), together with providing incremental error feedback for blocks of trials. These procedures are likely to ensure effective control in visual search experiments, when search rates are to be determined accurately (which requires checking the increase in miss rates with increasing set size) and to be compared between experimental conditions conducted separately.

GENERAL DISCUSSION Although Experiment 1 failed to reveal an asymmetry in terms of search rates between moving and stationary 45° targets early during practice, it showed an asymmetry in terms of overall miss rates, that is, overall positive (and negative) RTs when the differential speed–accuracy trade-off between moving and stationary search is compensated for. This asymmetry at least pointed in the

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direction of the findings reported by McLeod and his colleagues (Berger & McLeod, 1996; Driver & McLeod, 1992). In Experiment 2, response accuracy was equated in the two search conditions at all stages of practice, permitting a direct comparison between the moving and stationary (positive) search rates. This comparison indeed revealed an asymmetry in search efficiency along the lines reported by McLeod and his colleagues early on during practice. However, the initial advantage for detecting moving 45° targets disappeared after some 500 experimental trials, due to a marked improvement in stationary search efficiency. One problem in interpreting the convergence of the search rate in the stationary condition with that in the moving condition may be that moving conjunction search was performed near-optimally from the start of the experiment, so that it had little room for improvement with practice. In other words, since the moving search efficiency was at ceiling, the assumption that equal search rates in the moving and stationary conditions at the end of practice reflect equally 4 efficient underlying processes might be challenged. However, although the search rates for the moving condition appear to be flat from the start of the experiment (e.g. see Figure 7), this is likely to be an artefact of the way the search rates were calculated. Following the procedure adopted by McLeod and his colleagues, the rates were calculated across all, moving and static, display items. This procedure implicitly assumes that all items contribute equally to the search rate. However, on a strong motion filter account, according to which all stationary items are excluded from the search in a parallel step, the increase in search RT with increasing display size should be related to the number of moving items only (i.e. to half the display items). Accordingly, the search rates estimates for the moving condition would be doubled to approximately 10msec/item, which is conventionally regarded as the borderline between serial and parallel search. Thus, in absolute terms, there would be room for improvement in search efficiency from borderline parallel to strictly parallel. Recent work by von Mühlenen and Müller (submitted, Exp. 1) is consistent with the view that calculation of the search rates across all display items is misleading. Using independent variation of the numbers of moving and stationary items (easy moving conjunction search), von Mühlenen and Müller estimated the contribution of the moving items to the search rate to be twice as large as that of the stationary items. By implication, the moving search rates as calculated in the present experiments (across all display items) are likely to underestimate the true contribution of the moving items. Thus, on an absolute scale of search efficiency, search for moving conjunction targets was non-optimal and, in principle, had some room for improvement, lending support to the argument

4

We thank Jon Driver and Jim Enns for pointing this problem out to us. See also Trick and Enns (1997).

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that equal search rates in the moving and stationary conditions at the end of practice may be interpreted as reflecting equally efficient underlying processes. The finding that participants found search for stationary 45° targets more difficult initially than search for moving 45° targets, but could compensate for this difficulty with practice, has implications for theories of how motion–form conjunctions are processed. McLeod and his colleagues took the advantage for moving over stationary 45° targets as indicating that gross aspects of form discrimination can be accomplished within the motion system. However, after sufficient practice, our participants achieved positive search rates, and error rates, with both moving and stationary targets that were as fast, and low, as those attained by the participants of McLeod and his colleagues with moving 45° targets only. In other words, McLeod and his colleagues did not demonstrate an insuperable limit to performance with stationary 45° targets. Thus, what requires special consideration is not why search is so efficient with moving 45° targets, but rather why it may be comparatively slow during initial stages of practice with stationary 45° targets. We suggest that it is slow during early stages of practice with the task, when participants find it hard to keep moving nontargets (whatever their line tilt) out of the search. We also provide a reason why this should be so, namely: Filtering-out of the moving items requires participants to reverse the default setting of the motion system (from positive tagging of the moving items to negative tagging). Practice improves participants’ ability to do this, minimizing interference. 5 Our account (see also Müller & Found, 1996) is illustrated in Figure 8. It assumes that target selection operates from and overall-saliency , or master, map (of locations), with the relative activation of the master map units determining the attention priority for the stimuli within their receptive fields (e.g. Cave & Wolfe, 1990; Koch & Ullman, 1985; Treisman & Sato, 1990). The master map units integrate, in parallel, the output of dimension-specific feature analysis (via spatiotopic connections between feature analyser and master map units). Selection can be top-down controlled to some extent by enhancing the saliency of display items sharing target features, at the feature map level (e.g. Cave & Wolfe, 1990; Treisman & Sato, 1990; Wolfe, 1994). In conjunction search for, say, a moving 45° titled line— see Figure 8(a)— moving items will be activated through the motion system, via its positive links to the master map, and items orientated to 45° through the orientation system. The moving 45° line target will achieve a higher saliency at the master map level than moving vertical line and stationary 45° line distractors — because it is the only item receiving activation from both the motion and the orientation feature detector (distractors

