VISUAL COGNITION, 2006, 14 (4/5/6/7/8), 781 807

Visual memory for natural scenes: Evidence fromchange detection and visual search
Andrew Hollingworth
University of Iowa, Iowa City, IA, USA

This paper reviews research examining the role of visual memory in scene
perception and visual search. Recent theories in these literatures have held that
coherent object representations in visual memory are fleeting, disintegrating upon
the withdrawal of attention from an object. I discuss evidence demonstrating that,far from being transient, visual memory supports the accumulation of informationfrom scores of individual objects in scenes, utilizing both visual short-term memory
and visual long-term memory. In addition, I review evidence that memory for the
spatial layout of a scene and memory for specific object positions can efficiently
guide search within natural scenes.

In the past decade, the interaction between perception and memory hasreceived a great deal of attention from cognitive scientists. Much of thisinterest has originated from increased understanding that perception is a
dynamic, serial process, extended over space and time. In this paper, I will
discuss two related lines of research in which the relationship between
perception and memory has come to the fore: Scene perception and visual
search. While viewing natural scenes, the eyes shift (via saccadic eye
movements) approximately three times each second to bring different scene
regions onto the fovea, where visual acuity is highest (see Henderson &Hollingworth, 1998, for a review). Across saccades, visual encoding is
suppressed (Matin, 1974), dividing visual input into discrete episodes. Given
the discrete, serial nature of scene perception, memory is required to retain
and accumulate visual information from local objects as the eyes andattention are oriented from object-to-object within a scene. Similarly, visual
search typically requires the serial allocation of attention to individual
objects in the course of finding a target and rejecting distractors (Woodman& Luck, 2003). Roles for memory in search include keeping track of objects
Please address all correspondence to Andrew Hollingworth, Department of Psychology,
University of Iowa, 11 Seashore Hall E, Iowa City, IA 52242-1407, USA.
E-mail: andrew-hollingworth@uiowa.edu
This research was supported by National Institute of Health Grant R03 MH65456.
http://www.psypress.com/viscog# 2006 Psychology Press Ltd
DOI: 10.1080/13506280500193818

782

HOLLINGWORTH

that have already been examined (e.g., Klein, 1988) and guiding attention totargets that appear in predictable locations (e.g., Chun & Jiang, 1998).
The following discussion of scene perception, search, and visual memory
will be placed within the context of recent claims that visual memory istransient, playing little or no role in the representation of natural scenes or insearch (Rensink, 2000; Wolfe, 1999). I will review evidence that, contrary tothese claims, visual memory supports robust accumulation of visual
information from scores of individual objects in scenes and that visualmemory can efficiently guide search within real-world environments.

SCENE PERCEPTION, CHANGE BLINDNESS, AND
VISUAL MEMORY
The phenomenon of change blindness has shaped recent thinking on the roleof visual memory in scene perception. In change blindness studies,participants often fail to detect otherwise salient changes when detection
depends on visual memory. Dependence on memory has been achieved
either by introducing an interstimulus interval (ISI) between differing images(e.g., Rensink, O’Regan, & Clark, 1997), by introducing a change to an
image during an eye movement (e.g., Grimes, 1996; Henderson & Hollingworth, 1999, 2003c), or by occluding a change with a physical object (e.g.,
Simons & Levin, 1998). In perhaps the most well-known change blindness
paper, Rensink et al. (1997) presented photographs of real-world scenes andintroduced a change to a portion of the image on each trial, such as thedeletion of an airplane’s engine in an airport scene. Each image in this flicker
paradigm was presented for 240 ms, with an 80 ms neutral grey ISI. Thechange was repeated by alternating the two images until the participant
detected the change (i.e., the airplane’s engine would disappear, thenreappear, then disappear, and so on). Rensink et al. found that for manychanges, participants required extended viewing (often more than 30 s)
before they detected the change. Researchers have concluded from such
effects that very little visual information (at an extreme, no visual
information, O’Regan, 1992; O’Regan & Noë, 2001) is retained from one
view of a scene to the next, and therefore that visual representations ofcomplex, natural scenes must be impoverished (Becker & Pashler, 2002;
Irwin & Andrews, 1996; Rensink, 2000, 2002; Rensink et al., 1997; Simons,
1996; Simons & Levin, 1997; Wheeler & Treisman, 2002; Wolfe, 1999).
Rensink (2000, 2002) has provided the most theoretically elaborated
account of this visual transience hypothesis. Rensink’s view, which he terms
coherence theory, provides a broad account of vision, attention, and memory.
Coherence theory can be distilled into the following set of claims. First,
low-level vision produces a description of the visual field in terms of

VISUAL MEMORY FOR NATURAL SCENES

783

proto-objects. Proto-objects, according to Rensink, are the earliest form ofobject representation in the visual system, in which local regions of the visual
field are parsed, and sensory features corresponding to individual objects areloosely assembled. Proto-objects are computed in a bottom-up fashion, inparallel across the visual field, and independently of attention. They are also
highly volatile: Proto-objects decay very quickly after visual stimulation isremoved and are highly susceptible to interference from new sensory
information (i.e., they are susceptible to backward masking, which Rensink
terms overwriting ). In the context of change detection, proto-objects are notby themselves sufficient to detect a change, because they will typically havedecayed prior to the appearance of the changed image, or if they have not yet
decayed, sensory processing of the new image will mask them (overwritethem), leaving the visual system with no informational basis upon which todetect the change.
Further, coherence theory holds that visual attention is critical to theconsolidation of proto-objects into a coherent, robust representation that
does not immediately decay and is not susceptible to backward masking.
When attention selects a set of proto-objects that correspond to an object inthe visual field, the object’s features, which were only loosely assembled prior
to the allocation of attention, are bound into a coherent object representation (which Rensink terms a nexus ). Activation from the nexus feeds back to
the proto-objects, and this recurrent flow of information to the proto-object
level allows proto-objects to be maintained robustly across delays and toresist masking from subsequent sensory input. Rensink terms the nexus plus
stabilized proto-objects a coherence field . Under coherence theory, it is thecontinued maintenance of these proto-objects in the coherence field thatallows one to perceive a change to an attended object in a scene acrossdisruptions such as a brief ISI or eye movement.
Finally, coherence theory holds that once attention is removed from an
object, the coherent object representation (the nexus) comes unbound, therecurrent activation from the nexus to the proto-objects is lost, and theproto-objects return to their original state as fleeting and susceptible tomasking. Thus, perceiving a change to a previously attended object isequivalent to perceiving a change to an object that has never been attended.
In neither case is there a coherent representation to support change
detection: ‘‘After focused attention is released, the object loses its coherence
and dissolves back into its constituent proto-objects. There is little or no‘after effect’ of having been attended’’ (Rensink, 2000, p. 20). AlthoughRensink claims that coherent visual representation is limited to the currently
attended object, he does allow that other forms of representation may beretained robustly from a complex scene. In particular, the gist (or basic
identity of a scene, such as ‘‘kitchen’’ or ‘‘airport’’) is remembered robustly,
as well as the abstract spatial organization of the scene, or layout. But

784

HOLLINGWORTH

neither of these representations preserves information about the visual
details of individual objects in the scene.
In summary, coherence theory claims that a coherent visual representation sufficient to support change detection is available only for the currently
attended object. When attention is withdrawn from an object, featurebinding comes undone, and any coherent object representation dissolves
back into its constituent features. As a result, visual representations do notaccumulate as attention is oriented from object-to-object within a scene, andvisual scene representations are therefore impoverished, leading to change
blindness. These claims are not unique to coherence theory. Many otherresearchers have made similar proposals regarding the transience of visualmemory. Some prominent examples include the following.
. ‘‘Binding information [in memory] can be lost when new visual objects
are presented and attention is withdrawn, causing bound objects to fallapart. Bound visual objects may survive in memory across distraction
only when they are recoded into a nonvisual form, such as a verbal
label.’’ (Wheeler & Treisman, 2002, p. 62). ‘‘. . . when a scene is viewed, observers create two representations. One
contains the gist, or meaning, of a scene, and the other represents thevisual details of a small portion of the scene. The gist representation isthought to be relatively stable, and more conceptual than visual innature (Wolfe, 1998). By contrast, the representation of visual details isthought to be volatile and fleeting. At any instant, observers represent
only the small portion of the visual observer’s attention shifts, so do thecontents of VSTM [visual short-term memory], leaving no memory or
representation of the previously attended visual stimuli . . ..’’ (Becker &Pashler, 2002, p. 744)
. ‘‘During any fixation, we have a rich visual experience. From that visualexperience, we abstract the meaning or gist of a scene. During the next
visual fixation, we again have a rich visual experience, and if the gist is
the same, our perceptual system assumes the details are the same.’’(Simons & Levin, 1997, p. 267)
. ‘‘When attention is deployed elsewhere, the visual representation of an
object appears to revert to its preattentive state.’’ (Wolfe, 1999, p. 78)
As is evident from these statements, the concept of visual transience and its
consequences (such as visually impoverished representations of scenes) havebeen highly influential in recent thinking on vision and memory. Thus, thevisual transience view deserves close scrutiny. My discussion will focus on
Rensink’s coherence theory, because it is the most prominent and mostclearly specified visual transience theory, but coherence theory stands for a

VISUAL MEMORY FOR NATURAL SCENES

785

larger class of theory that has been widely accepted within the visioncommunity.

