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深度学习+多目标追踪经典文章.pdf
Multiple Object Tracking and Attentional Processing
CHRISTOPHER R. SEARS, University of Calgary
ZENON W. PYLYSHYN, Rutgers University
Abstract How are attentional priorities set when multiple
stimuli compete for access to the limited-capacity visual
attention system? According to Pylyshyn (1989) and Yantis
and Johnson (1990), a small number of visual objects can be
preattentively indexed or tagged and thereby accessed more
rapidly by a subsequent attentional process (e.g., the tradi-
tional "spotlight of attention"). In the present study, we
used the multiple object tracking methodology of Pylyshyn
and Storm (1988) to investigate the relation between what
we call "visual indexing" and attentional processing. Partici-
pants visually tracked a subset of a set of identical, independ-
ently randomly moving objects in a display (the targets), and
made a speeded identification response when they noticed a
target or a nontarget (distractor) object undergo a subtle
form transformation. We found that target form changes
were identified more rapidly than nontarget form changes,
and that the speed of responding to target form changes was
unaffected by the number of nontargets in the display when
the form-changing targets were successfully tracked. We also
found that this enhanced processing only applied to the
targets themselves and not to nearby nontarget distractors,
showing that the allocation of a broadened region of visual
attention (as in the zoom-lens model of attentional alloca-
tion) could not account for these findings. These results
confirm that visual indexing bestows a processing priority to
a number of objects in the visual field.
It is widely accepted that visual attention can be shifted from
one location to another independently of eye movements,
and that the processing of stimuli appearing at attended
locations is enhanced. The methodological paradigm that
produced much of the evidence for this is the attentional
cueing procedure (Posner, Snyder, & Davidson, 1980). In a
cueing experiment, visual attention is shifted to a predeter-
mined location either endogenously, in which the shift is
under the volition of the observer, or exogenously, in which
the shift is involuntarily elicited by a highly salient cue. A
substantial number of studies have demonstrated that the
speed and accuracy of processing at cued locations is supe-
rior to that at uncued locations. For example, Downing
(1988) found that perceptual sensitivity was enhanced at the
location of a cue, and concluded that focused attention
serves to facilitate visual information processing.
that
this
enhanced processing
Previous research suggests that, whether controlled
endogenously or exogenously, there can be only one focus
of attention at any one time. Posner, Snyder, and Davidson
(1980) provided evidence that visual attention is allocated to
single contiguous regions of the visual field, enhancing the
processing of stimuli falling within the single contiguous
"spotlight." Eriksen and St. James (1986) subsequently
showed
falls off
monotonically as one moves out from the locus of visual
attention, and that the resolution of the spotlight varies
inversely with the size of the region encompassed (the
"zoom-lens" model). Many investigators have concluded that
the spotlight is the primary processing bottleneck of the
attentional system, as only stimuli falling within this region
undergo extensive perceptual analysis (e.g., Eriksen &
Hoffman, 1974; Yantis & Johnston, 1990), and only one
such region can be attended to at any one time (Eriksen &
Yeh, 1985). There is also evidence that focal attention may
be directed at objects in the field of view rather than at
spatial regions (Baylis & Driver, 1993) and that the objects
selected in this way continue to be identified as the same
objects as they move about (Kahneman & Treisman, 1992;
Pylyshyn, 1989).
Although the evidence for the unitary nature of focal
attention is somewhat contentious (e.g., Castiello & Umilta,
1990; Joula, Bouwhuis, Cooper, & Warner, 1991; Pashler,
1998), there do seem to be severe limitations on the alloca-
tion of focal attention. Thus, when multiple stimuli compete
for attention, there must be some mechanism that priorizes
the stimuli in some way so that stimulus selection can
proceed in an efficient manner. Consequently, an under-
standing of the means by which attentional priorities are
assigned is of some importance.