5

Our account is functional in nature; it is not meant to be a neurophysiological model.

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are supported by only one detector). Thus, search can be efficiently guided to the moving target even when there are stationary items in the display sharing the target line tilt. However, guiding the search to the stationary items in the presence of moving items is difficult, because dynamic stimuli usually have selection priority over static stimuli (or static changes such as in stimulus colour) (e.g. Folk, Remington, & Wright, 1994). In terms of the model, in default mode, the motion system passes the moving items: It operates via its positive links to the master map, making the moving items more salient at the stage of selection (positive tagging). If the task requires filtering-out of the moving items, thereby making the stationary items more salient, the system must be reset under 6 top-down control to operate via its negative links (negative tagging ). Thus, in conjunction search for a stationary 45° tilted line— see Figure 8(b)— the master map units corresponding to the moving items will be inhibited through the negative links from the motion system, and the units corresponding to items oriented to 45° will be activated through the orientation system. As a result, the master map activation of the moving 45° line distractors through the orientation system would be offset by inhibition from the motion system (i.e. activation from the orientation system is cancelled by inhibition from the motion system); and the stationary 45° target would be the only salient item, due to its activation from the orientation system (which is not cancelled by inhibition from the motion system because stationary items are not registered by the motion system). However, resetting the motion tagging system is demanding and prone to failure; that is, there is a tendency to revert to the default operation of the system. Consequently, moving nontargets are more likely to intrude into the FIG. 8. (Opposite). Visual search for motion–form conjunctions. Search for such conjunctions can be explained by the interactions between three mechanisms: An overall-saliency (master) map, an orientation system, and a motion system. (a) When searching for a moving 45° tilted target, master map units representing moving items are activated through positive links from the motion system; (b) when searching for a stationary 45° tilted target, such master map units are inhibited through negative links from the motion system. In both search tasks, the orientation system activates master map units representing 45° tilted items. After integration of the orientation and motion signals by the master map units, the target is the most salient item in both tasks (a unit’s activation is represented by its grey-level).The top-right close-ups in each figure illustrate the hypothetical pattern of connectivity between the motion system and the master map that would implement either positive tagging, via on-node activity — Figure 6(a)— or negative tagging, via off-node activity — Figure 6(b). On- and off-nodes are mutually inhibitory, so that the system can operate in only one mode (positive or negative tagging) at a time. Which mode is operational can be top-downcontrolled. Note that “starred” links (*) between the motion system and the master map in (a) and (b) indicate that the link is inactive. See text for further details.

6

Dual (positive and negative) links between the motion system and the overall-saliency map are required assuming that there are no special statis detectors responding to the absence of motion.

