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We see the world in scenes, where visual objects occur in rich surroundings, often embedded in a typical context with other related objects. How does the human brain analyse and use these common associations? This article reviews the knowledge that is available, proposes specific mechanisms for the contextual facilitation of object recognition, and highlights important open questions. Although much has already been revealed about the cognitive and cortical mechanisms that subserve recognition of individual objects, surprisingly little is known about the neural underpinnings of contextual analysis and scene perception. Building on previous findings, we now have the means to address the question of how the brain integrates individual elements to construct the visual experience.
Think ofa giraffe, a basketball or a microscope.It is hard
to imagine seeing any of them without a background and
other objects. Our experience with the visual world
dictates our predictions about what other objects to
expect in a scene,and their spatial configuration.For
example, seeing a steering wheel inside a car sets expecta-
tions about where the radio,ashtray and mirrors might
be.These predictable properties of our environment can
facilitate perception, and in particular object recognition
(BOX 1).Recognizing someone’s hand,for instance, signifi-
cantly limits the possible interpretations of the object on
that persons wrist to either a watch or a bracelet; it is
not likely to be a chair or an elephant. This a priori
knowledge allows the visual system to sensitize the corre-
sponding visual representations (of a watch and a
bracelet) so that it is easier to recognize the surrounding
objects when we attend to them.In fact, these context-
driven predictions can allow us to choose not to attend
to this object at all if none of the possible identities
‘suggested’by the context are of immediate interest.
Representing and processing objects in groups that
tend to be found together might explain why recogni-
tion of an object that is highly associated with a certain
context facilitates the recognition of other objects that
share the same context
.Is this clustering reflected
in the cortical analysis of contextual associations? How
does contextual knowledge facilitate recognition of
individual objects in a scene? What cortical areas are
involved and how does information flow in the brain
when contextual representations are activated? The
primary goal of this article is to review the current
knowledge about this field, and to propose what is
further needed to answer these questions.Cognitive
studies from the past 30 years are reviewed with recent
physiological and neuroimaging data,and a theoretical
proposal regarding how contextual knowledge facilitates
object recognition is described.
There are many studies on the broad subject of con-
text,considerably more than can be covered here.I will
concentrate on the visual context of objects; the glue’that
binds objects in coherent scenes.Within the underlying
definition,each context (for example, an airport or a zoo)
is a prototype that has infinite possible exemplars (spe-
cific scenes).In these prototypical contexts, certain
elements are present with certain likelihoods,and the
spatial relations among these elements adhere to typical
configurations.Visual objects are contextually related if
they tend to co-occur in our environment,and a scene
is contextually coherent if it contains items that tend to
appear together in similar configurations.
Understanding how contextual associations and
object recognition are accomplished is essential for any
complete theory of the brain. Studies of the organization
ofcortical representations have focused on groups of
objects with the same basic-level name (such as faces,
chairs or flowers)
,generally ignoring the effects of
context and the typical co-appearance of related objects.
This necessary research has allowed us to progress to
Moshe Bar
We see the world in scenes, where visual objects occur in rich surroundings, often embedded in
a typical context with other related objects. How does the human brain analyse and use these
common associations? This article reviews the knowledge that is available, proposes specific
mechanisms for the contextual facilitation of object recognition, and highlights important open
questions. Although much has already been revealed about the cognitive and cortical
mechanisms that subserve recognition of individual objects, surprisingly little is known about the
neural underpinnings of contextual analysis and scene perception. Building on previous findings,
we now have the means to address the question of how the brain integrates individual elements
to construct the visual experience.
Martinos Center at
Massachusetts General
Hospital, Harvard Medical
School, 149 Thirteenth
Massachusetts 02129, USA.
© 2004
The level of abstraction that
carries the most information,
and at which objects are typically
named most readily.For
example,subjects would
recognize an Australian
Shepherd as a dog (that is,basic-
level) more easily than as an
animal (that is, superordinate-
level) or as an Australian
Shepherd (that is,subordinate-
An experience-based facilitation
in perceiving a physical stimulus.
In a typical object priming
experiment, subjects are
presented with stimuli (the
primes) and their performance
in object naming is recorded.
Subsequently,subjects are
presented with either the same
stimuli or stimuli that have some
defined relationship to the
primes.Any stimulus-specific
difference in performance is
taken as a measure of priming.
618 | AUGUST 2004 | VOLUME 5
physical appearance in the occipital visual cortex
by basic-level categories in the anterior temporal
,by contextual relations in the parahippo-
campal cortex (PHC)
,and by semantic relations in the
prefrontal cortex (PFC)
.In addition,the grouping of
objects might be represented by stored relationships.In
the framework promoted here, different brain regions
represent different possible grouping relations,and there
is one centralized,detailed object representation compo-
nent that serves all of these relations on demand’.
How are contextual representations of associated
objects stored so that cortical processing can take
advantage of predictable aspects of our environment? A
recurring proposal is that prototypical contexts might
be represented in structures that integrate information
about the identity of the objects that are most likely to
appear in a specific scene with information about their
relationships. These contextual structures are referred
to here as ‘context frames’
but have also been described
as schemata
and frames
.In general,
these structures can be viewed as sets of expectations
that can facilitate perception.
We know very little about how the brain arranges and
retains such contextually associated information,
although cognitive studies have provided important
insights. For example, Biederman defined five types of
relations that characterize a scene
:support (most objects
are physically supported rather than float),interposition
(for example,occlusion),probability (the likelihood that
certain objects will be present in a scene),position (the
typical positions of some objects in some scenes) and
size (the familiar relative size of objects) (see also
Objects that violate these relations in a scene are gener-
ally processed more slowly and with more errors
These findings not only indicate what information is
represented about scenes,but also that the semantic
context of a scene might be extracted early enough to
affect our perception of individual objects in it, possibly
in a ‘top–down’manner.
Context frames are assumed to be derived from
exposure to real-world scenes.The extent to which the
information in these frames is abstract,perceptually
concrete or exists on multiple levels of abstraction
is unclear.Some evidence for abstract representation
of scenes comes from the phenomenon of boundary
— a type of memory distortion in which
observers report having seen not only information that
was physically present in a picture, but also information
that they have extrapolated outside the scene’s bound-
aries.Similarly, in visual false memory experiments,
participants report that they remember’having seen, in a
previously presented picture,objects that are contextually
related to that scene but that were not in the picture
Such memory distortions might be byproducts of an
efficient mechanism for extracting and encoding the gist
of a scene.
Context frames can be viewed as prototypical repre-
sentations ofunique contexts (for example, a library),
which guide the formation ofspecific instantiations in
episodic scenes (for example,our library). It might be
possible to generalize knowledge stored in context frames
considering objects within scenes and context.After all,
our visual environment consists of contextually bound
scenes,and research in this direction is ecologically
most valid.
Representation of visual context
Objects can be related in various dimensions. For
example,a hairdryer and a hairbrush are contextually
related; two different hairdryers are different exemplars of
the same
BASIC-LEVEL CONCEPT;and a hairdryer and a drill
look physically similar and so are perceptually related.
Our brains can distinguish even subtle differences along
these dimensions so that,for instance,physically similar
objects are still labelled as different objects (for example,
the cell phone and the calculator in
FIG. 1), and two
visually different objects can still be given the same basic-
level name (for example,the two phones in
relationships are usually explored using
improvement in performance after exposure to a stimulus
can reveal the characteristics of underlying representa-
tions and their relations.What representation framework
would make these intricate distinctions possible? Each
object does not need to be represented in rich detail in
multiple cortical regions — instead,different regions
could represent features of objects that are relevant for
the dimensions along which objects are grouped in
these regions.For example,objects might be grouped by
Box 1 | The powerful effects of context
The idea promoted throughout this review is that context-based predictions make object
recognition more efficient. Furthermore,in cases where recognition cannot be
accomplished quickly based only on the physical attributes of the target, contextual
information can provide more relevant input for the recognition of that object than can
its intrinsic properties (see
REFS 131,151 for computational demonstrations). The
hairdryer in the left panel and the drill in the right panel are identical objects: contextual
information uniquely resolves ambiguity in each case.
The benefits of biasing recognition processes to cohere with an activated context,
however,are accompanied by occasional inaccuracies. For example,you can probably
IVAUTRE REIVEWS IVEOURCSEICNEas Nature Reviews Neuroscience reasonably
quickly,without wasting time on details, and therefore without noticing at least seven
syntactic errors. (A counter-example where context is not strong enough to make the
observer ignore details is: NaTuRe ReViEwS NeUrOsCiEnCe, where attention is drawn to
the irregularity,and the observer therefore misses the advantage from context.) Similarly,
objects that follow the presentation of a contextual scene are misrecognized if they look
like an object that belongs in the context
.False memory
,boundary extension
change blindness
are additional demonstrations of how contextually driven
expectations can ‘taint’subjective perception. Such effects might be considered as
manifestations of the efficiency and flexibility of our perceptual and semantic
mechanisms,which possibly,but rarely, fail.Similar contextual biases have been
demonstrated in studies with linguistic stimuli (for example,see
REFS 154–156).
© 2004
Identifying the exact structure of these representations
will subsequently help us to understand how context
frames are activated to facilitate our perception of the
visual world.