Yantis and Johnson (1990) sought to determine how
attentional priorities are set under conditions in which
attention is allocated in a stimulus-driven manner. They had
Canadian Journal of Experimental Psychology, 2000, 54:1, 1-14
participants search for a target letter in static multielement
displays composed of multiple abrupt onset and no-onset
items. Abrupt onset and no-onset items are distinguished by
their attentional saliency: No-onset items are presented by
removing camouflaging line segments from an existing
object in the display until the target is revealed, whereas
onset items abruptly appear in previously empty locations
of the display. In previous studies, Yantis and his colleagues
(Jonides & Yantis, 1988; Yantis & Jones, 1991; Yantis &
Jonides, 1984) found that single abrupt onsets automatically
capture attention, and that abrupt onset items are processed
before other no-onset items in a display. Yantis and Johnson
(1990) found that when multiple abrupt onsets are present,
a limited number of them (approximately four) can be
processed before other no-onset items. They proposed that
attentional priorities are set by means of attentional "tags"
that are bound to the representations of highly salient
objects (such as abrupt onsets), and that a limited number of
abrupt onsets can be attentionally tagged and then given
priority access to focused attention.
Like Yantis' priority tag model, Pylyshyn's (1989) FINST
model of visual indexing provides a means for setting
attentional priorities among multiple stimuli. In the FINST
model, a limited number of objects can be preattentively and
simultaneously indexed independently of their retinal
locations or identities (FINST is an acronym for Fingers of
iNSTantiation, a reference to the idea that these indexes
point to objects and provide a way to bind objects to
internal symbolic arguments). Pylyshyn (1989) suggested
that a primary function of visual indexing is to individuate
a small number of objects so that they may be directly
accessed and subjected to focused attentional processing.
Indexing provides direct access to the objects, so that once
an object is indexed it is not necessary to use attentional
scanning to find that object (Pylyshyn et al., 1994). In
contrast, in order to selectively attend to a nonindexed
object, its position must first be ascertained through
attentional scanning (e.g., Treisman & Gelade, 1980; Tsal &
Lavie, 1993). Visual indexing thus provides a means of
setting attentional priorities when multiple stimuli compete
for attention, as indexed objects can be accessed and at-
tended before other objects in the visual field.
In the FINST model, visual indexes are assigned primarily
in a stimulus-driven manner, so that salient feature charac-
teristics or changes are automatically indexed. Typical
stimulus events that would be indexed roughly correspond
to stimuli that automatically attract focal attention, such as
peripheral cues (Posner & Cohen, 1984), abrupt visual
events such as onset stimuli (Yantis & Jonides, 1984), and
"pop-out" visual features (e.g., a red circle embedded in a
display of black circles; Treisman & Gelade, 1980).
Burkell and Pylyshyn (1997) have provided more direct
evidence of the ability of several simultaneous onsets to
attract indexes which could then be used to access and
Sears and Pylyshyn
examine the indexed items. They used Treisman-type visual
search tasks to study the selection of search subsets by onset
cues, and they appealed to the fact that visual search behav-
iour depends on the nature of the set through which one has
to search. In the original visual search studies, Treisman and
Gelade (1980) showed that search targets that differed from
each nontarget in the search set by a single feature (called the
"single-feature" search condition) were easily recognized and
the time it took to recognize them was very nearly inde-
pendent of the number of nontargets in the search set. In
contrast, displays in which each nontarget shared some
feature with the target, so that only a combination of two
features defined the target (called the "conjunction-feature"
search condition), resulted in generally slower searches as
well as reaction times that increased substantially with
increasing numbers of nontargets in the display. Burkell and
Pylyshyn used displays which were of the "conjunction
search" type. However, they selected subsets through which
participants were required to search, and the subsets could
constitute either a single-feature or a conjunction-feature
search.
The subsets were selected as follows. All members of the
search set were precued by "X" place holders, but a subset of
variable size that was to contain the target was cued by
placeholders that occurred about one second later than the
other placeholders, and about LOO ms before the search
began. The precued subset itself could constitute either a
single-feature or a conjunction-feature search task (i.e., the
target could be distinguished from members of the subset by
one feature or only by a conjunction of two features).
Burkell and Pylyshyn found that a subset precued by these
late onset cues could, for the purposes of speeded visual
search, be separated from the rest of the display and treated
as though they were the only items present. Burkell and
Pylyshyn showed, for example, that only the single-feature
versus conjunction-feature property of the subset was
relevant to the pattern of search reaction times (the overall
display always provided a conjunction-search condition
because it contained items with all combinations of proper-
ties). Moreover, they found that increasing the relative
distance between search targets did not increase the search
time, showing that the indexed targets could be accessed
without having to find them first by scanning the display.