Visual memory systems
Before discussing whether visual memory is indeed transient, it is necessary
to briefly review current knowledge regarding the properties of visualmemory. Visual memory appears to be composed of four different memory
stores: Visible persistence, informational persistence, visual short-term
memory (VSTM), and visual long-term memory (VLTM) (see Irwin,
1992b, for an excellent review). Visible and informational persistence areoften grouped together as iconic memory or, preferably, sensory persistence(Coltheart, 1980). Visible persistence and informational persistence preservea precise, high-capacity, sensory trace that is generated across the visual field
but is highly volatile. Visible persistence, as the name suggests, isphenomenologically visible (that is, one sees a stimulus as visibly persisting
after it has been removed). The duration of visible persistence is extraordinarily brief, decaying within approximately 80 100 ms after the onset ofa stimulus (Di Lollo, 1980). Informational persistence is a nonvisible sensory
trace that persists for approximately 150300 ms after stimulus offset (Irwin& Yeomans, 1986). Both visible persistence and informational persistence
are highly susceptible to interference from new sensory processing; they aresusceptible to backward masking. VSTM maintains visual representations
abstracted away from precise sensory information. It has a limited capacityof 3 4 objects (Irwin, 1992a; Luck & Vogel, 1997; Pashler, 1988) and lessspatial precision than point-by-point sensory persistence (Irwin, 1991;
Phillips, 1974). However, VSTM is not significantly disrupted by backward
masking (Pashler, 1988; Phillips, 1974) and can be maintained over durations
on the order of seconds (Phillips, 1974) and across saccades (Irwin, 1992a).
VLTM appears to maintain visual representations similar to those maintained in VSTM (see Hollingworth, 2004) but with the capability toaccumulate visual information from scores of individual objects (Hollingworth, 2004, 2005b). Note that of the four visual memory stores, only visible
persistence directly supports visual phenomenology. Other forms of visualmemory certainly maintain visual information, but they do not directly
support visual experience. With the exception of extraordinarily brief visible
persistence, visual memory is nonvisible.
Aligning coherence theory with the visual memory literature, it is clearthat proto-objects in coherence theory map onto sensory persistence (i.e.,
visible persistence and/or informational persistence). Both proto-objects andsensory persistence are low-level visual representations generated in parallel
across the visual field independently of attention, both are highly volatile,

786

HOLLINGWORTH

and both are susceptible to backward masking. Further, the coherence field(the nexus plus the stabilized proto-objects) directly maps onto VSTM
(Rensink, 2000).1 Thus, coherence theory can be rephrased in the following
manner using terminology from the visual memory literature. When looking
at a visual image, low-level sensory representations are generated across thevisual field. When the image is removed, low-level sensory (iconic)
persistence is fleeting and is highly susceptible to masking. However, thevisual system can consolidate a small number of visual objects into a morestable store, VSTM, which can then be maintained in the service of explicit
report or comparison in a change detection task.
From this perspective, visual transience theories are perfectly consistent
with existing visual memory research. Early research on sensory memory(Averbach & Coriell, 1961; Sperling, 1960) found that for briefly presented
arrays of letters, low-level sensory representations were generated across thevisual field, but after stimulus removal, these low-level representations were
fleeting and highly susceptible to masking. Approximately three or four
letters, however, could be attended and consolidated into a more stable
memory store (which we now term VSTM) that could support letter reportat longer delays. The transience of sensory representations has been abackground assumption in the visual memory literature for the last 40 years.
Thus, the novelty of visual transience theories lies not in the claim that
sensory representations are fleeting, but rather in the claim that after an
object is attended and consolidated into VSTM, that object representation
comes unbound when attention is withdrawn, leaving no trace of thecoherent object representation that had been previously formed. Undervisual transience theories, it is this absence of visual accumulation that
explains poor detection in change blindness studies. One must be attending
the object that changes in order to detect the change, because a coherent
object representation is maintained only for the currently attended object.
From this review of visual memory systems, it is clear that the only
plausible candidates for the accumulation of visual information during scene
viewing are VSTM and VLTM; visible and informational persistence decay
too quickly and are highly susceptible to masking. The critical question,
1
The coherence theory view of VSTM differs from standard models in that coherence theory
claims low-level sensory representations (proto-objects) constitute a component of VSTM;
attention allows the continued maintenance of proto-objects for the attended object in VSTM
(Rensink, 2000). However, a great deal of evidence demonstrates that even for attended objects,
VSTM is abstracted away from precise sensory information (Henderson, 1997; Henderson &Hollingworth, 2003b; Irwin, 1991; Phillips, 1974). For example, Phillips (1974) presented single,
checkerboard objects at fixation in a change detection task. The checkerboard object was clearlyattended, since it was the only stimulus on display. Yet, Phillips found that high-capacity sensory
persistence was fleeting, and that VSTM maintained representations abstracted away fromsensory persistence, even for an attended object.

VISUAL MEMORY FOR NATURAL SCENES

787

then, is whether VSTM and VLTM are indeed used to accumulate visual
information from individual objects as the eye and attention are oriented
from object-to-object within a scene, or whether visual object representations come unbound upon the withdrawal of attention, with little or noaccumulation of visual information.

Robust visual memory for objects in natural scenes
My colleagues and I have conducted a series of studies to answer thisquestion (see Henderson & Hollingworth, 2003b, for an earlier review).
Hollingworth and Henderson (2002; Hollingworth, Williams, & Henderson,
2001b) examined the basic issue of whether coherent visual object
representations can be maintained after the withdrawal of attention. In
these experiments, eye movements were monitored while participants viewed
computer-generated depictions of real-world scenes. Figure 1 shows asample scene. The computer waited until the participant had directly fixateda target object in the scene (to ensure it had been attended). Subsequently,
the target object was changed during a saccade to a different (nontarget)
object in the scene. Because visual attention is automatically and exclusively
allocated to the goal of a saccade prior to the initiation of that eye
movement (e.g., Hoffman & Subramaniam, 1995), the target object was nolonger attended when the change occurred; attention had shifted to thenontarget object that was the goal of the saccade. The target object waschanged either by rotating it 908 in depth or by replacing it with anotherobject from the same basic-level category (token change). Rotation change
detection, in particular, required memory for the visual details of the target,
since the changed target differed from the original target only in orientation.
Coherence theory predicts that these object changes should not have been

Figure 1. Sample stimuli from studies of visual memory and scene perception (Hollingworth, 2003a,
2004, 2005b; Hollingworth & Henderson, 2002). Panel A shows the initial scene. Panel B shows an
object change (rotation in depth of the toy truck).