Context and object recognition
We seem to be able to take advantage of visual regularities
in our environment,as contextual knowledge facilitates
perception and cognition in many domains. Context-
based facilitation ofvisual perception has been reviewed
,and only the most relevant studies are
highlighted here.
A typical scene structure that follows physical and
contextual semantic rules facilitates recognition
,at least
compared with situations in which these rules are
violated.When subjects are presented with a scene of a
familiar context, such as a kitchen, objects that are con-
sistent with that context (such as a loaf of bread) are
recognized more easily than objects that would not be
expected in that context (for example, a drum)
findings support the idea that context facilitates object
recognition by activating context frames.
Context also facilitates the recognition of related
objects even if these objects are ambiguous when seen
in isolation
(FIG. 2); an ambiguous object becomes
recognizable if another object that shares the same
context is placed in an appropriate spatial relation to it.
In recognition,the goal is to determine the identity
of viewed objects, despite possible variations in appear-
.Expectations derived from the context frame
about the identity of other objects, as well as their
position,orientation,size and so on,could therefore
greatly facilitate the recognition of related objects.
If contextual information facilitates object recogni-
tion,one might expect that it would be easier to recognize
a fixated object in a contextually coherent scene than in
isolation.Two studies that tested this prediction found
that an individually presented object was actually recog-
nized more easily than the same object when it was
embedded in a coherent scene
.However, at least two
possible confounds make it hard to isolate the contribu-
tion of context per se.First,segmenting an individual
object from its background is a consuming process,and is
likely to make recognition more difficult,even in contex-
tually coherent scenes. Second, attentional distraction
from the scene might have affected the execution of
response in those studies
,rather than the perception
ofthe target.In addition,the results of one study that
addressed this issue indirectly
indicated that a contextu-
ally consistent background facilitates object recognition
compared with the effect of a meaningless background
that was equated for visual appearance. This adds
support to contextual facilitation of recognition.
At what level of processing might context facilitate the
recognition of individual objects? One possibility is that
context is extracted from the scene so rapidly that it can
facilitate the perceptual analysis of individual objects and
therefore directly promote their recognition
slight variation of this idea is that when a context frame is
activated it might sensitize the representation of all the
objects associated with it, so that when the input image
to instances of scenes where relations are novel but
,although information about relational plausi-
bility (a person holding a dog is plausible, whereas a dog
holding a person is not) might be represented indepen-
dently as general world knowledge outside specific
context frames.A central prediction that stems from this
proposed co-representation ofcontextually related objects
is that processing of‘typical’items and relations will be
faster than processing of novel items and relations, and
this has been supported by many studies.Context frames
provide sets of expectations that can guide perception
and action, and they can influence our exploration
ofa scene using eye movements and attention.Context
frames can also modulate memory encoding and retrieval
(memory can be improved when the encoding context
is reinstated at retrieval
).As will be explained later,
context frames can be activated by coarse,global scene
information. It is proposed (and has been shown
)that it is possible to construct a coarse
representation ofa scene that bypasses object identities,
where the scene is represented as a single entity. This
rudimentary information can provide a shortcut for
automatic activation of high-level semantic information
by relatively low-level perceptual information.
In summary,typical arrangements in our environ-
ment are represented in context frames,which provide
expectations that facilitate the perception of other scenes
that can be represented by the same context. Objects and
relations that are sufficiently characteristic of the context
are extracted and recognized readily, on the basis of
global information and expectation-based shortcuts
provided by defaults in the frame.The recognition of
atypical objects and relations requires further scrutiny
mediated by fine detail and elaborated analysis of local
features.During recognition,an object can activate a
context frame (or a set of frames), and a frame can acti-
vate an object (or a set of objects)
.Our understanding
of these representations is limited, and the concept
of context frames is helpful in guiding our search.
Object representations
Same but different
Figure 1 | Some of the intricate object relations that are accommodated in the brain.
Objects that look very similar can be represented and recognized as different objects, whereas
objects that look very different can be recognized as the same basic-level objects.
© 2004
620 | AUGUST 2004 | VOLUME 5
relations that are present in coherent typical scenes
whereas the criticism is legitimate with regard to position,
it does not account for findings about the contribution
of context when compared with violations of other
relations (such as support and size).
Second,in recent experiments where the possibility of
response bias was specifically controlled
contributed significantly to object recognition, although
response bias probably contributed to part of the
improvement that was previously attributed exclusively
to context
.In addition,object recognition facilitates the
recognition of a scene’s background
,which constitutes
its context, indicating a bidirectional exchange between
the two processes. Therefore, scene recognition does
not seem to proceed in parallel, separated channels,
but instead is a more interactive process that integrates
contextual information to facilitate object recognition,
and uses object identities to promote the understanding
of a scene.
Another reason why opinions about the role of
contextual information in object recognition have been
mixed might be that object recognition is very efficient.
As a result,a clear,prototypical, isolated object (usually
on a computer screen in the laboratory) is recognized in
less than 150 ms
(REFS 45,46).In real life, however,clutter,
occlusion,shading, viewing angles and other factors
make recognition harder, and in these realistic situations
recognition can benefit from other sources.Context,
as well as familiarity, non-contextual expectations,
top–down facilitation
and movement
,might all
facilitate object recognition.In other words,object (and
scene) recognition vary in difficulty, and added sources
might facilitate recognition at increasing levels of diffi-
culty.When studying the possible effects ofthese auxiliary
factors,the difficulty of the task must be manipulated.
Studying these influences when recognition is atypically
easy might give the impression that other sources do not
contribute to the process.
Our discussion of contextual facilitation in object
recognition emphasizes recognition of a fixated, target
object.However,given the proposed structure of context
frames and the sets of expectations they elicit, it would be
predicted that the recognition of expected non-target
objects would also be facilitated,perhaps even more than
the recognition of the target object. Indeed,contextual
associations promote the deployment of attention
towards associated objects
,thereby facilitating their
recognition compared with non-associated objects.Sim-
ilarly,contextual understanding helps us to determine,
consciously or not, where to look next
What is the benefit of the representation of common
relations, and how do they influence the recognition of
individual objects? From a computational standpoint, it
is clear that representing such regularities allows an
efficient generalization in new situations
as well as
analysis shortcuts produced by the expectations that the
context frames provide,possibly mediated by long-range
cortical connections
.More generally,contextual facili-
tation might be mediated by lowering the response
thresholds (increasing the sensitivity) of the cortical
representations of anticipated objects.
has been sufficiently analysed to be compared with mem-
ory, this contextual activation facilitates convergence into
the most likely interpretation
.The third possibility
is that object recognition and contextual scene analysis
are functionally separate and interact only at a later,
semantic stage
.The first two alternatives have been
criticized on the grounds of a possible response bias
inherent to the experimental design of early studies,
where cuing observers to a specific object identity and/
or position in the scene could be selectively helpful for
guessing when the scene is contextually consistent with
the object
.In other words,if subjects identified the
scenes context (for example,a street) they could infer that
a mailbox was present at the cued location even without
perceiving the mailbox. If the cued object was contextu-
ally incongruent (for example, a blender at the same
position in the street), however, subjects could not
respond correctly without first recognizing the object.
In a study addressing the bias effect,Henderson and
Hollingworth reported results that support their view of
functional separation
(but see REFS 37,44).Nevertheless,
there is continuing evidence that context exerts its effect
relatively early during object recognition. First, the
response bias criticism focused on the biasing position
of probable target objects.However, there are at least five
Figure 2 | Resolution of ambiguous objects by context.
a | Two examples of the incomplete but recognizable figures
from which the stimuli were derived (created by the artist Haro;
reproduced, with permission, from
REF.156 (1960) Elsevier
Science). These figures are missing many features, but are
nevertheless recognizable. b| The glasses on the left are
ambiguous, but when a related object (hat) is placed next to
them, in an appropriate spatial relation, they become
recognizable. c | The object on the left is ambiguous in
isolation. Placing a related object (head) next to it
disambiguates its identity, as a purse, if placed in a typical,
relative spatial position (middle), but does not affect its
recognition when placed inappropriately (right).
© 2004
Given that contextual information is extracted
rapidly, presumably on the basis of coarse representa-
tions,does contextual processing require awareness?
Context can be processed implicitly
,and it can be
learned incidentally (without explicit intentions)
Furthermore,subjects can categorize visual objects in
contextual scenes in the ‘near absenceof attention
Finally, contextual information can be automatically
activated by a scene and can subsequently interfere
with task performance
.Implicit access to semantic
information about context does not need to be direct.
Instead,contextual processing might use a shortcut
whereby high-level semantic information is activated
by coarse input, even before this input has been identi-
fied (see the proposed model below and also
REF.65 for
a similar treatment of subliminal semantic activation
in words).
Event-related potential (ERP) studies have provided
some insight into the neural dynamics that underlie the
rapid extraction of context.For example,ERPs can dis-
tinguish between visual categories for individual objects
in as little as 75–80 ms
(REF.45).In other studies
signals distinguished between new and old contexts
around 100–200 ms after stimulus onset, depending
on the task.Finally,preliminary data from a study that
combined functional magnetic resonance imaging
(fMRI) and
that activity that is directly related to contextual process-
ing develops first in the PHC,which has previously been
implicated in contextual processing
, and in the
fusiform gyrus around 130 ms after stimulus onset.