Visual indexes point to features or objects, not to the
locations that these stimuli occupy. Like the object files of
Kahneman, Treisman, and Gibbs (1992), visual indexes are
object-centred and continue to reference objects despite
changes in their location. According to the visual indexing
model, a visual index automatically individuates and tracks
moving objects. Because there are a small number (around
four or five) of such indexes, observers can track around
four or five independently moving distinct visual objects in
parallel. In an empirical test of this hypothesis, Pylyshyn
and Storm (1988) had participants visually track a
Multiple Object Tracking
prespecified subset of a larger number of identical, randomly
moving objects in a display. The members of the subset to
be tracked (the targets) were identified by briefly flashing
them several times, prior to the onset of movement. Accord-
ing to the model, targets designated in this fashion are
automatically indexed. During the tracking task, the targets
were indistinguishable from the other distractor objects,
which made the historical continuity of each target's motion
the only clue to its identity. Participants tracked the target
objects for 5 to 10 seconds, after which either a target or a
distractor was probed by superimposing a bright square over
it. The participants' task was to determine whether the
probed object was a target or a distractor. According to
Pylyshyn and Storm (1988), the indexing of the target
objects would allow each of them to be simultaneously
tracked and identified throughout the motion phase of the
experiment, despite the fact that the targets were perceptu-
ally indistinguishable from the distractors.
Pylyshyn and Storm (1988) found that performance in
this multiple object tracking task was extremely high for
subsets of up to five elements — in fact, participants could
simultaneously track up to five target objects at an accuracy
approaching 90%. McKeever and Pylyshyn (1993), Yantis
(1992), Scholl and Pylyshyn (1999), Viswanathan and
Mingolla (1989), Cavanagh (1999), and Culham et al. (1998)
have all reported similar results. Moreover, using a simula-
tion of the task, analyses by Pylyshyn and Storm (1988) and
McKeever and Pylyshyn (1993) indicate that a single
spotlight of focused attention moving rapidly among the
target objects and updating a record of their locations could
not produce this level of tracking performance in the setup
used in their study. In the Pylyshyn and Storm (1989)
simulations, for example, tracking performance was no
higher than 50% based on extremely conservative assump-
tions. One such assumption was an attentional scan velocity
as high as 250 degrees per second (the highest estimated scan
velocity in the previous literature). Another assumption was
that participants stored (with zero encoding time) the
predicted locations based on the direction and speed of the
targets' motion, and that they used a guessing strategy when
they were uncertain. These results suggest that it is not
possible for the task to be performed at the observed level of
accuracy without some parallel tracking of the target
objects. Pylyshyn and Storm (1988) concluded that their
results confirmed a key prediction of the visual indexing
model, namely, that a small number of objects can be
indexed and that the indexes are used to keep track of them
in parallel without attentional scanning and without
encoding their locations.
We should note that Yantis (1992) has added an addi-
tional mechanism to explain how multiple target objects are
tracked in this task. Yantis (1992) argued that participants
spontaneously group the targets together to form a virtual
polygon, whose vertices correspond to the continually
changing positions of the targets, and that it is this single
"object" that is tracked throughout the trial. While it may
be that observers conceptually group elements into a
polygon, it is still the case that the individual targets them-
selves must be tracked in order to keep track of the location
of the vertices of such a virtual polygon. Consequently, we
do not consider Yantis's (1992) account to be incompatible
with Pylyshyn and Storm's (1988) analysis. Indeed, we have
offered a closely related proposal for what we call an "error
recovery" stage of the tracking process, wherein a polygon-
like representation of the relative location of targets is
maintained and referred to when the loss of a target is
detected (McKeever & Pylyshyn, 1993; Pylyshyn et al.,
1994).
The purpose of the present investigation was to explore
the relation between visual indexing and attentional process-
ing in the context of the multiple-object tracking paradigm.
According to Pylyshyn (1989), visual indexing provides a
means of keeping track of a number of objects, in the sense
of providing a means for querying them, without first
having to ascertain their positions through attentional
scanning. We assume that, in order to focus unitary atten-
tion on an object, one must first find the object and move
focal attention to it. However, if the object has already been
indexed, focal attention can be allocated to it directly,
without prior search. Consequently, shifting attention to
indexed objects should be faster than shifting attention to
nonindexed objects. Because of this we expect that in a task
requiring focal attention, a response to an indexed object
will generally occur before a response to other objects in the
visual field.
This hypothesis was investigated in Experiment 1.