788

HOLLINGWORTH

detectable, because attention had been withdrawn from the target object
prior to the change. Yet, participants were able to successfully detect token
and rotation changes on a significant proportion of trials, demonstratingthat visual memory accumulates visual representations from previously
attended objects in scenes.
In a converging experiment (Hollingworth & Henderson, 2002), a
previously attended target object was masked during a saccade to a different
object in the scene. Two object alternatives were then displayed sequentiallywithin the scene. One was the original target, and the other was either adifferent token or different orientation distractor. Despite the fact that
attention was no longer directed to the target when it was masked,
participants performed the discrimination tasks at rates above 80% correct.
Further, accurate discrimination performance was observed even when many
fixations on other objects intervened between target fixation and test. When
more than nine fixations on other objects intervened between target fixationand test, token discrimination performance was 85.3% correct and orientation discrimination performance was 92.3% correct. Memory for the visual
details of previously attended objects was clearly robust across shifts ofattention and of the eyes.
The experiments in Hollingworth and Henderson (2002) depended on therelationship between eye position and attention (that attention covertly
precedes the eyes to a saccade target) to ensure that tested objects were notcurrently attended at test. Hollingworth (2003a) used a converging method
to control the allocation of attention. Participants viewed a scene for 20 s.
Then, a bright green dot appeared abruptly in the scene at a locationdifferent from that of the target object. The target object was then masked,
and the mask was removed to reveal either the target object changed(rotation or token change) or unchanged. The task was change detection.
Given evidence that abruptly appearing objects capture attention (e.g.,
Yantis & Jonides, 1984), attention should have been allocated to the dot
onset, and not to the target, when the target was masked. A control
experiment demonstrated that participants did indeed shift attention to theonset dot. Finally, a four-digit verbal working memory load minimized thepossibility of verbal encoding. Despite the fact that the target was not
currently attended, change detection performance was very high indeed.
Percentage correct data were used to calculate A ?, a signal detection measurethat models proportion correct in a two-alternative forced choice paradigm
and varies from .5 (chance) to 1.0 (perfect sensitivity) (Grier, 1971). Mean A ?
was .91 for token change detection and .87 for rotation change detection,
consistent with the results of Hollingworth and Henderson. Again, visual
representations sufficient to make subtle judgements were retained reliably
after the withdrawal of attention.

VISUAL MEMORY FOR NATURAL SCENES

789

Accurate memory for the visual form of objects in these studies wasalmost certainly due to the retention of abstracted visual representations in
VSTM, in VLTM, or in both. To examine the relative contributions of
VSTM and VLTM to the online visual representation of natural scenes,
Hollingworth (2004) used a serial position manipulation to control thesequence of objects fixated and attended within a scene. On each trial of thisfollow-the-dot paradigm, participants followed a small, bright green dot as it
visited a series of objects in a scene, shifting gaze to fixate the object mostrecently visited by the dot. Each object in a scene was visited once, and thesequence of objects was designed to mimic a natural eye movement scanpath on the scene. The serial position of a target object in the sequence wasmanipulated. The dot could appear on the target relatively early in viewing
or relatively late. After the sequence was completed, the target object was
masked, and the mask was then removed to reveal either the original target
object or a different object token.2 Again, the task was change detection.
If VSTM contributes to online scene representation, then one would
expect the objects attended most recently before the test to be rememberedmost accurately, a recency effect characteristic of retention in short-term
memory (Murdock, 1962; Phillips & Christie, 1977). If VLTM contributes
to online scene representation, then one would expect memory for objects
fixated early in viewing to be consistently above chance and to reflectretention beyond typical 3 4 object estimates of VSTM capacity. This isexactly what was found. The basic pattern of results from Hollingworth(2004) is depicted in Figure 2. Object memory was consistently superior forthe two objects fixated most recently before the test. This recency
advantage indicates a VSTM component to online scene representation,
apparently limited to two objects.3 Objects examined earlier than twoobjects before the test were nonetheless remembered at rates well above
chance (A ? / /.80), and there was no evidence of further forgetting withmore intervening objects. That is, performance was equivalent for objects
fixated between three objects before the test and ten objects before the test.
At ten objects before the test, memory capacity easily exceeded 34 object
estimates of VSTM capacity. This robust prerecency performance therefore
indicates a VLTM component to online scene representation. Irwin and
Zelinsky (2002; see also Zelinsky & Loschky, 1998) have found similareffects for object position memory. Thus, VSTM appears to support
memory for the visual form of the last two objects fixated and attended ina scene, with memory for objects attended earlier supported by VLTM.
2Memory for object orientation was also tested in a two-alternative forced-choice task andproduced the same serial position effects as token change detection.
3
This estimate is consistent with independent estimates of VSTM capacity for complex
objects (Alvarez & Cavanagh, 2004).

790

HOLLINGWORTH
Perfect sensitivity

Change Detection Accuracy

Two-object recency advantage
characteristic of VSTM retention

Robust pre-recency performance
characteristic of VLTM retention

Chance

0

1

2

3

4

5

6

7

8

9

10

Number of Objects Intervening between Target Fixation and Test

Figure 2. Stylized depiction of the serial position results from Hollingworth (2004), plotting change
detection accuracy against the number of objects fixated between target fixation and test. Zero objects
indicate that the last object attended in the scene was tested.

Given this significant role for VLTM in the online representation ofnatural scenes, how robustly are the visual details of individual objects
retained in long-term memory (LTM)? In the 1960s and 1970s, studies ofpicture memory found that LTM could support the retention of multiple
thousands of individual photographs (Nickerson, 1965; Shepard, 1967;
Standing, 1973; Standing, Conezio, & Haber, 1970). The distractor pictures
used in these experiments, however, were typically chosen to be highly
different from studied images, making it difficult to identify the type ofmemory supporting recognition. Based in part on change blindness effects,
recent discussions of this literature have tended to ascribe accurate long-term
picture memory to retention of scene gist rather than to retention of thevisual details of the photographs (Chun, 2003; Potter, Staub, & O’Connor,
2004; Simons, 1996).
To examine the capacity of LTM for the visual details of individual
objects in natural scenes, Hollingworth (2004) used the follow-the-dot
method but delayed the change detection test until the end of the session,
after all scenes had been viewed. In this condition, more than 400 objects, on
average, were examined between target examination and test. Of course,
participants did not know which of these objects would be tested until thetest occurred. Despite these considerable memory demands, participants
performed the token change detection task at a rate well above chance(A ?/.75), which was only moderately lower than change detection
performance when object memory was tested during scene viewing. We

VISUAL MEMORY FOR NATURAL SCENES

791

have recently delayed object change detection tests (both token andorientation changes) for 24 hours (Hollingworth, 2005b), and change
detection performance was still above chance (A ?/ /.70). To put this inconcrete terms, after having viewed 48 different scenes and hundreds ofindividual objects, and after a delay of 24 hours, participants can still detectthat the toy truck in the bedroom scene (Figure 1) has changed orientation.
Clearly, VLTM is not limited to scene gist.
These results are in striking contrast to visual transience claims in thechange blindness literature. For example, Simons and Levin (1997)
speculated that only the basic meaning of a scene (the gist, such as bedroom )may be retained across a saccade from one eye fixation on a scene to thenext, a delay of only 2060 ms. Instead, participants are capable of retaining
the visual details of hundreds of individual objects (Hollingworth, 2004)
across delays of at least 24 hours (Hollingworth, 2005b).

Understanding change blindness
Evidence of robust visual memory for the visual details of individual objects
in scenes naturally leads one to consider why change blindness would ever beobserved in the first place. Change blindness is a relative phenomenon.
Rarely are subjects entirely insensitive to changes.4 For example, in the first
demonstration of change detection failure within real-world scenes, Grimes(1996) and McConkie (1991) found that some changes were detected by only
25% of participants, whereas others were detected by as much as 80% ofparticipants. Similarly, in a prominent example of an incidental, real-worldchange paradigm, Simons and Levin (1998) found that approximately 50%
of participants failed to detect the replacement of one person for another.
Failures of change detection are typically juxtaposed with an ideal oferror-free change detection. But what would be necessary to ensure errorfree change detection? Error-free change detection across temporal disruptions such as saccades and brief ISIs requires (at least) two representations
and a comparison operation. First, the initial image must be represented invisual memory across the disruption. Second, the perceptual information
available in the second image must be represented after the change has been
introduced. Third, a comparison process must operate to detect discrepancies between the two representations. To ensure error-free change detection
performance, the memory representation would need to be a precise andcomplete record of the visual information available in the initial image.
Similarly, the perceptual representation from the test image also would need4
See Henderson and Hollingworth (2003b) for one of the few cases in which change
blindness is apparently absolute.