A second wave of differential activation develops there
around 230 ms after stimulus onset. The functional
significance of these two waves has yet to be determined,
but we propose that the first is a manifestation of a quick
and coarse activation of the scene’s representation and
the second reflects a richer representation, incorporating
the full spatial bandwidth.
Cortical processing of context
In contrast to the number of behavioural studies that
have addressed contextual processing, little has been
revealed about the underlying neural mechanisms.
Most of the related research has focused on associative
processing,which can be considered as the building
blocks of context.
Structures in the medial temporal lobe,including
the hippocampus,PHC, and perirhinal and entorhinal
cortices, are thought to be important in associative
.Unfortunately, there is not enough
evidence to make a clear functional distinction between
the subdivisions of the medial temporal lobe. For
example,the hippocampus receives input from many
sources,and there is some evidence that it emphasizes
associative rather than single-item representations
under some conditions
but not others
.More gener-
ally, there are active debates about which sub-region
within the medial temporal cortex mediates associative
versus non-associative representations, episodic versus
semantic memory, spatial versus contextual analysis,
and familiarity versus recognition judgements.
Rapid extraction of context
For contextual information to assist the recognition
process,it has to be extracted rapidly and subsequently
to generate guiding expectations.How quickly context is
extracted from a scene or an object has been the subject
of extensive research.
We can recognize visual scenes in a glance’
.A study
in which pictures of objects were briefly presented
vided evidence that semantic meaning about context is
extracted from the input at an early stage,possibly even
before perceptual processing is complete (see also
Although some reports indicate an absence of semantic
priming in subliminal presentations (for example,
REF.54), the inconsistency might be due to differences in
experimental conditions and varying degrees of subjects’
non-awareness. Subjects can understand a visual scene
with exposure durations of around 100 ms
(REFS 14,55,56),
and might be able to extract semantic information about
context from presentations as brief as 80 ms
Another study indicates that contextual information
is extracted before observers can saccade towards the
portions of the picture that were rated as contributing
most to the context of the scene, and possibly even
before the recognition of individual objects
more,observers process the most informative portions
of an image earliest
How is contextual meaning extracted so rapidly? I
propose that this swift extraction is mediated by global
cues that are conveyed by low spatial frequencies in the
,and that details conveyed by the high
spatial frequencies are analysed later
(BOX 2).The global
shape information that is conveyed by the low spatial
frequencies can activate ‘scene schema
or context
frames, although frames can also be activated by indi-
vidual objects
(MEG).A non-invasive
technology for functional brain
mapping, which provides
superior millisecond temporal
resolution.It measures magnetic
fields generated by electric
currents from active neurons in
the brain. By localizing the
sources of these currents, MEG
is used to reveal cortical
Box 2 | Spatial frequencies and the information they convey
Different spatial frequencies convey different information about the appearance of a
stimulus. High spatial frequencies represent abrupt spatial changes in the image (such as
edges), and generally correspond to configural information and fine detail (left panel).
Low spatial frequencies,on the other hand, represent global information about the shape
(such as general orientation and proportions) (right panel). The centre panel shows the
original image containing the entire spectrum.The initial perception of global scene
information, carried by low spatial frequencies,might mediate the rapid extraction of gist
information from scenes.
© 2004
622 | AUGUST 2004 | VOLUME 5
responses to conditions in which a target object and its
background were congruent and incongruent.On the
basis of the spatial distribution of the response across
the scalp, they suggested that schemata (context
frames) might be stored and activated in the anterior
temporal lobes. The spatial resolution provided by ERP
does not allow further localization of the source of this
signal. However, intracranial recordings in humans
have shown that activity in regions of the medial
temporal lobe, including the PHC,is modulated by
contextual information in words.These findings are
consistent with our proposal that the PHC stores infor-
mation about contextual associations
,which will be
elaborated below.
It is not clear whether the same semantic system
subserves the representation and processing of contextual
information conveyed by words and pictures.The dual-
code’ view
posits multiple semantic systems,and the
posits a unitary system.Experi-
mentally distinguishing between these alternatives has
proven difficult. Contextual information for words and
pictures affects the ERP measurement similarly, but
the picture-related N400 seems to be more frontal,and
the word-related N400 to be more occipital
studies,and a related study using positron emission
tomography (PET)
,indicate that the contextual
systems that mediate word and picture processing might
be similar but not completely overlapping. It is possible
that the semantic representations of pictures and words
use a shared system,but that their processing uses differ-
ent circuits
.That the semantic information conveyed
by words and pictures is processed differently, beyond
differences in perceptual analysis, is supported by other
consistent differences. For example, words are read
quicker than the corresponding pictures can be named,
but pictures of objects are categorized faster than the
corresponding object names
comparing the cortical processing of semantic context
conveyed by written and spoken words, responses to
both seem to be initiated by modality-specific circuitry,
but then elaborated primarily in amodal regions
These findings indicate only a partial overlap
between the mechanisms that mediate semantic analysis
conveyed by different formats,and they demonstrate
a role for both the medial temporal lobe and the PFC
in contextual analysis. The involvement of the PFC in
contextual processing has been demonstrated in the
past,for example, in mediating face–name associations,
using fMRI
.Prefrontal contextual processing has
further been reported in studies of the N400 effect
that used intracranial depth recordings
,and in studies
that combined fMRI with MEG
and with ERP
This frontal activity occurred along with medial temporal
,possibly reflecting the interaction between
these two regions
How is the clustering of objects into typical contexts
reflected in the cortical processing of contextual associ-
ations? To address this question,we compared the fMRI
signal elicited during the recognition of visual objects
that are highly associated with a certain context (for
example, a bowling pin) with that elicited by objects
Human neuroimaging studies have started to address
the cortical basis of context and scene processing. These
studies revealed a region in the PHC that responds
preferentially to topographical information and spatial
— the parahippocampal place area
(PPA). The invariance of the representations and
processes in the PPA to viewing position are currently
being characterized
in an analogous manner to what
has been characterized behaviourally
.This region
might have an important role in large-scale integration
and there is an increasing assumption that it isa module
for analysing scenes
(but see REF. 85). However,
although the PHC seems to be part of a cortical contex-
tual network
,treating it as a special module for scene
representation and analysis might be overly simplistic.
We see the world in scenes. This implies that the
cortical mechanism that mediates scene perception
integrates the output of many processes,which analyse
different aspects of the input into one smooth and coher-
ent scene. Consider individual objects. The various
components of representing and recognizing individual
objects (shape,identity and so on) have been attributed to
a network
that includes the lateral occipital cortex
the fusiform gyrus
,each of which is larger than the PPA.
Could this large network merely mediate pre-processing
before the information is synthesized into a comprehen-
sive visual scene in a relatively small region in the PHC?
This is plausible,but because most attention has been
allocated so far to the cortical mechanisms of object
recognition, and considerably less to scene and visual
contextual analysis,our view of the cortical mechanisms
allocated for each faculty might be distorted. In any event,
it would seem that the suggestion that ‘the PPA is for
scenes’ is only part of the story. The framework for inte-
grating cues into scenes might be more complex, possibly
including regions that have previously been implicated in
processing individual objects,and where the PPA might
be responsible only for some aspects of scene processing.
An even larger-scale network is predicted to be
involved in the representation and processing of contex-
tual scenes.Consider that scenes are not always bound
by semantic context.Scenes can be: coherent in their
visual properties exclusively, without adhering to
semantic context and to our knowledge of the physical
world (a zebra reading a book on a cloud); physically
but not contextually coherent (a zebra reading a book
on the street); or coherent also in semantic context
(a zebra grazing on tall grass in the wilderness).
Evaluating these dimensions requires more information
and incorporates an increasingly complicated set of
constraints and experience-based rules.Therefore,to
analyse real-world contextual scenes,the brain would
rely not only on the circuitry that subserves visual scene
perception,but also on pre-existing representations of
common associations and typical relations.
With regard to contextual processing per se,the field
of ERP measurements has been particularly active.The
main catalyst has been the discovery and characteriza-
tion of
.Using the N400 phenomenon,
Ganis and Kutas
studied the dynamics of contextual
effects in scene perception, mainly by comparing the
Originally described as a
negative deflection in the event-
related potential waveform
occurring approximately 400 ms
following the onset of
contextually incongruent words
in a sentence.It has consistently
been linked to semantic
processing.Although it is
probably one of the best neural
signatures of contextual
processing,its exact functional
significance has yet to be
© 2004
houses and other environmental landmarks
second focus of activation was found in the retro-
splenial cortex (RSC),which has also been implicated in
the analysis of spatial information
.In addition, the
processes in the PHC site seemed to be sensitive to
visual appearance,whereas the RSC was more insensi-
tive to specific stimulus appearance. Consequently,we
proposed that both the PHC and the RSC represent
familiar associations, but with a different level of
abstraction. Finally, a third focus, revealed only in our
follow-up event-related fMRI and MEG experiments
was found in the superior orbital sulcus (SOS). We
propose that this region integrates information from
several sources to create a continuously updated repre-
sentation of the current context,and that it uses this
knowledge for top–down facilitation of scene and
object recognition.