Participants tracked a set of target objects and made a
speeded identification response when they detected that a
target or a distractor object underwent a subtle form
transformation. According to our hypothesis, targets are
indexed during the tracking task, and therefore unitary focal
attention can be moved directly to them. If focal attention
is shifted to a target, either on some regular basis or when
some global change-detector reports a change, then any
change at that target is more readily recognized. Thus, if
targets undergoing changes are found to be more rapidly
identified, this would support our hypothesis that visual
indexing facilitates attentional processing.
Experiment 1
METHOD
Participants. Seventeen individuals participated in this
experiment. All had normal or corrected-to-normal vision,
and were paid $15 for their participation.
Apparatus and stimuli. Stimuli were presented on a 19-inch
Sun MicroSystems monochrome monitor with a resolution
of 1,056 by 900 pixels. A Sun MicroSystems 3/50 microcom-
puter controlled the stimulus presentation and randomiza-
tion of trials. Response times were collected with a three-
button mouse and a dedicated Zytec timing board (Danzig,
1988) which provided response latencies accurate to one
millisecond.
Stimuli consisted of rectangular, seven-segment box figure
eights (B), and the capital letters E and H. The E and H
characters were created by removing lines from the figure
eights. The line segments used to construct these stimuli
were a single pixel in width. The E and H figures were used
because of their high degree of similarity to the figure eight
character.
All the stimuli were created as pixel-drawn "objects" in
video memory that could be moved across the screen
without being continually recreated. Each object subtended
a visual angle of 0.84° in height and 0.63° in width from a
viewing distance of 50 cm. Stimuli were white and were
presented on a dark background.
The animation of the objects in the display was con-
trolled by an algorithm which simulated brownian motion,
creating a random, independent, and continuous pattern of
movement for each object. Motion sequences were generated
in real-time during each trial and all the participants re-
ported the perception of smooth and continuous motion
during the practice trials. Each object was surrounded by an
invisible circular barrier which insured that no two objects
could collide or superimpose themselves over one another.
Each object moved at a randomly determined velocity of
between 4° and 8° per second. Once determined, the
velocity of each object did not vary throughout a trial. A
wall repulsion force retained all the objects within a 15° by
15° area by bouncing them off these invisible borders.
Although the total number of target and distractor objects
displayed varied according to the trial type, the movement
and velocity of these objects did not vary as a function of
the number of targets and distractors present in a given trial.
This was accomplished by having the maximum number of
objects (16) moving in the display on every trial, but making
only a subset of them visible for the particular condition
concerned. This technique eliminated any relation between
the density of objects in the display and the freedom of their
movement.
Procedure. Participants were seated in a darkened room
approximately 50 cm from the display and used a chinrest to
reduce head movements and control viewing distance. A
three-button mouse was used to collect responses. Partici-
pants were given written instructions prior to the experi-
ment, which outlined the general procedure and emphasized
the importance of maintaining fixation throughout the
session. Each participant was then given a demonstration
and explanation of a trial sequence. They were instructed to
note the positions of the blinking target objects at the start
of each trial, because the task was to keep track of these
Sears and Pylyshyn
objects when they began moving. During the motion phase,
they were instructed to track the target objects without
moving their gaze from the fixation cross (eye position was
not monitored). At some point in the trial, one of the target
or distractor objects would transform into an E or an H, and
participants were to respond by pressing the E or H button
as quickly as possible when they had identified this form
change. Participants were informed that the target and
distractor objects were equally likely to undergo form
changes. Each participant completed 20 practice trials prior
to the experiment.
Figure 1 depicts a trial sequence. Each trial began with
the presentation of an isolated fixation cross for 2 s. Then,
depending upon the condition, from 7 to 16 figure-eight
objects appeared on the screen. The placement of the objects
was randomly determined on each trial, subject to the
constraint that none could overlap one another or their
invisible "barriers." Three or four of these objects then
began to blink on and off for 3 s, designating them as the
target objects to be tracked. The appearance of the remain-
ing objects (the distractors) did not change during this target
designation phase. All of the objects then began to move in
a random and continuous fashion about the screen (subject
to the previously mentioned constraints), and the partici-
pant attempted to simultaneously track each of the target
objects. The target and distractor objects were indistinguish-
able from one another during this tracking phase. Partici-
pants tracked the targets for a randomly determined interval
of between 5 and 9 s, after which either a target or a
distractor object was transformed into an E or an H. On 50%
of the trials, a target object underwent a form change, and
on the remaining 50% of the trials a distractor underwent a
form change. The remaining targets and distractors, as well
as the new letter in the display (E or H), maintained their
movement during the transformation and continued moving
until the participant responded. After a response had been
made, the screen was cleared and a new trial was initiated
following a 3-s inter-trial interval.