792

HOLLINGWORTH

to be precise and complete. Finally, the comparison process would need tooperate over the entire extent of both representations.
But this model of error-free change detection performance has little apriori plausibility. The human retina encodes high-resolution visual information only over a very small region of the visual field (Riggs, 1965).
Consider the case in which a participant is provided a single fixation on ascene before the introduction of a change. Even if the memory representation of the initial image has perfect fidelity, with no visual information loss
across the delay, a change could be missed because the changing object lay inthe periphery of the visual field and information of sufficient resolution had
not been available.
To rescue our model of error-free change detection, we might proposethat if participants were allowed multiple fixations on the initial image prior
to the change, high-resolution, sensory information from foveated regions
might be integrated to form a composite representation retaining precise
information across much of the visual field. This type of composite global
image model was proposed in the 1970s (e.g., McConkie & Rayner, 1976),
primarily to explain the phenomenology of seeing a complete and detailed
visual world across eye movements. Such a model has typically been
considered to predict error-free change detection (that is, change blindness
has been taken as evidence against this model). However, even with theability to construct a composite image, changes may go undetected if thechanging region has not been fixated prior to the change. Since a composite
image would require fixation of many local scene regions, its construction
would take a significant amount of time (on the order of seconds), and many
regions of a scene are not fixated even given extended viewing of 20 s or
longer (Henderson & Hollingworth, 1998). And, even if the critical region
had been fixated prior to the change, if the critical region is not fixated in thetest image, then the change could go undetected despite the retention of
precise sensory information in memory. Thus, the physical structure of theeye and the serial nature of foveal object processing makes it highly unlikelythat one could ever achieve error-free change detection performance, evenwith the most generous model of visual memory and comparison processes.
Things get worse for change detection, because we have known for many
years that the visual system does not build up precise representations ofscenes by integrating high-resolution, foveal information from fixatedregions. As reviewed above, precise sensory memory is fleeting (Averbach& Coriell, 1961; Di Lollo, 1980; Sperling, 1960) and simply does not lastlong enough to support sensory integration across multiple fixations. In theearly 1980s, researchers directly tested whether visible persistence isintegrated from one fixation to the next, as would be needed to constructa visible, composite sensory image of a scene (Bridgeman & Mayer, 1983;
Irwin, Yantis, & Jonides, 1983; McConkie & Zola, 1979; O’Regan & Lévy-

VISUAL MEMORY FOR NATURAL SCENES

793

Schoen, 1983; Rayner & Pollatsek, 1983; see also Henderson, 1997;
Henderson & Hollingworth, 2003c; Irwin, 1991). For example, Irwin et al.
(1983) found that participants could not integrate two complementary
patterns of dots when the dots were presented in the same spatial position
but on subsequent fixations, demonstrating that the type of sensory
integration possible within a fixation at short SOAs (Di Lollo, 1980) does
not occur across separate fixations.
Recently, Henderson and Hollingworth (2003c) sought to put the issue of
sensory accumulation across eye movements to rest. Participants were shown
images of common environments, with each image partially occluded by aset of vertical grey bars (as if viewing the scene from behind a picket fence).
During eye movements, the occluded and visible portions of the image werereversed, so that all previously occluded regions of the scene became visible
and all previously visible regions occluded. This change drastically altered
the low-level content of the entire image (the value of every single pixelchanged) but preserved more abstract visual information, such as the generalshape of objects and the spatial relationships between objects. Participants
were almost entirely insensitive to these changes, demonstrating that visualmemory across eye movements is abstracted away from precise sensory
information. If sensory representations are not retained and integrated
across an eye movement, then sensory information could not be accumulated across multiple fixations to form a composite, global image of a scene.
Actually, one can easily demonstrate that high-resolution, foveal information is not integrated across saccades to form a visible, composite
image. Choose two smallish objects (any two objects will do) and place themat two different positions about a foot apart on a nearby surface. Fixate
object 1, and without removing fixation, attend to the quality of visualexperience for object 2. Object 2 will be projecting to a region of the retina
with relatively low resolution, so it should appear fuzzy and indistinct. Now,
shift fixation to object 2 and fixate it as long as you care to. Shift fixationback to object 1, and again attend to the quality of perceptual experience forobject 2. It should still appear fuzzy and indistinct, precisely as it did before
it was directly fixated. The high-resolution foveal information encoded
during the fixation on object 2 was not retained to support subsequentdetailed perceptual experience of that object: No composite sensory image
was formed.
This demonstration illustrates an additional point about visual experienceand change detection. Change blindness is often thought to be surprising
given the fact that we see a detailed visual world across the visual field. But,in fact, we do not see detail across the visual field, even for previously fixated
objects. People see a complete and detailed visual world only in the loose
sense that they are not typically aware of the fact that they are notexperiencing detail across the visual field (see Dennett, 1991). But it takes

794

HOLLINGWORTH

only a modicum of effort to realize that peripheral vision is sketchy andindistinct. It would be more accurate to say that we perceive the visual world
as being complete and detailed. This is a perfectly valid inference (Cohen,
2002), because the world itself is indeed complete and detailed, even if we donot see all of that detail at once.
To summarize, there is simply no plausible model of visual memory andcomparison that would produce error-free change detection in change
blindness paradigms. Some degree of change blindness is inevitable. But thatstill leaves open the question of why, given evidence of robust memory forthe visual form of objects (Hollingworth, 2003a, 2004, 2005b; Hollingworth& Henderson, 2002), change detection can be as poor as it often is. For
example, the appearance and disappearance of the engine on an aeroplane ina flicker paradigm should certainly be detectable by the retention of a higher
level visual representation in VSTM or VLTM. Why, then, do participants
often fail to notice this sort of change?
Failures of encoding. Change blindness may occur in many circumstances because the local information from the target region has yet to be
encoded when a change occurs. Hollingworth and Henderson (2002; see also
Hollingworth, Schrock, & Henderson, 2001a) examined change detection
performance as a function of whether the target object had been fixatedprior to the change. Changes to previously fixated objects were detected at
rates well above chance. However, changes to unfixated objects were detected
at a rate no higher than the false alarm rate, suggesting that without direct
fixation, information sufficient to detect a change was rarely encoded. It may
take participants many seconds to fixate each of the potentially changing
objects in a scene, explaining delays in detection of repeating changes, suchas those in the flicker paradigm. These observations are consistent with thegeneral claim that attention is important for change detection (Rensink
et al., 1997), especially when one considers that fixation position and thespatial allocation of visual attention are tightly linked. Although attention
may be critical for forming a visual memory representation sufficient todetect most changes, that does not imply that visual representations comeunbound after the withdrawal of attention, however.
Failures of retrieval and comparison. Even if one forms a visual memory
representation of sufficient accuracy to detect a change, it may be no trivial
matter retrieving that representation and comparing it with current
perceptual information in order to detect a change. Early change blindness
studies assumed that explicit change detection provided an exhaustive
measure of visual memory. A number of converging sources of evidence,
however, demonstrate that explicit change detection significantly under-

VISUAL MEMORY FOR NATURAL SCENES

795

estimates visual memory and that retrieval and comparison failures are asignificant cause of change blindness.
First, changes may go undetected despite accurate memory, because thechanged object is not attended or fixated after the change. In Hollingworthand Henderson (2002), single changes to objects in scenes often went
undetected until the object happened to be refixated later in viewing. Such adelay, if it had been observed in a flicker paradigm, would have been
considered extended change blindness, yet the ultimate detection of thechange demonstrated that participants had a memory representation of therelevant object; that representation just was not retrieved and compared tocurrent perceptual information until attention and the eyes were directedback to the target.
Hollingworth (2003a) directly examined the role of retrieval andcomparison failure in change blindness using a change detection paradigm
in which a scene was viewed for 20 s, followed by a brief mask and a test
scene. The target object in the test scene was either the same, rotated, or
replaced by a different token. In addition, the target object in the test scene
was either postcued by a green arrow or not postcued. The latter method istypical of change blindness experiments. Without a postcue, participants had
to decide whether any object in the scene had changed. With a postcue,
participants only needed to determine whether the cued object had changed.
If change blindness is caused, at least in part, by failed retrieval andcomparison, then change detection should be improved when retrieval andcomparison demands are minimized by the postcue. This was indeed thecase, with significantly higher change detection performance in the postcue
condition. In addition, with the benefit of a postcue, change detection
performance approached ceiling, both for token and orientation change
detection; change blindness was largely eliminated.
Converging evidence that explicit change detection underestimates visualmemory comes from three studies conducted by Simons, Levin, andcolleagues. In Simons, Chabris, Schnur, and Levin (2002), a naı̈ve participant
engaged in conversation with a person carrying a basketball. The basketball
was covertly removed during a disruption, and the participant was thenasked to report any odd events or changes. If such general questions did notyield report of the removed basketball, the participant was asked specifically
about the basketball. With a direct retrieval cue, participants could thenoften report specific perceptual details of the basketball, even though they
did not notice that it had been removed. Similarly, Angelone, Levin, andSimons (2003) found that when participants failed to detect the replacementof one person for another, they could still choose the original person in aforced-choice test at levels above chance. Mitroff, Simons, and Levin (2004),
using a computer-based object change detection task, found that on some