The association ofthe PHC and RSC with the percep-
tion of places leads to two possible interpretations of
these results.First,perceiving the contextual objects (for
example, a roulette wheel) might have indirectly activated
the corresponding places (a casino) and,consequently,
elicited an fMRI signal in regions that have been associ-
ated with the perception of places.Alternatively, the PHC
and RSC might mediate the representation and process-
ing offamiliar contextual associations in general,rather
than places per se.In many cases,sets of associations
correspond to landmarks, which generally associate
objects with places
,but the PHC and RSC processes
might also involve non-spatial sets of associations. To
distinguish between these alternatives, we compared the
fMRI activation elicited by spatial, place-specific contexts
(such as ‘street’) with the signal elicited by non-spatial
contexts (such as ‘romance’).
Both spatial and non-spatial contexts elicited sig-
nificant differential activation in the PHC and the RSC,
supporting our hypothesis that the PHC and RSC sites
mediate the general analysis of contextual associations,
rather than of place-related associations exclusively.
Notably, the spatial and non-spatial contexts activated
slightly different,non-overlapping subregions of the
PHC: the spatial contexts elicited a stronger signal in a
relatively posterior part of the PHC focus, possibly
encompassing the PPA, whereas the signal for the
non-spatial contexts peaked more anteriorly. Our
generalization of the role of the PHC to non-spatial as
well as spatial associations is supported by recent
(but see REF.117), and by reports that only
8% of the input to the PHC consists of visuospatial
.Its multimodal inputs might further
indicate that the PHC binds together more than just
visual components.
Interestingly,the PHC
and the RSC
have also
been associated with aspects of episodic memory.
The proposal that emerged from our findings is that
these two regions process familiar associations between
individual constituents,which provide a basis both for
episodic memories and for navigation. Consequently,
the proposal that these regions mediate contextual
associations provides a framework that bridges these
seemingly unrelated functions.
that are not associated with any unique context (for
example,a camera)
The first and largest focus of differential activity was
in the posterior PHC.This site encompasses the PPA,
which has been reported to respond selectively to
Superior orbital
Continuously updates
knowledge of current
context (?)
Contextual associations
that are sensitive to visual
appearance and that are
organized in a hierarchy
of spatial specificity
Cortical network for analysing contextual associations
Strong context Weak context
Figure 3 | Cortical areas involved in processing context. a | A functional magnetic resonance
imaging (fMRI) statistical activation map representing the difference between perceiving objects
that are strongly associated with a specific context and perceiving objects that are not associated
with a unique context. This is a medial view of the left hemisphere, shown using a precise
computer reconstruction where the sulci have been exposed by ‘inflation’. The parahippocampal
cortex (PHC) is circled in blue; the retrosplenial cortex (RSC) is circled in red; the superior orbital
sulcus (SOS) is circled in yellow. Note that in all experimental conditions, subjects viewed similar
looking colour photographs of meaningful, everyday common objects that were equally
recognizable. Consequently, activation due to low-level processes was presumably subtracted out,
and the differential activation map shown here represents only processes that are related to the
level of contextual association. b | The cortical network for contextual associations among visual
objects, suggested on the basis of existing evidence. Other types of context might involve
additional regions (for example, hippocampus for navigation
and Broca’s area for language-
related context). Modified, with permission, from REF.12 © (2003) Elsevier Science.
© 2004
624 | AUGUST 2004 | VOLUME 5
(for example,association of different exemplars of the
same object,conjunction of properties such as smell,
taste and sound).This flexible representation system is
not limited to visual objects and can be generalized to
other modalities.
Support for this proposal comes from neurophysio-
logical studies of memory and perception in the
temporal lobe in monkeys.The response to an object’s
perceptual features occurs in area TE (positioned
laterally to the medial temporal lobe) before it occurs in
the medial temporal lobe,but information about asso-
ciations between such objects elicits an earlier response
in the medial temporal lobe than in area TE
Moreover, lesioning regions in the medial temporal
lobe eliminates the representation of paired visual
associations in the intact region TE
.These findings
support the proposal that knowledge about associations
is stored in the medial temporal lobe, but perceptual
representations are stored in the visual inferior temporal
cortex.Such paired association responses appear in the
perirhinal cortex rapidly, and so are believed to be
mediated by a bottom–up mechanism that connects
two TE neurons representing two individual items to a
single perirhinal neuron representing the association
between them
.Additional support for indirect asso-
ciative activation comes from recent fMRI studies in
.For example, a face-sensitive area in the
fusiform gyrus was shown also to be active for images
of a person with a blurred face,presumably owing to
contextual expectations
The multiplexer can be considered as a type of dis-
tributed representation
,in which the components of a
scene do not need to be spatially adjacent in the cortex,
but rather are activated in their centralized representa-
tion system as dictated by the stored set of associations
in the PHC.The associations might be activated using
BAYESIAN inference methods
,and are reinforced
through mechanisms such as
and long-term potentiation (LTP)
The PHC receives polysensory input through the
RSC,and the cingulate gyrus, visuospatial informa-
tion from the posterior parietal cortex in the dorsal
visual stream,auditory input from the superior tem-
poral gyrus, somatosensory information through the
insula,and visual shape input through areas TE/TEO
and the perirhinal cortex
.Therefore,it seems that
the PHC receives the input that is required for mediat-
ing global contextual associations.Furthermore, the
PHC shows an ERP N400 effect for semantically
incongruent stimuli,which might indicate that it sig-
nals the violation of a familiar set of associations and
alerts the observer to an aspect of the environment
that requires attention.
In summary, contextual processing involves regions
in the medial temporal lobe,the PFC and the RSC. It is
necessary to understand the functional division of
labour between these sites.Furthermore, it is impor-
tant to differentiate which aspects of the observed
dynamics can be attributed solely to contextual
processing,and which are a manifestation of direct
contextual facilitation of object recognition.
This proposal might also shed light on disputes
concerning the function of other medial temporal
regions.A related account is proposed every few years
about various medial temporal structures, predominantly
the hippocampus
.The main rationale for having
attributed a role in navigation to hippocampal cells
as well as to parahippocampal regions in humans
the use ofplace-related paradigms.But these tasks can
generally be viewed, instead,as mediated by contextual
associations.We do not argue that these regions are not
involved in spatial analysis,but the terminology might
have to be modified to accommodate the non-spatial
responses of these medial temporal regions.
We have consistently found activity related to visual
context in the PHC
.In this study, we did not obtain
consistent context-related activity in the hippocampus
and the perirhinal cortex.This might be because the
perirhinal cortex represents relatively simple,paired
,which serve as building blocks for the
more global contextual associations that are represented
in the PHC.
It is unclear what exactly is being represented in the
PHC regarding associations.I propose that the PHC
serves as a switchboard-like ‘multiplexer’of associations
between items that are represented in detail elsewhere,
allowing flexible use of a representation system.
This proposal is reminiscent of Kosslyns definition of
associative memory and tokens
.In the framework
proposed here, each object’s representation ‘stands
alone’in the inferior temporal cortex (ITC) and can
be connected to one of its possible association sets
depending on guidance signals from the PHC.
Visual objects are represented in detail in the ITC,
and there is no reason to believe that detailed represen-
tations are replicated elsewhere. Instead, a multiplexing
system that maps a particular situation to its corre-
sponding set of connective associations would allow
the brain to use one multi-purpose representation
system for visual objects. Furthermore,if associations
between objects were implemented by direct connec-
tions that are co-activated automatically whenever
one of the objects is presented,then seeing a television
set would immediately make us think about a living
room, an appliance store, news,talk shows and so on,
regardless of the specific context. In a typical situation,
however, we need to activate only the relevant subset of
existing associations, and therefore it does not seem
efficient to connect all of the associations of an object
to its representation. In this framework, the PHC
represents information about recurring regularities in
our environment so that,for example,a television set
in the living room would co-activate sofa and coffee
table, but a television set in an electronic store
would activate other appliances,a cashier and so on
(FIG. 4).Associations are not always symmetrically
bi-directional; scissors might activate the representation
of paper, but paper might not activate the representa-
tion of scissors. This reinforces the need for indirect
associative connections rather than simple ‘lines’.
Most sets of associations can be considered as context
frames, but not all sets are about context per se
Use a priori probability
distributions derived from
experience to infer optimal
expectations. They are based on
Bayes’ theorem, which can be
seen as a rule for taking into
account history information to
produce a number representing
the probability that a certain
hypothesis is true.
Builds on Hebbs learning rule
that the connections between
two neurons will strengthen if
the neurons fire simultaneously.
The original Hebbian rule has
serious limitations, but it is used
as the basis for more powerful
learning rules. From a
neurophysiological perspective,
Hebbian learning can be
described as a mechanism that
increases synaptic efficacy as a
function of synchrony between
pre- and postsynaptic activity.
© 2004
sufficient for limiting its possible interpretations.The
intersection of these two sources of information would
result in a unique identification.