Each participant completed eight blocks of 45 trials. The
order of trials was randomized separately for each partici-
pant, and there was a five-minute rest period between each
block.
Design. There were three factors manipulated in this experi-
ment: the type of form change (target or distractor form
change), the number of targets tracked (3 or 4) and the
number of distractors present in the display (4, 8, or 12).
There were 30 trials in each of the 12 conditions, for a total
of 360 trials.
RESULTS
Response latencies greater than three seconds were classified
as outliers and were removed from the dataset. This proce-
dure resulted in the removal of 3.6% of the data. Response
Multiple Object Tracking
Figure 1. A schematic representation of a trial sequence. Participants view items on a video monitor. In the target designation phase (t - 2) the target
objects were flashed for 3 seconds (the selected targets are shown in circles in this illustration). The targets were then tracked for several seconds during
the motion phase (t — 3), and then a target or a distractor object underwent a form change by dropping two segments and ending up as an E or an H
(t - 4). The participants' task was to identify this form change as quickly and as accurately as possible. In this example, there are three target objects, and
one of these target objects undergoes a form change to an E shape.
latencies and error rates were submitted to a 2 (Type of
Form Change) x 2 (Number of Targets) x 3 (Number of
Distractors) repeated measures analysis of variance (ANOVA).
The mean identification error rate (E or H) across all the
conditions was 3.8%, and a three-factor repeated measures
ANOVA yielded no significant effects, all Fs < 1. Unless
otherwise stated, the p values for all significant statistics
reported in the text are less than .05.
In the analysis of the response latencies, the main effect
for the number of targets tracked (3 or 4) was not signifi-
cant, F < 1. That is, response latencies were similar whether
three or four target objects were being tracked. The mean
response latencies in Experiment 1, collapsed across the
number of targets tracked (3 or 4), are listed in Table 1.
There was a significant main effect for the type of form
change (target or distractor object), which confirmed that
participants responded more rapidly to target form changes
than to distractor
form changes, f(l, 16) = 29.58,
MSB = 25,084. Responses to target form changes were an
average of 121 ms faster than responses to distractor form
changes. The main effect for the number of distractors (4, 8,
or 12) was significant, f(2, 32) = 17.04, MSE = 9,009, as
response latencies increased as the number of distractors in
the display increased.
The interaction between the type of form change and the
number of distractors was not significant, F(2, 32) = 1.91,
MSE = 7,772. The absence of this interaction suggests two
things: (a) that participants responded to target form changes
more rapidly than to distractor form changes regardless of
the number of distractors in the display, and (b) that
response latencies to both target and distractor form changes
increased with increases in the number of distractors. This
latter finding was unexpected, and will be discussed in
greater detail in the Discussion section.
The interaction between the number of targets and the
number of distractors was not significant, F < 1, nor was
the interaction between the type of form change and the
number of targets, F(2,16) = 3.40, MSE = 7,692. Finally, the
three-way interaction was not significant, / < 1.
Analysis of region-bounded effects. The results of this experi-
ment clearly indicate that target form changes are identified
more rapidly than distractor form changes when partici-
pants are tracking the target objects. This result supports our
hypothesis
that visual indexing facilitates attentional
processing.
Although we contend that the latency advantage for
target form changes is a consequence of the visual indexing
of the target objects, there is an alternative explanation for
this finding. Specifically, it is possible that during the
tracking task the participant's attention was focused on the
continually changing triangular or polygonal figure whose
vertices constituted the three or four targets in the display.