796

HOLLINGWORTH

miss trials, participants had sufficient pre- and postchange information to
detect a change but had not adequately compared those representations.
Finally, evidence of preserved memory in the face of change blindness isobserved using measures more sensitive than explicit report of change(Fernandez-Duque & Thornton, 2000; see Thornton & Fernandez-Duque,2002, for a review). When a change is not reported, participants are slower toincorrectly report ‘‘same’’ for an object that changed than to correctly report‘‘same’’ for an object that did not change (Williams & Simons, 2000). And
when a change is not reported, fixation durations on a changed object are
longer than on the same object when it has not changed (Hayhoe, Bensinger,& Ballard, 1998; Henderson & Hollingworth, 2003a; Hollingworth et al.,2001b; Ryan, Althoff, Whitlow, & Cohen, 2000). There is currently debate
about whether these effects indicate that there are multiple change detection
mechanisms (i.e., an implicit mechanism and an explicit mechanism), or
whether the data can be accounted for by a single change detection
mechanism (Fernandez-Duque, Grossi, Thornton, & Neville, 2003; Fernandez-Duque & Thornton, 2003; Mitroff, Simons, & Franconeri, 2002).
Regardless of the resolution of this debate, effects of change on indirect
measures (such as RT and fixation duration) in the absence of explicit reportof change demonstrate that explicit change detection underestimates visualmemory.
Effects of unreported change on indirect measures may be generated bythreshold mechanisms for signalling change in the world. Dynamic vision
often introduces perceptual discrepancies that could be attributed either tointernal error or to external change. For example, when making a saccade toan object, the eyes often fall short of the target of the eye movement. Afterthe completion of such an eye movement, the saccade target object does notlie at the centre of gaze. This circumstance could be due to the inaccuracy ofthe eye movement (internal error), but it could also be due to the movementof the target object during the saccade (external change). By actually shifting
saccade targets during saccades, researchers have revealed that the visualsystem sets a threshold for attributing position discrepancy to a change inthe world. If the displacement of the saccade target is greater thanapproximately one-third of the distance of the saccade, participants arelikely to perceive the target to have moved, attributing the discrepancy tochange in the world (e.g., Bridgeman, Hendry, & Stark, 1975; McConkie &Currie, 1996). Below that threshold, the visual system remains sensitive tothe displacement (a corrective saccade is executed to bring the saccade target
onto the fovea), but participants are rarely aware of the displacement or of
the corrective saccade. Thus, for small discrepancies that are likely to havebeen caused by motor error, the visual system does not attribute thediscrepancy to a change in the world, and participants do not perceive thetarget object to have moved. Despite sensitivity to the discrepancy andVISUAL MEMORY FOR NATURAL SCENES

797

appropriate correction, participants are ‘‘blind’’ to the change. Further, if
external change is made clearly evident by blanking the target briefly after
the eye movement, participants are more likely to attribute the discrepancy
to a shift of the target, and explicit awareness of the shift is dramatically
improved (Deubel, Schneider, & Bridgeman, 1996).
Threshold mechanisms can also be observed in the phenomenon ofinsensitivity to incremental change (Hollingworth & Henderson, 2004). In a
version of the flicker paradigm, Hollingworth and Henderson graduallyrotated an entire scene, with each image incremented by 1 degree oforientation. Participants were remarkably insensitive to these gradualchanges, often coming to treat significantly different views of a room (i.e.,
ones in which many of the original objects had rotated out of view) as
unchanged continuations of the initial view. Despite failure to detect theincremental rotation, memory was nevertheless sensitive to the difference
between views. With incremental rotation, scene memory came to reflect the
recent, changed state of the environment rather than the initial state. Thisimplicit updating of memory to reflect the most recent state of theenvironment meant that comparison typically operated over similar representations: The currently visible image was compared to memory for themost recent image(s). Thus, the discrepancy between perceptual informationand memory tended to be very small, falling below threshold for explicit
detection of change, despite the fact that both representations were highly
different from the initial image. Even though memory was sensitive to thefact that the image had changed, individual comparisons rarely exceeded
threshold for explicit awareness of change, yielding change blindness.
The original explanations for change blindness were highly attractive intheir parsimony: Changes were missed because coherent visual representations disintegrate upon the withdrawal of attention (Rensink et al., 1997). It
is clear that visual sensory memory is indeed transient (Sperling, 1960), andparticipants would certainly detect changes more accurately if sensory
information was retained and integrated across disruptions such as eye
movements, but we have long known that such integration does not occur(e.g., Irwin et al., 1983). Although sensory memory is transient, higher levelvisual representations are retained robustly in VSTM and in VLTM
(Hollingworth, 2003a, 2004, 2005b; Hollingworth & Henderson, 2002):
Coherent visual representations do not necessarily disintegrate upon thewithdrawal of attention. Recent evidence suggests that despite the ability toaccumulate visual representations in VSTM and VLTM, participants fail todetect changes (1) because they have not fixated and attended the changing
object prior to the change and thus have not had an opportunity to encode
information sufficient to detect a change, (2) because they have not retrievedor adequately compared a memory representation to current perceptual

798

HOLLINGWORTH

information, and (3) because, for many comparisons, evidence of discrepancy falls below threshold for signalling a change in the world.

VISUAL SEARCH AND THE REPRESENTATION OF
NATURAL SCENES
Simultaneously with work on change blindness, Horowitz and Wolfe (1998)
reported a phenomenon they termed memory-free search, which led to atheoretical account of perception, attention, and memory very similar toRensink’s (2000) coherence theory. Horowitz and Wolfe used a search task inwhich they either kept the positions of search elements static or scrambled
the locations of search elements every 111 ms. If search has a memory
component, e.g., one that keeps track of which objects and locations havealready been examined (Klein, 1988), then search should have been lessefficient when scrambling eliminated the utility of memory. Yet, Horowitz
and Wolfe found that search efficiency, as measured by the slope relating
reaction time to set size, was no different for scrambled search and static
search. They concluded that visual search does not rely on memory.
Converging evidence came from a paradigm in which participants repeatedly
searched for a different target over a static search array (Wolfe, Klempen, &Dahlen, 2000). Wolfe et al. found that search efficiency did not improve witharray repetition, suggesting that participants did not form a memory
representation of the array that could influence dynamic visual search.
To account for these findings, Wolfe (1999) proposed that early vision
produces loose assemblages of visual features (which Wolfe & Bennett, 1997,
termed preattentive object files and which appear to be essentially the same
concept as Rensink’s proto-objects). Attention serves to bind features into a
coherent object representation (Wolfe, 1999, p. 77):
When the eyes first open on a new scene, preattentive processes extract
features and assign them, loosely, to preattentive objects. Typically,
attention will be deployed to one object. The act of attention allows thefeatures of the object to be organized and processed in a way that permits
object recognition. The attended object is perceived differently than the
not-yet-attended objects in the scene. Assuming this is to be the case, what
happens when attention is to be deployed to the next object? Does thevisual representation have a memory for the work of attention?
Based on the evidence from search paradigms, Wolfe concluded, as did
Rensink (2000), that visual representations dissolve into their elementaryfeatures after the withdrawal of attention.

VISUAL MEMORY FOR NATURAL SCENES

799

Wolfe’s (1999) proposals primarily concern conscious vision. Whatevereffects attention has on conscious perception are lost as soon as attention iswithdrawn from an object. Put in slightly different terms, Wolfe proposesthat the visual system does not accumulate visible, coherent object
representations as attention is oriented from one object to another. But isthere a plausible model that could produce visible accumulation across shifts
of attention? The only visual memory store that preserves visible representations is visible persistence, and visible persistence decays within 80 100 ms
after the onset of a stimulus, providing no plausible basis for accumulation.
Thus, the Wolfe claim appears entirely consistent with early researchdemonstrating that visible, sensory memory is transient (Averbach & Coriell,
1961; Di Lollo, 1980; Sperling, 1960) and that visible representations do notaccumulate during viewing (e.g., Irwin et al., 1983).5
Leaving issues of phenomenology aside, the more general claim thatmemory plays no role in search (Horowitz & Wolfe, 1998) has generated agreat deal of research demonstrating that memory does indeed play animportant role in visual search paradigms. Memory supports search bothwithin a trial, as the visual system keeps track of which objects have been
examined (Gibson, Li, Skow, Brown, & Cooke, 2000; Klein, 1988; Klein &MacInnes, 1999; Kristjánsson, 2000; Müller & von Mühlenen, 2000;
Peterson, Kramer, Wang, Irwin, & McCarley, 2001; Takeda & Yagi, 2000;
von Mühlenen, Müller, & Müller, 2003), and across trials, as memory forprevious searches guides attention to a target object (Chun & Jiang, 1998,
1999). I will not discuss this work in detail, as it has been reviewed
comprehensively elsewhere (Shore & Klein, 2000; Woodman & Chun, 2006
this issue). I will, however, briefly review studies conducted in my laboratory
designed to investigate the role of memory in real-world search, linking
research on visual search to research on scene perception and change
detection.
To provide a direct test of the role of memory in search over naturalscenes, I developed a search paradigm in which previous exposure to asearch scene was controlled (Hollingworth, 2003b). Participants either saw
a preview of a scene prior to search through that scene, or they did not see
a preview. Figure 3 shows the key events in a trial. Each trial in the preview condition began with a preview display of a real-world scene for 10 s
5
If visible object representations were to accumulate as attention shifts from object to object
in a scene, then the following should occur. When first gazing upon a new environment,
perceptual experience should be quite impoverished, since few objects would yet have been
attended. However, visual experience should get progressively richer as more objects areattended and visible representations are accumulated. The fact that this does not happen *theworld looks equivalently rich whether one has been looking at a scene for a few hundred ms or a
few minutes *provides further, intuitive evidence that visible information does not accumulate
during viewing, whether across shifts of attention or shifts of the eyes.