The model is illustrated in
FIG. 5.A blurred, low-
spatial-frequency representation is projected early and
rapidly from the visual cortex to the PFC and PHC.In the
PHC, this image activates an experience-based guess
about the context frame that needs to be activated.This
contextual information is projected to the ITC,where a
set ofassociations that corresponds to the relevant context
is activated
(FIG.4).In parallel, the same blurred image,
but with the target object selected by foveal vision
and attention, activates information in the PFC that
subsequently sensitizes the most likely candidate interpre-
tations ofthat individual object
.In the ITC, the intersec-
tion between the representations of the objects associated
with the specific context and the candidate interpretations
ofthe target object results in the reliable selection of a
single identity. This representation (for example,a car) is
then refined and further instantiated (for example,as an
old convertible Mustang), with specific detail gradually
arriving in higher spatial-frequency information.
A model for contextual facilitation
A context frame represents prototypical information
about a unique context, and contains information about
the identities and typical spatial arrangements of objects
that tend to co-appear within that context. This infor-
mation can be considered as a set of expectations about
the environment, which, once activated, are tested
against incoming information.Unfilled ‘slots’in these
are filled by default values that are based on
stereotypical expectations. The rapid activation of these
frames, as discussed above,can be triggered by either
global scene information (low spatial frequencies;
BOX 2)
or by key objects in the scene
.In this section, I will
describe a specific model for how contextual activation
facilitates object recognition.
At the heart of this model is the observation that a
coarse, low-spatial-frequency representation of an input
image is usually sufficient for rapid object recognition.
Specifically, the low-spatial-frequency image of a scene
is typically sufficient for deriving a reliable guess about
the context frame that needs to be activated, and a
low-spatial-frequency image of a single target object is
Figure 4 | The proposed model of how information in the parahippocampal cortex might activate visual representations
in the inferior temporal cortex in a flexible manner. A particular object (for example, a hairdryer) can be associated with several
context frames (for example, a hair salon, a bathroom, an appliance store), as well as with abstract concepts (heat, style and wind).
The specific context (for example, hair salon), which is established by global scene information and/or by contextual cues that are
provided by the recognition of other objects in the scene (for example, a barber pole), dictates which of the experience-based
association sets should be activated to facilitate recognition in the particular situation. Coloured lines link different association sets.
Wind Style Heat
Abstract associations
Appliance store
Hair salon
Parahippocampal cortex
Inferior temporal cortex
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626 | AUGUST 2004 | VOLUME 5
mediate both bottom–up and top–down cortical
.I previously proposed a detailed
mechanism for how such top–down processing would
be triggered to facilitate object recognition
.In the pro-
posed framework,low spatial frequencies in the image
are extracted quickly and projected from early visual
areas to the PFC.This projection is considerably faster
than the thorough bottom–up analysis,and therefore is
predicted to use fast anatomical connections — possibly
the magnocellular pathway, which propagates low-
spatial-frequency information early and rapidly
The global information that is conveyed by low spatial
frequencies is typically sufficient to activate a small
set of probable candidate interpretations of the input
(‘initial guesses’).When the input representation is
associated with one of the candidates, recognition
is accomplished and the other initial guesses are no
longer active.Preliminary and unpublished data from
my laboratory support this model by showing that:
differential activity that is diagnostic of recognition
success develops in the orbital PFC significantly earlier
than it does in the temporal cortex,as shown by MEG
physiological recordings ( al,submitted);the
fMRI signal in the orbital PFC is significantly stronger
for low spatial frequencies than for high spatial
;and orbital PFC activity increases as a
direct function of the number of alternative interpreta-
tions that can be produced about the object image on
the basis of its low spatial frequencies
This model is expanded in this article from the
recognition of individual, isolated objects to entire
scenes.The expansion makes the proposal more ecolog-
ically valid and accounts for the processes that are
involved in the extraction of context and its use for
recognizing objects in scenes.It has been proposed
that the orbital PFC might contain a ‘look-up table’that
maps the low-spatial-frequency appearances of objects
to their most probable interpretations.Along the same
lines,the PHC could map low-spatial-frequency repre-
sentations to the most likely context frames, which
contain information about possible objects
(FIG.4) and
their spatial arrangement. Unlike the PFC,the PHC has
not been shown explicitly to receive direct magnocellular
connections from early visual cortex.However, it does
receive massive visual input (in addition to input from
other modalities) and,on the basis of its anatomical
architecture and output connections,it is sometimes
considered part of the visual system
In addition to psychophysical and computational
,single-unit recordings in monkeys
also indicate that low spatial frequencies are extracted
from scenes earlier than high spatial frequencies,and
that this global information can be sufficient for scene
.Activity in the ITC is initially broadly
tuned and represents only the global features (the low
spatial frequencies) of a stimulus
.Later, 51 ms after
the onset of the global response
,neurons in the ITC
also represent the fine attributes of the image, presum-
ably propagated by the high spatial frequencies.So,the
ITC responds to low-spatial-frequency information
before it receives high-spatial-frequency information.
This model focuses on contextual facilitation of
visual object recognition, and two particular exclusions
should be noted.First, the model is not intended to
explain contextual influences in letter, word or sentence
recognition. Recognition in the language domain, for
which many accounts have been proposed
ably benefits from context through mechanisms other
than those that mediate object recognition,in spite of
several similarities. Second, contextual information
involving human faces, which can be considered as
uniquely important visual objects, might also be
analysed separately from other objects. On the one
hand,recognizing a face outside of its typical context
(your dentist on the beach) will be harder than when it
benefits from a consistent context.In the present model,
such conflicts will be expressed by the wrong expecta-
tions being elicited by the context frame. On the other
hand,it is not clear how helpful low spatial frequencies
are with regard to limiting the possible identities of
faces,and it is therefore not clear how faces would be
incorporated into this proposal.Face identification is in
many respects analogous to subordinate-level individu-
ation,and even for non-face objects,such knowledge is
available only after the arrival of details in higher spatial
In support of this model, anatomical studies have
shown both ascending and descending connections
between visual areas
,and these connections might
Low spatial
Low spatial
LF image
Possible identities
of the highlighted
LF image
Most likely
Context frame
+ High spatial
High spatial frequencies
Figure 5 | The proposed model for the contextual facilitation of object recognition. The
early intersection of the association set in the context frame with the candidate interpretations of
the individual target object results in rapid recognition of that object as a generic beach umbrella.
The exact representation of the specific exemplar is subsequently derived from the later arrival of
higher spatial frequencies. Several of the specific cortical mechanisms have yet to be
characterized, and the assignment of functions to specific cortical regions in the proposed model
might be refined as more data become available. In particular, current reports make it plausible
that other medial temporal structures, in addition to the parahippocampal cortex (PHC), might
contribute to the analysis of various aspects of associations. For simplicity, only the relevant
connections and flow directions are illustrated here. ITC, inferior temporal cortex; LF, low
frequency; PFC, prefrontal cortex; V2 and V4, early visual areas. ‘Lightening strike’ symbol
represents activation of representations.
© 2004
for many relevant findings.For example,individual
objects in isolation seem to be recognized more easily
than if they are embedded in contextually congruent
.By definition, isolated objects occur without
background. Consequently, their low-frequency
appearance will be less ambiguous than when they
are surrounded by other objects,resulting in a more
precise initial guess from the PFC to the ITC, and
therefore more efficient recognition compared with
individual objects in a scene background.Second, the
identity of an individual object can help subjects to rec-
ognize the background scene
.In the proposed model,
the initial recognition of a semantically informative
object in the scene,mediated by the pathway from the
PFC to the ITC, can result in a projection from the ITC
to the PHC that will elicit the context frame that corre-
sponds to the recognized object. This pathway is
expected to be more helpful when the recognition of
the background is not straightforward,for example,
in atypical instances of familiar contexts.Third,the
identity of an otherwise ambiguous object can be
disambiguated by the presence of another object that is
contextually related to the target object
.In such cases,
the recognition of one object would activate a context
frame that would improve the imprecise initial guess
that was produced by the ambiguous object. In addi-
tion,when both objects are ambiguous, their visual and
spatial properties can activate the correct context
frame, which then activates a set of expectations that
facilitates their relatively late but successful recognition.
This mechanism might also explain why a particular
object can be interpreted differently in different
(BOX 1), and how an object that does not
belong in the context would elicit an N400 effect in the
PHC after all the alternatives from the PFC have been
compared with those activated by the context frame
and no intersection has been found.
Our brain takes advantage of common associations
among objects in the environment to facilitate visual
perception and cognition. The cortical network that
mediates the processing of such contextual associations
and their interface with object recognition involves
regions in the PHC,PFC and RSC. Important open
questions include: how are context frames represented
in the cortex,and what triggers their activation? How is
contextual information translated into expectations?
How does context facilitate object recognition? How
is gist information from a contextual scene represented
in the brain? And how do motivation and attention
modulate contextual processing? I have proposed
a testable model for the rapid use of contextual asso-
ciations in recognition,in which an early projection
of coarse information can activate expectations about
context and identity that, when combined, result in
successful object recognition.With progress in spatio-
temporal neuroimaging and theoretical formulation,we
are certainly on the verge of exciting discoveries about
the behavioural and cortical mechanisms that combine
visual elements into rich,coherent scenes.