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search for a stationary target than vice versa. It follows that, in default mode, there is an advantage, in terms of search rates, for moving over stationary search. The setting of the motion tagging system might operate via competitive on/off nodes (e.g. Houghton & Tipper, 1994), where the on-nodes, passing moving items, have a higher baseline activation (or lower threshold). To disable the on-nodes, thereby giving control to the off-node, the baseline activation of the on-nodes must to be lowered (or their threshold raised) by some top-down influence; alternatively, the top-down influence might act on the off-nodes. Perhaps this influence needs to be established and actively maintained during each trial. At the end of a trial, the system may revert to default mode. Within this framework, the effect of practice on stationary search would be to increase the efficiency with which participants can suppress the default operation (positive tagging) of the motion system. Once participants have learned to do so efficiently, a stationary target can be detected as readily, in parallel, as a moving target, since the saliency differences between the target and distractors are equivalent in the two search conditions — compare the relative targetdistractor saliencies between Figure 8(a) and Figure 8(b). However, early on during practice, when suppressing the default mode of the motion system is inefficient, moving distractors would achieve a certain amount of activation in the saliency map (as activation from the orientation system would not be completely cancelled by inhibition from the motion system), reducing the saliency difference between the target and the moving distractors; in a redrawn Figure 8(b), the target would be less differentiated from the nontargets in the saliency map. Assuming that the parallel saliency computations are noisy (Cave & Wolfe, 1990; see also Enns, 1992), there would be an increased probability that a moving nontarget is selected and transferred to the higher object recognition and response selection stages. These stages compare an item’s attributes with those specified in the target description, and initiate a positive response if they match, dealing with one item at a time. Consequently, moving nontargets that are selected would need to be rejected by the serial matching process, delaying the detection of the stationary target. The delay would be expected to be dependent on the set size, as the likelihood of a moving nontarget intruding into the serial stage would increase with the number of moving items (half the set size). Assuming that the serial matching process is accurate (Cave & Wolfe, 1990), intrusions of moving nontargets into the serial stage would be expected to increase the positive and negative search RTs, but not the rate of false alarms. The false alarm rate is not determined by the likelihood of such intrusions, but rather by the probability that the matching process mistakes a moving item for the stationary target (which is negligible if the matching process is accurate). However, if participants tend to respond prematurely, there would be an

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increased likelihood that the serial stage has not yet detected the stationary target by the time a response is initiated. Assuming that a positive response is given only if the target is actually encountered (see Chun & Wolfe, 1996), the result would be an increase in the rate of target misses. The finding of increased miss rates for stationary relative to the moving search early on in Experiment 1 (in which the stationary group of participants adopted a risky speed–accuracy trade-off), and the absence of increased false alarm rates in Experiment 2 are consistent with this account. In summary, according to the present account, the effect of practice would be to increase the efficiency with which participants can suppress the default operation of the motion system. After several hundred trials of practice, our participants searched for stationary 45° targets as efficiently as for moving 45° targets, indicating that they were able to keep moving nontargets out of the search. Note that the present account is not entirely different from that proposed by McLeod, Driver, and their colleagues. Both accounts assume (1) that there is a general tendency to attend to moving in preference to stationary display items and (2) that there is a part of the processing system responding to motion but not stasis, while there is no part exhibiting the reverse response. However, the two accounts differ with regard to the assumption whether or not object form (orientation) is represented by the motion system. Driver and McLeod (1992) have advocated the idea that at least gross aspects of orientation are coded by motion-sensitive cells in V5. In contrast, our account is more parsimonious in that it assumes no special-purpose mechanisms coding motion–form conjunctions. This is not to say that it is impossible to derive orientation information from motion information. For example, when a segment of a moving line passes through the receptive field of a motion-sensitive cell, the velocity signal generated by the line differs according the angle between the line’s orientation and its motion direction. The signal is maximal when the angle is 90°, and minimal when the angle is 0°. (The “aperture problem” arises from the fact that it is difficult to derive the true velocity, that is, the speed and direction, of a translating object from local velocity measurements; for example, Hildreth, 1984; see also Lorenceau, Shiffrar, Wells, & Castet, 1993). Consequently, a vertical line moving upwards (0° angle) would produce weaker velocity signal than a 45° line moving upwards (45° angle). Thus, differences in the velocity signals generated by the target and distractor lines could be used to make a “formbased” response, without the motion-sensitive cell coding line orientation explicitly. Further work is required to ascertain whether search for moving conjunction targets indeed exploits the form information contained in differential velocity signals and, if so, how efficiently that information is derived relative to that coded by the stationary form system.

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