800

HOLLINGWORTH

Figure 3.

Key stimuli in a preview condition trial of Hollingworth (2003b). In the search scene, thetarget object has been mirror reversed.

(Figure 3, panel A). The object that would later be the search target waspresent in the preview, but participants did not know which of the objects inthe preview would be the target. The target was then displayed in isolation in
the centre of the screen (the target probe, Figure 3, panel B) to indicate which
object should be found in the search scene. The target probe was identical to
the target object that had appeared in the preview scene. Next, a search scene
was displayed (Figure 3, panel C). The target object was always present in
the search scene in the same location as it had appeared in the preview.
However, the target in the search scene was either identical to the target in
the preview scene (and thus identical to the target probe), or it was mirror
reversed. Participants’ task was to find the target in the search scene andrespond to indicate whether it was the same as the target probe or mirror
reversed. Note that memory for the orientation of the target in the preview
could not facilitate search, since the target orientations in the preview andsearch scenes were uncorrelated. Only memory for spatial properties of thescene (such as target position) could facilitate search. The no-preview
condition was identical to the preview condition, except no scene preview
was displayed. Search efficiency was assessed by collecting reaction time
data and by monitoring eye movements.
If search were more efficient with a scene preview, this would provide astraightforward demonstration that memory for the spatial structure of ascene can dynamically influence search. First, mean correct RT was reliably
faster with a preview (1232 ms) than without (1487 ms), a difference of 255
ms. Second, mean elapsed time from the onset of the search scene to thefirst eye fixation on the target object was shorter in the preview condition(374 ms) than in the no preview condition (586 ms). With a 10 s preview,
participants came to fixate the target object only 374 ms after the onset ofthe search scene. On most trials in the preview condition, there was only
one or two fixations intervening between search onset and target fixation.
In these cases, either the very first saccade or the second saccade on the
search scene brought the eyes to the target object. These data actually
underestimate how efficiently the eyes were oriented to the target; for manyof the trials with two intervening fixations, the first saccade during search

VISUAL MEMORY FOR NATURAL SCENES

801

was directed to the target but landed just short, leading to a quick fixation
and a corrective saccade. Memory typically guided attention directly to thetarget.
These results are consistent with work by Chun and Jiang (1998) showing
benefits for search when the spatial configuration of elements is repeated.
Chun and Jiang used a search task with randomly configured simple stimuli(rotated ‘‘T’’s and ‘‘L’’s). Throughout the session, some spatial configurations of search elements were repeated. Specifically, the locations of
distractors and the target were held constant, but the identities of the
distractors and target were randomly varied. After just a handful ofrepetitions, search over repeated configurations was faster than searchover novel configurations, suggesting that participants had learned that a
particular spatial configuration predicted a particular target location andhad used this knowledge to guide attention efficiently to that target location.
This learning appeared to be implicit, because participants could notrecognize repeated configurations at the end of the session.
The scene preview results described above (Hollingworth, 2003b)
complement the Chun and Jiang (1998) findings and extend our knowledgeof how memory influences search. First, repeated exposures to a search
environment are not necessary to produce memory effects; memory canguide search even after a single exposure. Second, repeated search and target
localization are not necessary to produce memory effects; search was
facilitated by memory even though participants had never searched for thetarget object before. Finally, memory representations supporting search neednot be implicit in nature. After a 10 s preview, participants can explicitly
recall the locations of objects in scenes and can estimate target position quite
accurately (Hollingworth, 2005a).
What type of spatial memory supported search in the scene preview
experiment? There appear to be two main possibilities. First, memory for the
spatial configuration of contextual surfaces and objects in the scene could
have guided attention to a location where the target object was likely to befound. For example, if one remembered the spatial position of a kitchencounter from the preview image, followed by a toaster target, one could bias
search towards the likely location of the toaster on the counter (seeHenderson, Weeks, & Hollingworth, 1999; Oliva, Torralba, Castelhano, &Henderson, 2003). Second, participants might remember the specificlocation of the target object and direct attention to that rememberedlocation. Memory for the configuration of contextual surfaces and objects isbroadly consistent with visual transience theories of scene representation(Rensink, 2000; Simons, 1996), because these views hold that abstract spatiallayout is encoded and retained robustly without attention. However,memory for the specific locations of objects requires maintaining binding
between object representations and scene locations (Irwin & Zelinsky, 2002).

802

HOLLINGWORTH

Evidence that search can be facilitated by memory for specific target
location would bolster the claim that visual memory supports the retention
and accumulation of local object information in scenes (Hollingworth,
2003a, 2004, 2005b; Hollingworth & Henderson, 2002) and would extend
those claims to the domain of spatial memory.
To examine the contributions of memory for contextual information andmemory for specific target location, Hollingworth (2003b) added a thirdpreview condition to the scene preview paradigm. In this target-absent
preview condition, the scene preview was identical to the standard preview
condition, except the target object was not present in the preview. If searchwere more efficient in the standard preview condition than in the targetabsent preview condition, this would demonstrate that memory for the
specific position of the target object facilitates search, as the two conditions
differed only in the presence of the target in the preview scene. If search weremore efficient in the target-absent preview condition than in the no preview
condition, this would demonstrate that memory for the layout of contextual
objects and surfaces facilitates search. Indeed, search was more efficient in
the standard preview condition than in the target-absent preview condition,
which in turn was more efficient than search in the no-preview condition,
both for correct RT and for elapsed time to target fixation. In summary,scene memory can exert a strong influence on dynamic visual search, guiding
attention and the eyes to a search target. Scene representations supporting
search include memory for the layout of contextual objects and surfaces andmemory for the positions of individual objects.

CONCLUSION
When looking upon a complex scene, visual sensory representations are
generated across the visual field. If the scene is removed or perceptual
processing otherwise interrupted (e.g., across an eye movement), sensory
persistence decays very quickly (Averbach & Coriell, 1961; Di Lollo, 1980;
Sperling, 1960) and is not integrated from one view of the scene to the next
(Henderson & Hollingworth, 2003c; Irwin et al., 1983). However, directing
attention to an object allows the formation of a coherent visual representation (Treisman, 1988) and the consolidation of that representation into morestable VSTM (Averbach & Coriell, 1961; Irwin, 1992a; Schmidt, Vogel,
Woodman, & Luck, 2002; Sperling, 1960), which maintains visual representations abstracted away from precise sensory information (Irwin, 1991;
Phillips, 1974). After attention is withdrawn from an object, abstracted
visual representations persist (Hollingworth, 2003a; Hollingworth & Henderson, 2002), and they accumulate in memory as attention and the eyes
are oriented from object-to-object within a scene (Hollingworth, 2004),

VISUAL MEMORY FOR NATURAL SCENES

803

supported both by VSTM (for the last two objects attended) and by VLTM
(for objects attended earlier) (Hollingworth, 2004). VLTM then supports theretention of scores of individual object representations over relatively long
periods of time (Hollingworth, 2004, 2005b). Scene representations retain
information not only about the visual form of individual objects but also
about the locations of objects and the configuration of objects and surfaces
within a scene (Hollingworth, 2005a). Memory for the spatial properties of ascene can interact dynamically with perceptual processing during visual
search, efficiently guiding attention and the eyes to target locations (Chun &Jiang, 1998; Hollingworth, 2003b).