According to this model,early in scene analysis, the
ITC has access to a sensitized set of associated objects
from the PHC (where the level of sensitization of each
object’s representation depends on the strength of its
association with the specific context),and an ‘initial guess’
from the PFC containing the most likely interpretations
ofthe target object.To select the correct identity from
these initial guesses,an intersection operation, which can
be considered as the neuronal equivalent of an AND’
function followed by selective inhibition and excitation, is
performed. For example,if the PFC ‘suggests’that the
object might be a television set, a microwave or a fire-
place,and the PHC suggests that the context is a kitchen,
the microwave alternative is selected,and all other candi-
dates can be suppressed.Note,however, that if the context
in this example were a living room, the output of the
intersection would still remain ambiguous, because a tele-
vision and a fireplace are equally likely.In such cases,final
recognition is accomplished only after the arrival of more
detail,which is conveyed by the higher spatial frequencies.
Shifting gaze and/or attention would make the PFC
shift its focus to different objects of interest,which in
turn would result in a different set of initial guesses being
transmitted to the ITC. However, assuming that the
scenes context has not changed between shifts, there will
be little or no change in the context frame projected
from the PHC.Furthermore,the top–down projection
from the PFC might not be as crucial for the develop-
ment of expectations in the ITC as the projection that
stemmed from focusing on the first object.In that sense,
the identification of the context and of the first object
bootstraps the recognition, at some level, of the complete
scene.That observers fixate first on the most informative
aspects of a scene
indicates that our system might oper-
ate to maximize the extraction of contextual information
from the first fixation.
Although context frames have been suggested to
represent both object identities and their spatial relations,
this model emphasizes identities more than relations.
This is partly because not much is known about the corti-
cal analysis ofspatial arrangements (or about analysis of
the other relations that define a scene),and partly because
the availability ofcontextual and identity information is
typically sufficient for object recognition.Indeed,when
scenes are presented briefly, information about objects
identities seems to be acquired earlier than information
about their locations
.The representation of typical
spatial relations can nevertheless be a powerful principle
for guiding expectations,attention and eye movements,
and so is certain to have a central role.
What is the role of the magnocellular projections
from early visual cortex to the ITC
? These projections
might provide the ITC with a spatial template in which
to anchor’the interpretations derived from the initial
guesses.Note that the proposed projections from the
PFC and PHC to the ITC lack information about the
spatial arrangement of the scene elements,which can be
supplemented by the direct magnocellular projection of
the blurred scene from early visual cortex to the ITC.
Importantly, all aspects of the model can be
addressed experimentally. Furthermore,it can account
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628 | AUGUST 2004 | VOLUME 5
1. Biederman, I., Mezzanotte, R. J. & Rabinowitz, J. C. Scene
perception: detecting and judging objects undergoing
relational violations. Cogn. Psychol. 14, 143–177 (1982).
A seminal study that characterizes the rules that govern
a scene’s structure and their influence on perception.
2. Bar, M. & Ullman, S. Spatial context in recognition.
Perception 25, 343–352 (1996).
3. Kanwisher, N., McDermott, J. & Chun, M. M. The fusiform
face area: a module in human extrastriate cortex specialized
for face perception. J. Neurosci. 17, 4302–4311 (1997).
4. Puce, A., Allison, T., Asgari, M., Gore, J. C. & McCarthy, G.
Differential sensitivity of human visual cortex to faces,
letterstrings, and textures: a functional magnetic resonance
imaging study. J. Neurosci. 16, 5205–5215 (1996).
5. Martin, A. et al. Neural correlates of category-specific
knowledge. Nature379, 649–652 (1996).
6. Ishai, A. et al. Distributed representation of objects in the
human ventral visual pathway. Proc. Natl Acad. Sci. USA 96,
9379–9384 (1999).
7. Grill-Spector, K., Kourtzi, Z. & Kanwisher, N. The lateral
occipital complex and its role in object recognition. Vision
Res. 41, 1409–1422 (2001).
8. Malach, R., Levy, I. & Hasson, U. The topography of high-
order human object areas. Trends Cogn. Sci. 6, 176–184
9. Tanaka, K. Neuronal mechanisms of object recognition.
Science 262, 685–688 (1993).
10. Haxby, J. V. et al. Distributed and overlapping
representations of faces and objects in ventral temporal
cortex. Science293, 2425–2430 (2001).
11. Downing, P. E. et al. A cortical area selective for visual
processing of the human body. Science 293, 2470–2473
12. Bar, M. & Aminoff, E. Cortical analysis of visual context.
Neuron 38, 347–358 (2003).
Defines the cortical regions that are directly involved
in the contextual analysis of visual objects.
13. Gabrieli, J. D., Poldrack, R. A. & Desmond, J. E. The role of
left prefrontal cortex in language and memory. Proc. Natl
Acad. Sci. USA 95, 906–913 (1998).
14. Biederman, I. et al. On the information extracted from a
glance at a scene. J. Exp. Psychol. 103, 597–600 (1974).
15. Bartlett, F. C. Remembering: A Study in Experimental and
Social Psychology (Cambridge Univ. Press, Cambridge, UK,
16. Mandler, J. M. in Memory Organization and Structure (ed.
Puff, C. R.) 259–299 (Academic, New York, 1979).
17. Palmer, S. E. The effects of contextual scenes on the
identification of objects. Mem. Cogn. 3, 519–526 (1975).
One of the earliest and most compelling reports of
contextual influences on object recognition.
18. Piaget, J. The Child’s Construction of Reality (Routledge &
Kegan Paul, London, 1955).
19. Schank, R. C. in Theoretical Issues in Natural Language
Processing (eds Schank, R. C. & Nash-Weber, B.) 117–121
(Tinlap, Arlington, Virginia, 1975).
20. Minsky, M. in The Psychology of Computer Vision (ed.
Winston, P. H) 211–277 (McGraw-Hill, New York, 1975).
21. Friedman, A. Framing pictures: the role of knowledge in
automatized encoding and memory for gist. J. Exp. Psychol.
Gen. 108, 316–355 (1979).
A thorough study of the concept of frames in
contextual representations
22. Barsalou, L. W. in Frames, Fields, and Contrasts: New
Essays in Semantic and Lexical Organization (eds Kittay, E.
& Lehrer, A.) 21–74 (Lawrence Erlbaum Associates,
Hillsdale, New Jersey, 1992).
23. Mandler, J. M. & Johnson, N. S. Some of the thousand
words a picture is worth. J. Exp. Psychol. Hum. Learn.
Mem. 2, 529–540 (1976).
24. Intraub, H. et al. Boundary extension for briefly glimpsed
photographs: do common perceptual processes result in
unexpected memory distortions? J. Mem. Lang. 35,
118–134 (1996).
25. Gottesman, C. V. & Intraub, H. Wide-angle memories of
close-up scenes: a demonstration of boundary extension.
Behav. Res. Methods Instrum. Comput. 31, 86–93 (1999).
26. Miller, M. B. & Gazzaniga, M. S. Creating false memories for
visual scenes. Neuropsychologia 36, 513–520 (1998).
27. Hock, H. S. et al. Real-world schemata and scene
recognition in adults and children. Mem. Cogn. 6, 423–431
28. Cutler, B. L. & Penrod, S. D. in Memory in Context: Context
in Memory (eds Davies, G. M. & Thomson, D. M.) 231–244
(John Wiley & Sons Ltd, New York, 1988).
29. Oliva, A. & Torralba, A. Modeling the shape of a scene:
a holistic representation of the spatial envelope. Int.
J. Comput. Vision 42, 145–175 (2001).
Provides computational demonstrations that low
spatial frequencies are generally sufficient for scene
30. Henderson, J. M. & Hollingworth, A. High-level scene
perception. Annu. Rev. Psychol. 50, 243–271 (1999).
A systematic review that elaborates on the opposition
to the notion that context can facilitate object
31. Chun, M. M. Contextual cueing of visual attention. Trends
Cogn. Sci. 4, 170–178 (2000).
32. Intraub, H. The representation of visual scenes. Trends
Cogn. Sci. 1, 217–222 (1997).
33. Palmer, S. E. Vision Science. Photons to Phenomenology
(MIT Press, Cambridge, Massachusetts, 1999).
34. Lowe, D. G. Perceptual Organization and Visual Recognition
(Kluwer, Boston, 1985).
35. Ullman, S. Aligning pictorial descriptions: an approach to
object recognition. Cognition 32, 193–254 (1989).
36. Murphy, G. L. & Wisniewski, E. J. Categorizing objects in
isolation and in scenes: what a superordinate is good for.
J. Exp. Psychol. Learn. Mem. Cogn. 15, 572–586 (1989).
37. Davenport, J. L. & Potter, M. C. Scene consistency in object
and background perception. Psychol. Sci. 15, 559–564
38. Boyce, S. J., Pollatsek, A. & Rayner, K. Effect of background
information on object identification. J. Exp. Psychol. Hum.
Percept. Perform. 15, 556–566 (1989).
39. Metzger, R. L. & Antes, J. R. The nature of processing early
in picture perception. Psychol. Res. 45, 267–274 (1983).
40. Bar, M. A cortical mechanism for triggering top–down
facilitation in visual object recognition. J. Cogn. Neurosci.
15, 600–609 (2003).
Describes some of the conceptual bases for the
model of contextual facilitation that is proposed in
this review.
41. Kosslyn, S. M. Image and Brain (MIT Press, Cambridge,
Massachusetts, 1994).