Maximum attentional sensitivity may then have been
allocated to the region bounded by and including the group
of target objects. Such a process could be the consequence of
a deliberate attentional strategy employed by participants to
enable target tracking, in which a "zoom-lens" of visual
attention was focused on this region (Eriksen & St. James,
1986). If this was the case, then we would expect that
responses to target form changes would be facilitated,
because all of the target form changes would have occurred
within
this region of focused attention. Moreover,
distractors undergoing form changes within this region
would be identified more rapidly than distractors undergo-
ing form changes outside of this region, and the averaging of
these two types of trials would produce an apparent delay in
responding to distractor form changes relative to target form
TABLE 1
Mean Response Latencies (in Milliseconds) and Identification Error Rates
(in %) to Target and Distractor Form Changes in Experiment 1
Type of Form Change
Number of Distractors
Target
M
SE
Errors
Distractor
M
SE
Errors
1022
43.4
3.8
1175
51.6
3.8
1080
34.1
4.6
1194
50.9
3.4
12
1145
36.3
3.1
1241
46.3
4.5
Note. The data are averaged across the number of targets (3 or 4).
changes. Thus, a zoom-lens model could account for our
data by postulating that participants dynamically allocate
attention to the region encompassed by the targets.
To evaluate this hypothesis, for the last 6 of the 17
participants in Experiment 1, the screen coordinates of the
target and distractor objects were recorded following every
response. The trials in which a distractor object underwent
a form change were divided into two groups: trials in which
the participant responded to the form change when the
distractor was within the region encompassed by the targets,
and trials where the participant responded to the form
change when the distractor was outside of this region. The
target locations at the time of the response served as the
vertices of the region in question, with the boundaries of the
entire region defined by the convex hull of the target objects
(the smallest convex polygon that contained all of the
targets). These data were then submitted to a one-factor
repeated measures ANOVA, with distractor position
(distractor inside or outside the convex hull of the targets) as
the single factor. A zoom-lens account would predict an
effect of distractor position in this analysis, with distractor
form changes being identified more rapidly when they
occurred within the convex hull of the target set.
However, in this analysis the effect of distractor position
was not significant, F < 1. That is, response latencies to
distractor form changes were similar regardless of whether
the distractor was inside (1,215 ms) or outside (1,238 ms) the
convex hull of the target set.1 Consequently, it does not
appear that the facilitation in responding to target form
changes can be explained by appealing to a zoom-lens
Because the data from only 6 of the 17 participants in this experiment
were available for this analysis, one could argue that the lack of an
effect of distractor position (inside or outside the convex hull of the
target set) was due to a reduction in statistical power. However, the
data from the same six participants indicated that target form changes
were responded to more rapidly than distractor form changes, f(\,
5) - 14.19, MSE - 22,103. Moreover, there was no effect of distractor
position in Experiment 2 either, where the data from 24 participants
were examined.
Sears and Pylyshyn
mechanism. Note that a similar result was also reported by
Intriligator and Cavanagh (1992), who used a variant of the
multiple object tracking task involving only two targets
moving in a rigid configuration. They reported that the
detection of simple luminance changes was facilitated when
they occurred on targets being tracked, but not in the region
between the two tracked objects.
DISCUSSION
Why are target form changes identified more rapidly than
distractor form changes during the tracking task? According
to our hypothesis, the indexes bound to target objects confer
an access priority to these objects, so that target objects can
be checked via focal attention before distractor objects when
a form change occurs. Distractor form changes will only be
identified by way of a serial self-terminating attentional scan
initiated after the indexed objects have been checked. Our
explanation is similar to that of Yantis and Johnson (1990),
who argued that a small number of abrupt onset items can
be attentionally tagged and then examined before no-onset
items in visual search displays, producing a processing
advantage for abrupt onset items. Similarly, our claim is that
the target objects are indexed at the start of each trial, and
that this indexing allows them to be continually referenced
when they are in motion. When a form change occurs, the
indexes bound to the target objects allow them to be directly
examined via focused attention, so that target objects are
checked before distractor objects for the presence of a form
change. Note that we are assuming that the form change
itself can be detected without the aid of focused attention.
More specifically, we contend that the initial registration of
the form change event is detected preattentively, perhaps by
a generalized difference operator which signals that some
general change in the display has occurred but does not
provide precise location or identity information (e.g.,
Atkinson & Braddick, 1989; Scialfa & Joffe, 1995). The
detection of the form change would then permit the alloca-
tion of focal attentional required to identify the type of
form change (E or H).
If the target objects are being checked before the
distractor objects when a form change occurs, then target
form changes will be identified more rapidly than distractor
form changes. This would seem to be a plausible account of
the data at hand, and yet this explanation is not consonant
with the fact that responses to target form changes were
increasingly delayed as the number of distractors in the
display increased. That is, if the target objects are always
checked before the distractors, then increasing the number
of distractor objects in the display should have no effect on
the speed at which target form changes are identified.