REFERENCES
Alvarez, G. A., & Cavanagh, P. (2004). The capacity of visual short-term memory is set both byvisual information load and by number of objects. Psychological Science, 15 , 106 111.
Angelone, B. L., Levin, D. T., & Simons, D. J. (2003). The relationship between change detection
and recognition of centrally attended objects in motion pictures. Perception , 32 , 947 962.
Averbach, E., & Coriell, A. S. (1961). Short-term memory in vision. The Bell System Technical
Journal , 40 , 309 328.
Becker, M. W., & Pashler, H. (2002). Volatile visual representations: Failing to detect changes inrecently processed information. Psychonomic Bulletin and Review, 9 , 744 750.
Bridgeman, B., Hendry, D., & Stark, L. (1975). Failure to detect displacement of the visual
world during saccadic eye movements. Vision Research , 15 , 719 722.
Bridgeman, B., & Mayer, M. (1983). Failure to integrate visual information from successive
fixations. Bulletin of the Psychonomic Society, 21 , 285 286.
Chun, M. M. (2003). Scene perception and memory. Psychology of Learning and Motivation , 42 ,79 108.
Chun, M. M., & Jiang, Y. (1998). Contextual cueing: Implicit learning and memory of visualcontext guides spatial attention. Cognitive Psychology, 36 , 28 71.
Chun, M. M., & Jiang, Y. (1999). Top-down attentional guidance based on implicit learning ofvisual covariation. Psychological Science, 10 , 360 365.
Cohen, J. (2002). The grand grand illusion illusion. Journal of Consciousness Studies, 9 , 141 
157.
Coltheart, M. (1980). The persistences of vision. Philosophical Transactions of the Royal
Society of London. Series B , 290 , 269 294.
Dennett, D. C. (1991). Consciousness explained . Boston: Little, Brown.
Deubel, H., Schneider, W. X., & Bridgeman, B. (1996). Post-saccadic target blanking prevents
saccadic suppression of image displacement. Vision Research, 36, 985 996.
Di Lollo, V. (1980). Temporal integration in visual memory. Journal of Experimental Psychology:
General , 109 , 75 97.
Fernandez-Duque, D., Grossi, G., Thornton, I. M., & Neville, H. J. (2003). Representation ofchange: Separate electrophysiological markers of attention, awareness, and implicit
processing. Journal of Cognitive Neuroscience, 15 , 1 17.
Fernandez-Duque, D., & Thornton, I. M. (2000). Change detection without awareness: Do
explicit reports underestimate the representation of change in the visual system? Visual
Cognition , 7 , 323 344.

804

HOLLINGWORTH

Fernandez-Duque, D., & Thornton, I. M. (2003). Explicit mechanisms do not account for
implicit localization and identification of change: A reply to Mitroff et al. (2002). Journal of
Experimental Psychology: Human Perception and Performance, 29 , 846 858.
Gibson, B. S., Li, L., Skow, E., Brown, K., & Cooke, L. (2000). Searching for one versus two
identical targets: When visual search has memory. Psychological Science, 11 , 324 327.
Grier, J. B. (1971). Nonparametric indexes for sensitivity and bias: Computing formulas.
Psychological Bulletin , 75 , 424 429.
Grimes, J. (1996). On the failure to detect changes in scenes across saccades. In K. Akins (Ed.),
Vancouver studies in cognitive science: Vol. 5. Perception (pp. 89 110). Oxford, UK: Oxford
University Press.
Hayhoe, M. M., Bensinger, D. G., & Ballard, D. H. (1998). Task constraints in visual working
memory. Vision Research , 38 , 125 137.
Henderson, J. M. (1997). Transsaccadic memory and integration during real-world object
perception. Psychological Science, 8 , 51 55.
Henderson, J. M., & Hollingworth, A. (1998). Eye movements during scene viewing: An
overview. In G. Underwood (Ed.), Eye guidance in reading and scene perception (pp. 269 
283). Oxford, UK: Elsevier.
Henderson, J. M., & Hollingworth, A. (1999). The role of fixation position in detecting scenechanges across saccades. Psychological Science, 10 , 438 443.
Henderson, J. M., & Hollingworth, A. (2003a). Eye movements and visual memory: Detectingchanges to saccade targets in scenes. Perception and Psychophysics, 65 , 58 71.
Henderson, J. M., & Hollingworth, A. (2003b). Eye movements, visual memory, and scene
representation. In M. A. Peterson & G. Rhodes (Eds.), Perception of faces, objects, andscenes: Analytic and holistic processes (pp. 356 383). New York: Oxford University Press.
Henderson, J. M., & Hollingworth, A. (2003c). Global transsaccadic change blindness during
scene perception. Psychological Science, 14 , 493 497.
Henderson, J. M., Weeks, P. A., Jr., & Hollingworth, A. (1999). The effects of semantic
consistency on eye movements during complex scene viewing. Journal of Experimental
Psychology: Human Perception and Performance, 25 , 210 228.
Hoffman, J. E., & Subramaniam, B. (1995). The role of visual attention in saccadic eye
movements. Perception and Psychophysics, 57 , 787 795.
Hollingworth, A. (2003a). Failures of retrieval and comparison constrain change detection innatural scenes. Journal of Experimental Psychology: Human Perception and Performance, 29 ,388 403.
Hollingworth, A. (2003b). Visual memory and the online representation of complex scenes. Paper
presented at the Munich Visual Search symposium, Munich, Germany.
Hollingworth, A. (2004). Constructing visual representations of natural scenes: The roles ofshort- and long-term visual memory. Journal of Experimental Psychology: Human Perception
and Performance, 30 , 519 537.
Hollingworth, A. (2005a). Memory for object position in natural scenes. Visual Cognition, 12,1003 1016.
Hollingworth, A. (2005b). The relationship between online visual representation of a scene andlong-term scene memory. Journal of Experimental Psychology: Learning, Memory, andCognition, 31, 396 411.
Hollingworth, A., & Henderson, J. M. (2002). Accurate visual memory for previously attended
objects in natural scenes. Journal of Experimental Psychology: Human Perception and
Performance, 28 , 113 136.
Hollingworth, A., & Henderson, J. M. (2004). Sustained change blindness to incremental scene
rotation: A dissociation between explicit change detection and visual memory. Perception andPsychophysics, 66, 800 807.

VISUAL MEMORY FOR NATURAL SCENES

805

Hollingworth, A., Schrock, G., & Henderson, J. M. (2001a). Change detection in the flicker
paradigm: The role of fixation position within the scene. Memory and Cognition , 29 , 296 
304.
Hollingworth, A., Williams, C. C., & Henderson, J. M. (2001b). To see and remember: Visuallyspecific information is retained in memory from previously attended objects in naturalscenes. Psychonomic Bulletin and Review, 8 , 761 768.
Horowitz, T. S., & Wolfe, J. M. (1998). Visual search has no memory. Nature, 394 , 575 577.
Irwin, D. E. (1991). Information integration across saccadic eye movements. Cognitive
Psychology, 23 , 420 456.
Irwin, D. E. (1992a). Memory for position and identity across eye movements. Journal of
Experimental Psychology: Learning, Memory, and Cognition , 18 , 307 317.
Irwin, D. E. (1992b). Visual memory within and across fixations. In K. Rayner (Ed.), Eyemovements and visual cognition: Scene perception and reading (pp. 146 165). New York:
Springer-Verlag.
Irwin, D. E., & Andrews, R. (1996). Integration and accumulation of information acrosssaccadic eye movements. In T. Inui & J. L. McClelland (Eds.), Attention and performance
XVI: Information integration in perception and communication (pp. 125 155). Cambridge,
MA: MIT Press.
Irwin, D. E., Yantis, S., & Jonides, J. (1983). Evidence against visual integration across saccadic
eye movements. Perception and Psychophysics, 34 , 35 46.
Irwin, D. E., & Yeomans, J. M. (1986). Sensory registration and informational persistence.
Journal of Experimental Psychology: Human Perception and Performance, 12 , 343 360.
Irwin, D. E., & Zelinsky, G. J. (2002). Eye movements and scene perception: Memory for things
observed. Perception and Psychophysics, 64 , 882 895.
Klein, R. (1988). Inhibitory tagging system facilitates visual search. Nature, 334 , 430 431.
Klein, R. M., & MacInnes, W. J. (1999). Inhibition of return is a foraging facilitator in visual
search. Psychological Science, 10 , 346 352.
Kristjánsson, A. (2000). In search of remembrance: Evidence for memory in visual search.
Psychological Science, 11 , 328 332.
Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features andconjunctions. Nature, 390 , 279 281.
Matin, E. (1974). Saccadic suppression: A review and an analysis. Psychological Bulletin , 81 ,899 917.
McConkie, G. W. (1991). Where vision and cognition meet . Paper presented at the Human
Frontier Science Program workshop on Object and Scene Perception, Leuven, Belgium.
McConkie, G. W., & Currie, C. B. (1996). Visual stability across saccades while viewing complex
pictures. Journal of Experimental Psychology: Human Perception and Performance, 22 , 563 
581.
McConkie, G. W., & Rayner, K. (1976). Identifying the span of the effective stimulus in reading:
Literature review and theories of reading. In H. Singer & R. B Ruddell (Eds.), Theoreticalmodels and processes in reading (pp. 137 162). Newark, DE: International Reading
Association.
McConkie, G. W., & Zola, D. (1979). Is visual information integrated across successive fixations
in reading? Perception and Psychophysics, 25 , 221 224.
Mitroff, S. R., Simons, D. J., & Franconeri, S. L. (2002). The Siren Song of implicit change
detection. Journal of Experimental Psychology: Human Perception and Performance, 28 ,798 815.
Mitroff, S. R., & Simons, D. J., & Levin, D. T. (2004). Nothing compares 2 views: Change
blindness results from failures to compare retained information. Perception and Psychophysics, 66, 1268 1281.