42. de Graef, P., de Troy, A. & d’Ydewalle, G. Local and global
contextual constraints on the identification of objects in
scenes. Can. J. Psychol. 46, 489–508 (1992).
43. Hollingworth, A. & Henderson, J. M. Does consistent scene
context facilitate object perception? J. Exp. Psychol. Gen.
127, 398–415 (1998).
44. Auckland, M., Cave, K. R. & Donnelly, N. Perceptual errors in
object recognition are reduced by the presence of context
objects. Abstr. Psychon. Soc. 8, 109 (2003).
45. VanRullen, R. & Thorpe, S. J. The time course of visual
processing: from early perception to decision-making.
J. Cogn. Neurosci. 13, 454–461 (2001).
46. Potter, M. C. & Faulconer, B. A. Time to understand pictures
and words. Nature 253, 437–438 (1975).
This paper reports evidence for the speed with which
a scene can be comprehended.
47. Ullman, S. High-Level Vision (MIT Press, Cambridge,
Massachusetts, 1996).
48. Gibson, J. J. The Ecological Approach to Visual Perception
(Houghton Mifflin, Boston, 1979).
49. Moores, E., Laiti, L. & Chelazzi, L. Associative knowledge
controls deployment of visual selective attention. Nature
Neurosci. 6, 182–189 (2003).
50. Rumelhart, D. E., McClelland, J. E. & The PDP Research
Group. Parallel Distributed Processing: Explorations in the
Microstructure of Cognition Vol. 1 (MIT Press, Cambridge,
Massachusetts, 1986).
51. Sigman, M. et al. On a common circle: natural scenes and
Gestalt rules. Proc. Natl Acad. Sci. USA 98, 1935–1940
52. McCauley, C. et al. Early extraction of meaning from pictures
and its relation to conscious identification. J. Exp. Psychol.
Hum. Percept. Perform. 6, 265–276 (1980).
53. Carr, T. H. et al. Words, pictures, and priming: on semantic
activation, conscious identification, and the automaticity of
information processing. J. Exp. Psychol. Hum. Percept.
Perform. 8, 757–777 (1982).
54. Bar, M. & Biederman, I. Subliminal visual priming. Psychol.
Sci. 9, 464–469 (1998).
55. Potter, M. C. Short-term conceptual memory for pictures.
J. Exp. Psychol. Hum. Learn. Mem. 2, 509–522 (1976).
56. Intraub, H. Rapid conceptual identification of sequentially
presented pictures J. Exp. Psychol. Learn. Mem. Cogn. 10,
115–125 (1981).
57. Loftus, G. R. in Eye Movements and Psychological
Processes (eds Senders, J. & Monty, R.) 499–513
(Lawrence Erlbaum Associates, Hillsdale, New Jersey,
58. Schyns, P. G. & Oliva, A. Flexible, diagnosticity-driven, rather
than fixed, perceptually determined scale selection in scene
and face recognition. Perception26, 1027–1038 (1997).
59. Schyns, P. G. & Oliva, A. From blobs to boundary edges:
evidence for time- and spatial- dependent scene
recognition. Psychol. Sci. 5, 195–200 (1994).
An elegant study showing that observers can
categorize a scene briefly on the basis of the low-
spatial-frequency content in the image.
60. Chun, M. M. & Jiang, Y. Contextual cueing: implicit learning
and memory of visual context guides spatial attention.
Cogn. Psychol. 36, 28–71 (1998).
A convincing demonstration that contextual
information can be learned without awareness.
61. Chun, M. M. & Phelps, E. A. Memory deficits for implicit
contextual information in amnesic subjects with
hippocampal damage. Nature Neurosci. 2, 844–847 (1999).
62. Good, M., de Hoz, L. & Morris, R. G. Contingent versus
incidental context processing during conditioning:
dissociation after excitotoxic hippocampal plus dentate
gyrus lesions. Hippocampus 8, 147–159 (1998).
63. Li, F. F. et al. Rapid natural scene categorization in the near
absence of attention. Proc. Natl Acad. Sci. USA 99,
9596–9601 (2002).
64. Mathis, K. M. Semantic interference from objects both in
and out of a scene context. J. Exp. Psychol. Learn. Mem.
Cogn. 28, 171–182 (2002).
65. Kouider, S. & Dupoux, E. Partial awareness creates the
‘illusion’ of subliminal semantic priming. Psychol. Sci. 15,
75–81 (2004).
66. Tsivilis, D., Otten, L. J. & Rugg, M. D. Context effects on the
neural correlates of recognition memory: an
electrophysiological study. Neuron 31, 497–505 (2001).
67. Olson, I. R., Chun, M. M. & Allison, T. Contextual guidance
of attention: human intracranial event-related potential
evidence for feedback modulation in anatomically early
temporally late stages of visual processing. Brain124,
1417–1425 (2001).
68. Kassam, K. S., Aminoff, E. & Bar, M. Spatial-temporal
cortical processing of contextual associations. Soc.
Neurosci. Abstr. 128.8 (2003).
69. Squire, L. R., Stark, C. E. L. & Clark, R. E. The medial
temporal lobe. Annu. Rev. Neurosci. 27, 279–306 (2004).
A clear and thorough review of the controversy
surrounding the functional distinction of the various
sub-regions within the medial temporal lobe.
70. Brown, M. W. & Aggleton, J. P. Recognition memory: what
are the roles of the perirhinal cortex and hippocampus?
Nature Rev. Neurosci. 2, 51–61 (2001).
71. Eichenbaum, H. The hippocampus and declarative
memory: cognitive mechanisms and neural codes. Behav.
Brain Res. 127, 199–207 (2001).
72. Schacter, D. L. & Wagner, A. D. Medial temporal lobe
activations in fMRI and PET studies of episodic encoding
and retrieval. Hippocampus 9, 7–24 (1999).
73. Giovanello, K. S., Verfaellie, M. & Keane, M. M.
Disproportionate deficit in associative recognition relative to
item recognition in global amnesia. Cogn. Affect. Behav.
Neurosci. 3, 186–194 (2003).
74. Stark, C. E. & Squire, L. R. Simple and associative
recognition memory in the hippocampal region. Learn.
Mem. 8, 190–197 (2001).
75. Aguirre, G. K. et al. The parahippocampus subserves
topographical learning in man. Cereb. Cortex 6, 823–829
76. Epstein, R. & Kanwisher, N. A cortical representation of the
local visual environment. Nature392, 598–601 (1998).
This paper coined the term ‘parahippocampal place
area’ (PPA).
77. Maguire, E. A. et al. Knowing where things are:
parahippocampal involvement in encoding object locations
in virtual large-scale space. J. Cogn. Neurosci. 10, 61–76
78. Epstein, R., Graham, K. S. & Downing, P. E. Viewpoint-
specific scene representations in human parahippocampal
cortex. Neuron37, 865–876 (2003).
79. Sanocki, T. & Epstein, W. Priming spatial layout of scenes.
Psychol. Sci. 8, 374–378 (1997).
80. Christou, C. G. & Bülthoff, H. H. View dependence in scene
recognition after active learning. Mem. Cogn. 27, 996–1007
81. Levy, I. et al. Center-periphery organization of human object
areas. Nature Neurosci. 4, 533–539 (2001).
Provides a systematic alternative view of the
organization of the visual cortex.
82. Epstein, R. A. The cortical basis of visual scene processing.
Visual Cogn. (in the press).
83. Nakamura, K. et al. Functional delineation of the human
occipito-temporal areas related to face and scene
processing. A PET study. Brain 123, 1903–1912 (2000).
84. Stern, C. E. et al.The hippocampal formation participates in
novel picture encoding: evidence from functional magnetic
resonance imaging. Proc. Natl Acad. Sci. USA 93,
8660–8665 (1996).
85. Gaffan, D. Scene-specific memory for objects: a model of
episodic memory impairment in monkeys with fornix
transection. J. Cogn. Neurosci. 6, 305–320 (1994).
86. Bartels, A. & Zeki, S. Functional brain mapping during free
viewing of natural scenes. Hum. Brain Mapp. 21, 75–85
© 2004
87. Bar, M. et al. Cortical mechanisms of explicit visual object
recognition. Neuron 29, 529–535 (2001).
88. Kutas, M. & Hillyard, S. A. Reading senseless sentences:
brain potentials reflect semantic incongruity. Science 207,
203–205 (1980).
89. Ganis, G. & Kutas, M. An electrophysiological study of
scene effects on object identification. Brain Res. Cogn. Brain
Res. 16, 123–144 (2003).
Reports interesting observations about the temporal
dynamics of contextual analysis in scene recognition.
90. Smith, M. E., Stapleton, J. M. & Halgren, E. Human medial
temporal lobe potentials evoked in memory and language
tasks. Electroencephalogr. Clin. Neurophysiol. 63, 145–159
91. McCarthy, G. et al. Language-related field potentials in the
anterior-medial temporal lobe: I. Intracranial distribution and
neural generators. J. Neurosci. 15, 1080–1089 (1995).
92. Paivio, A. Imagery and Verbal Processes (Holt, Reinhart, &
Winston, New York, 1971).
93. Paivio, A. Dual coding theory: retrospect and current status.
Can. J. Psychol. 45, 255–287 (1991).