This application of the visual indexing model assumes,
however, that the target objects are flawlessly indexed and
tracked throughout every trial. This assumption is almost
certainly false, as the tracking task itself is subject to error,
Multiple Object Tracking
and failures of tracking do occur. It is probable that indexes
do not always remain permanently bound to the target
objects throughout the tracking task. Although we maintain
that indexing and tracking a small number of objects is
preattentive, maintaining those indexes over an extended
period of time requires some effort or reactivation to
prevent their decay or loss. A reasonable assumption is that
the probability of an index being lost or misplaced would
increase with the number and/or density of distractors in
the display, so that increases in the number of distractors
would lead to decreases in tracking performance. It is
known that the attentional resolution required for individu-
ating objects drops off rapidly with eccentricity (Intriligator,
1998), increasing the chances that when a target and a
distractor object pass close to one another in the periphery
of the display, the two objects may then momentarily fail to
be resolved. When the target and distractor subsequently
move away from each other, the index could be shifted to
the wrong object or lost altogether. If an index was shifted
to a distractor, the participant might then consider this
object a target, while the object that had been tracked might
then become a distractor.
If the index bound to a target object was lost or shifted,
and the same target subsequently underwent a form
change, then responses to this form change would be
delayed. In effect, this target would now be a distractor, and
responses to these nonindexed target form changes would be
slower than responses to indexed target form changes.
During the course of the experiment, a fair number of these
events may have occurred, increasing the average response
latencies to target form changes. More specifically, as the
number of distractors in the display increased, the probabil-
ity that an index would be lost or shifted to a distractor
would also have increased. Averaging the response latencies
to form-changing targets that were tracked with those that
were not tracked would create a spurious increase in
response latencies to target form changes with increases in
the number of distractors. In Experiment 2 we tested this
possibility.
Experiment 2
If imperfections in the process of indexing and tracking the
target objects was responsible for the display size effect
witnessed in Experiment 1, then such an effect should not
occur if the targets are perfectly tracked throughout every
trial. Although it is not possible to ensure that all the targets
are perfectly tracked, we have designed a procedure that
may, given certain assumptions, help us to determine
whether a particular target that underwent a form change
had in fact been correctly tracked on that particular trial. To
do this, we developed a dual-response procedure in which
we asked participants to indicate, on each trial, whether the
object that underwent a form change was a target or a
distractor. Thus, the participant was required to indicate the
identity of the changed object and also whether it was a
target or a distractor.
In this experiment, participants tracked four target
objects and made a speeded identification response to a form
change in the display (E or H), after which they made a two-
alternative forced choice categorization decision as to what
type of object underwent a form change (target or
distractor). Because participants had to indicate what type of
object underwent a form change on each trial, the trials
where participants had lost a form-changing target could be
distinguished from the trials where a form-changing target
was accurately tracked. If failures in tracking performance
produced the display size effect for target form changes in
Experiment 1, then excluding from the RT analyses those
trials where participants had lost a form-changing target
should greatly attenuate and perhaps even eliminate this
display size effect.
Of course, the success of this method depends on partici-
pants being able to categorize the changed objects as ones
they had or had not tracked. Because there will be some
trials on which participants may not be certain as to
whether the object that underwent a form change was a
tracked object, their category assignment may contain both
errors of omission and errors of commission. Such errors
may occur for several reasons. For example, a target may be
lost because the index simply becomes dissociated from it —
in which case the forced-choice categorization response may
merely reflect a guessing strategy. On the other hand, if the
target is lost because the index shifts to a nearby distractor,
the participant may unknowingly take the newly indexed
object to be a target or might even be aware of the shift and
exercise some wariness. Clearly, our ability to isolate those
trials in which the participant correctly tracked a form-
changing target will not be without some error. But so long
as the errors are relatively infrequent and the bias in the use
of the target versus distractor categories remains relatively
fixed over the conditions of the experiment, this method
may provide a useful way of restricting the trials used to
compute the R T measure to those which the participant has
actually tracked the form-changing target.
In summary, we can state our specific predictions as
follows: (a) If participants are more likely to lose target
objects as the number of distractors in the display increases,
then target categorization errors (incorrectly attributing
target form changes to distractors) should increase with
increases in the number of distractors. (b) Response latencies
to form-changing targets that are no longer tracked (and
thus not indexed) should be slower than those for accurately
tracked targets, (c) When the accuracy of the participants'
categorization decisions (target or distractor form change) is
ignored, response latencies to both target and distractor
form changes should increase with increases in the number
of distractors in the display (as in Experiment 1). (d) Exclud-
ing those trials where participants had lost a form-changing
target should greatly attenuate the effect of display size on
identification RT.