806

HOLLINGWORTH

Murdock, B. B. (1962). The serial position effect of free recall. Journal of Experimental
Psychology, 64 , 482 488.
Müller, H. J., & von Mühlenen, A. (2000). Probing distracter inhibition in visual search:
Inhibition of return. Journal of Experimental Psychology: Human Perception and Performance, 26 , 1591 1605.
Nickerson, R. S. (1965). Short-term memory for complex meaningful visual configurations: A
demonstration of capacity. Canadian Journal of Psychology, 19 , 155 160.
Oliva, A., Torralba, A., Castelhano, M. S., & Henderson, J. M. (2003). Top down control ofvisual attention in object detection. IEEE Proceedings of the International Conference on
Image Processing , 1 , 253 256.
O’Regan, J. K. (1992). Solving the ‘‘real’’ mysteries of visual perception: The world as an outside
memory. Canadian Journal of Psychology, 46 , 461 488.
O’Regan, J. K., & Lévy-Schoen, A. (1983). Integrating visual information from successive
fixations: Does trans-saccadic fusion exist? Vision Research , 23 , 765 768.
O’Regan, J. K., & Noë, A. (2001). A sensorimotor account of vision and visual consciousness.
Behavioral and Brain Sciences, 24 , 939 1011.
Pashler, H. (1988). Familiarity and the detection of change in visual displays. Perception andPsychophysics, 44 , 369 378.
Peterson, M. S., Kramer, A. F., Wang, R. F., Irwin, D. E., & McCarley, J. S. (2001). Visual
search has memory. Psychological Science, 12 , 287 292.
Phillips, W. A. (1974). On the distinction between sensory storage and short-term visualmemory. Perception and Psychophysics, 16 , 283 290.
Phillips, W. A., & Christie, D. F. M. (1977). Components of visual memory. Quarterly Journal of
Experimental Psychology, 29 , 117 133.
Potter, M. C., Staub, A., & O’Connor, D. H. (2004). Pictorial and conceptual representation of
glimpsed pictures. Journal of Experimental Psychology: Human Perception and Performance,
30 , 478 489.
Rayner, K., & Pollatsek, A. (1983). Is visual information integrated across saccades? Perception
and Psychophysics, 34 , 39 48.
Rensink, R. A. (2000). The dynamic representation of scenes. Visual Cognition , 7 , 17 42.
Rensink, R. A. (2002). Change detection. Annual Review of Psychology, 53 , 245 277.
Rensink, R. A., O’Regan, J. K., & Clark, J. J. (1997). To see or not to see: The need for attention
to perceive changes in scenes. Psychological Science, 8 , 368 373.
Riggs, L. A. (1965). Visual acuity. In C. H. Graham (Ed.), Vision and visual perception (pp. 321 349). New York: Wiley.
Ryan, J. D., Althoff, R. R., Whitlow, S., & Cohen, N. J. (2000). Amnesia is a deficit in relational
memory. Psychological Science, 8 , 368 373.
Schmidt, B. K., Vogel, E. K., Woodman, G. F., & Luck, S. J. (2002). Voluntary and automatic
attentional control of visual working memory. Perception and Psychophysics, 64 , 754 763.
Shepard, R. N. (1967). Recognition memory for words, sentences, and pictures. Journal of Verbal
Learning and Verbal Behavior, 6 , 156 163.
Shore, D. I., & Klein, R. M. (2000). On the manifestations of memory in visual search. Spatial
Vision , 14 , 59 75.
Simons, D. J. (1996). In sight, out of mind: When object representations fail. Psychological
Science, 7 , 301 305.
Simons, D. J., Chabris, C. F., Schnur, T. T., & Levin, D. T. (2002). Evidence for preserved
representations in change blindness. Consciousness and Cognition , 11 , 78 97.
Simons, D. J., & Levin, D. T. (1997). Change blindness. Trends in Cognitive Sciences, 1 , 261 
267.
Simons, D. J., & Levin, D. T. (1998). Failure to detect changes to people during a real-worldinteraction. Psychonomic Bulletin and Review, 5 , 644 649.

VISUAL MEMORY FOR NATURAL SCENES

807

Sperling, G. (1960). The information available in brief visual presentations. Psychological
Monographs, 74 (11, Whole no. 498).
Standing, L. (1973). Learning 10,000 pictures. Quarterly Journal of Experimental Psychology,
25 , 207 222.
Standing, L., Conezio, J., & Haber, R. N. (1970). Perception and memory for pictures: Singletrial learning of 2500 visual stimuli. Psychonomic Science, 19 , 73 74.
Takeda, Y., & Yagi, A. (2000). Inhibitory tagging in visual search can be found if search stimuli
remain visible. Perception and Psychophysics, 62 , 927 934.
Thornton, I. M., & Fernandez-Duque, D. (2002). Converging evidence for the detection ofchange without awareness. In J. Hyönä, et al. (Eds.), The brain’s eyes: Neurobiological andclinical aspects of oculomotor research (pp. 99 118). Amsterdam: Elsevier Science.
Treisman, A. (1988). Features and objects: The fourteenth Bartlett memorial lecture. Quarterly
Journal of Experimental Psychology, 40A , 201 237.
Von Mühlenen, A., Müller, H. J., & Müller, D. (2003). Sit-and-wait strategies in dynamic visual
search. Psychological Science, 14 , 309 314.
Wheeler, M. E., & Treisman, A. M. (2002). Binding in short-term visual memory. Journal of
Experimental Psychology: General , 131 , 48 64.
Williams, P., & Simons, D. J. (2000). Detecting changes in novel 3D objects: Effects of change
magnitude, spatiotemporal continuity, and stimulus familiarity. Visual Cognition , 7 , 297 
322.
Wolfe, J. M. (1998). Visual memory: What do you know about what you saw? Current Biology,
8 , R303 R304.
Wolfe, J. M. (1999). Inattentional amnesia. In V. Coltheart (Ed.), Fleeting memories (pp. 71 94).
Cambridge, MA: MIT Press.
Wolfe, J. M., & Bennett, S. C. (1997). Preattentive object files: Shapeless bundles of basic
features. Vision Research , 37 , 25 44.
Wolfe, J. M., Klempen, N., & Dahlen, K. (2000). Postattentive vision. Journal of Experimental
Psychology: Human Perception and Performance, 26 , 693 716.
Woodman, G. F., & Chun, M. M. (2006). The role of working memory and long-term memory
in visual search. Visual Cognition , 14 , 808 830.
Woodman, G. F., & Luck, S. J. (2003). Serial deployment of attention during visual search.
Journal of Experimental Psychology: Human Perception and Performance, 29 , 121 138.
Yantis, S., & Jonides, J. (1984). Abrupt visual onsets and selective attention: Evidence fromvisual search. Journal of Experimental Psychology: Human Perception and Performance, 10 ,601 621.
Zelinsky, G., & Loschky, L. (1998). Toward a realistic assessment of visual working memory.
Investigative Ophthalmology and Visual Science, 39 , S224.