TABLE 2
Mean Response Latencies (in Milliseconds) and Identification Error Rates
(in %) to Target and Distractor Form Changes in Experiment 2
Sears and Pylyshyn
METHOD
Participants. Twenty-four individuals participated in this
experiment, and were paid $10 upon completion of the 45-
minute session. None of these individuals had participated
in Experiment 1.
Apparatus and stimuli. The apparatus and stimuli were
identical to those of Experiment 1.
Procedure. Participants tracked four targets during every
trial, and after 5 to 9 s of tracking, one of the objects was
transformed into an E or an H figure. The time to identify
this form change was measured. The probability that a
target or a distractor would undergo a form change was
identical (50%), and the participants were informed of these
probabilities.
After the participant had responded to the form change,
the screen was cleared and two boxes labeled "Tracker" (the
target) and "Non-Tracker" (the distractor) appeared. The
participant then indicated what type of form change had
occurred (target or distractor form change) by moving a
mouse pointer into the appropriate box and then pressing a
mouse button. This categorization decision was made on
every trial, and participants were instructed to guess when
they were unsure of the correct response.
Design. Two factors were manipulated: the type of form
change (target or distractor object) and the number of
distractors in the display (4, 8, or 12). There were 30 trials in
each of the six conditions of this experiment.
RESULTS AND DISCUSSION
The mean identification error rate (E or H) across all the
conditions was 3.9%, and a two-factor repeated measures
ANOVA (Type of Form Change x Number of Distractors)
yielded no significant effects, all Fs < 1. Response latencies
greater than 3 seconds were classified as outliers and were
removed from the dataset. This procedure resulted in the
removal of 3.1% of the data.
Identification data. Before looking at the categorization data,
we replicated the analysis that was done in the first experi-
ment. In this analysis, the accuracy of the participants'
categorization decisions (i.e., attributing the form changes to
target or distractor objects) was ignored when computing
average response latencies. Thus, the response latencies to
form-changing targets that were no longer being tracked (as
indexed by target categorization errors) were averaged with
the response latencies to targets that had been accurately
tracked. This treatment of the data should reproduce the
pattern of effects observed in Experiment 1: Responses to
Type of Form Change
4
8
Number
of Distractors
Target
M
SE
Errors
Distractor
M
SE
Errors
1410
65.1
4.4
1609
64.7
3.1
1481
62.1
4.1
1662
67.4
4.3
12
1469
59.7
3.6
1722
65.6
3.8
target form changes should be faster than responses to
distractor form changes, and response latencies to both
target and distractor form changes should increase with
increases in the number of distractors.
The results of this analysis were in fact identical to those
of Experiment 1. The mean response latencies and error
rates are listed in Table 2. A two-factor (Type of Form
Change x Number of Distractors) repeated measures
ANOVA revealed a significant main effect for the type of
form change, f(l, 23) = 46.52, MSE = 34,500, and the
number of distractors, F(2, 46) = 9.48, MSE - 9,920, but no
interaction between these two factors, F(2, 46) = 2.28,
MSE = 7,383. Participants identified target form changes an
average of 211 ms faster than distractor form changes, and
response latencies to both target and distractor form changes
increased with increases in the number of distractors. Note
that response latencies were longer in this experiment than
in Experiment 1, presumably because participants had to
determine the identity of the changed object (E or H) as well
as the type of changed object (target or distractor) prior to
responding.
Categorization data. Earlier we argued that target objects are
not always perfectly tracked during the tracking task, and
that the probability of losing one or more targets increases
with increases in the number of distractors. This assumption
can now be directly tested. Because participants made a
categorization decision on every trial to indicate whether a
target or a distractor underwent a form change, the trials in
which participants accurately tracked form-changing target
objects could be distinguished from the trials in which they
did not. For example, if a target object underwent a form
change and the participant categorized the event as a
distractor form change, then the participant had not accu-
rately tracked this particular target throughout the trial.
Responses in which the participant correctly identified the
form change (E or H) but incorrectly categorized the type of
form change (target or distractor) were classified as categori-
zation errors.
Table 3 lists the percentage of target and distractor
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