94. Glaser, W. R. Picture naming. Cognition 42, 61–105 (1992).
95. Riddoch, M. J. et al. Semantic systems or system?
Neuropsychological evidence re-examined. Cogn.
Neuropsychol. 5, 3–25 (1988).
96. Holcomb, P. J. & McPherson, W. B. Event-related brain
potentials reflect semantic priming in an object decision
task. Brain Cogn. 24, 259–276 (1994).
97. West, W. C. & Holcomb, P. J. Imaginal, semantic, and
surface-level processing of concrete and abstract words: an
electrophysiological investigation. J. Cogn. Neurosci. 12,
1024–1037 (2000).
98. Federmeier, K. D. & Kutas, M. Meaning and modality:
influences of context, semantic memory organization, and
perceptual predictability on picture processing. J. Exp.
Psychol. Learn. Mem. Cogn. 27, 202–224 (2001).
99. Vandenberghe, R. et al. Functional anatomy of a common
semantic system for words and pictures. Nature383,
254–256 (1996).
100. Smith, M. C. & Magee, L. E. Tracing the time course of
picture — word processing. J. Exp. Psychol. Gen. 109,
373–392 (1980).
101. Glaser, W. R. & Dungelhoff, F. J. The time course of picture-
word interference. J. Exp. Psychol. Hum. Percept. Perform.
10, 640–654 (1984).
102. Marinkovic, K. et al. Spatiotemporal dynamics of modality-
specific and supramodal word processing. Neuron38,
487–497 (2003).
103. Sperling, R. et al. Putting names to faces: successful
encoding of associative memories activates the anterior
hippocampal formation. Neuroimage 20, 1400–1410
104. Halgren, E. et al. Spatio-temporal stages in face and word
processing. 2. Depth-recorded potentials in the human
frontal and Rolandic cortices. J. Physiol. (Paris) 88, 51–80
105. Dale, A. M. et al. Dynamic statistical parametric mapping:
combining fMRI and MEG for high-resolution imaging of
cortical activity. Neuron 26, 55–67 (2000).
One of the best demonstrations of high-resolution
spatiotemporal imaging, with a clear description of
the theoretical background.
106. Kuperberg, G. R. et al. Distinct patterns of neural modulation
during the processing of conceptual and syntactic
anomalies. J. Cogn. Neurosci. 15, 272–293 (2003).
107. Burgess, N. et al. A temporoparietal and prefrontal network
for retrieving the spatial context of lifelike events.
Neuroimage 14, 439–453 (2001).
108. Simons, J. S. & Spiers, H. J. Prefrontal and medial temporal
lobe interactions in long-term memory. Nature Rev.
Neurosci. 4, 637–648 (2003).
109. Maguire, E. A. The retrosplenial contribution to human
navigation: a review of lesion and neuroimaging findings.
Scand. J. Psychol. 42, 225–238 (2001).
110. Cooper, B. G. & Mizumori, S. J. Temporary inactivation of
the retrosplenial cortex causes a transient reorganization of
spatial coding in the hippocampus. J. Neurosci. 21,
3986–4001 (2001).
111. Vann, S. D. & Aggleton, J. P. Extensive cytotoxic lesions of
the rat retrosplenial cortex reveal consistent deficits on tasks
that tax allocentric spatial memory. Behav. Neurosci. 116,
85–94 (2002).
112. Düzel, E. et al. Human hippocampal and parahippocampal
activity during visual associative recognition memory for
spatial and nonspatial stimulus configurations. J. Neurosci.
23, 9439–9444 (2003).
113. Burwell, R. D. et al. Corticohippocampal contributions to
spatial and contextual learning. J. Neurosci. 24, 3826–3836
114. Mendez, M. F. & Cherrier, M. M. Agnosia for scenes in
topographagnosia. Neuropsychologia41, 1387–1395 (2003).
115. Henke, K. et al. Human hippocampus associates
information in memory. Proc. Natl Acad. Sci. USA 96,
5884–5889 (1999).
116. Jackson, O. & Schacter, D. L. Encoding activity in anterior
medial temporal lobe supports subsequent associative
recognition. Neuroimage 21, 456–462 (2004).
117. Hayes, S. M. et al. An fMRI study of episodic memory:
retrieval of object, spatial, and temporal order information.
Behav. Neurosci. (in the press).
118. Buckley, M. J. & Gaffan, D. Perirhinal cortex ablation impairs
configural learning and paired-associate learning equally.
Neuropsychologia 36, 535–546 (1998).
119. Insausti, R., Amaral, D. G. & Cowan, W. M. The entorhinal
cortex of the monkey: II. Cortical afferents. J. Comp. Neurol.
264, 356–395 (1987).
120. Ranganath, C. & D’Esposito, M. Medial temporal lobe
activity associated with active maintenance of novel
information. Neuron31, 865–873 (2001).
121. Valenstein, E. et al.Retrosplenial amnesia. Brain 110,
1631–1646 (1987).
122. Hirsh, R. The hippocampus and contextual retrieval of
information from memory: a theory. Behav. Psychol. 12,
421–444 (1974).
123. Redish, A. D. The hippocampal debate: are we asking the
right questions? Behav. Brain Res. 127, 81–98 (2001).
124. Miller, R. Cortico-Hippocampal Interplay and the
Representation of Contexts in the Brain. Studies of Brain
Function. Vol. 17 (Springer, Berlin, 1991).
125. O’Keefe, J. & Nadel, L. The Hippocampus as a Cognitive
Map (Clarendon, Oxford, 1978).
126. O’Keefe, J. & Dostrovsky, J. The hippocampus as a spatial
map. Preliminary evidence from unit activity in the freely-
moving rat. Brain Res. 34, 171–175 (1971).
127. Naya, Y., Yoshida, M. & Miyashita, Y. Forward processing of
long-term associative memory in monkey inferotemporal
cortex. J. Neurosci. 23, 2861–2871 (2003).
128. Naya, Y., Yoshida, M. & Miyashita, Y. Backward spreading of
memory-retrieval signal in the primate temporal cortex.
Science 291, 661–664 (2001).
129. Higuchi, S. & Miyashita, Y. Formation of mnemonic neuronal
responses to visual paired associates in inferotemporal
cortex is impaired by perirhinal and entorhinal lesions. Proc.
Natl Acad. Sci. USA 93, 739–743 (1996).
130. Cox, D., Meyers, E. & Sinha, P. Contextually evoked object-
specific responses in human visual cortex. Science304,
115–117 (2004).
131. Torralba, A. Contextual priming for object detection. Int. J.
Comput. Vision 53, 153–167 (2003).
132. Kersten, D., Mamassian, P. & Yuille, A. Object perception as
Bayesian inference. Annu. Rev. Psychol. 55, 271–304
133. Hebb, D. O. The Organization of Behavior (Wiley, New York,
134. Dudai, Y. The Neurobiology of Memory (Oxford Univ. Press,
Oxford, 1989).
135. McClelland, J. L. & Rumelhart, D. E. An interactive activation
model of context effects in letter perception: part 1. An
account of basic findings. Psychol. Rev. 88, 375–407 (1981).
136. Felleman, D. J. & Van Essen, V. C. Distributed hierarchical
processing in primate visual cortex. Cereb. Cortex 1, 1–47
137. Rempel-Clower, N. L. & Barbas, H. The laminar pattern of
connections between prefrontal and anterior temporal
cortices in the rhesus monkey is related to cortical structure
and function. Cereb. Cortex 10, 851–865 (2000).
138. Ullman, S. Sequence seeking and counter streams: a
computational model for bidirectional information flow in the
visual cortex. Cereb. Cortex 1, 1–11 (1995).
Provides a theory and compelling demonstrations for
the existence and role of bidirectional processes in
the cortex.
139. Grossberg, S. How does a brain build a cognitive code?
Psychol. Rev. 87, 1–51 (1980).
140. Graboi, D. & Lisman, J. Recognition by top–down and
bottom–up processing in cortex: the control of selective
attention. J. Neurophysiol. 90, 798–810 (2003).
141. Merigan, W. H. & Maunsell, J. H. How parallel are the
primate visual pathways? Annu. Rev. Neurosci. 16, 369–402
142. Bullier, J. & Nowak, L. G. Parallel versus serial processing:
new vistas on the distributed organization of the visual
system. Curr. Opin. Neurobiol. 5, 497–503 (1995).
143. Schmid, A. M. & Bar, M. Selective involvement of prefrontal
cortex in visual object recognition. Soc. Neurosci. Abstr.
161.8 (2002).
144. Schmid, A. M. & Bar, M. Activation of multiple candidate
object representations during top–down facilitation of visual
recognition. Soc. Neurosci. Abstr. 128.5 (2003).
145. Pandya, D. N., Seltzer, B. & Barbas, H. in Comparative
Primate Biology, Vol. IV: Neurosciences (eds Staklis, H. D. &
Erwin, J.) 39–80 (Alan R. Liss, New York, 1988).
146. Mannan, S. K., Ruddock, K. H. & Wooding, D. S. Fixation
patterns made during brief examination of two-dimensional
images. Perception26, 1059–1072 (1997).
147. Tamura, H. & Tanaka, K. Visual response properties of cells in
the ventral and dorsal parts of the macaque inferotemporal
cortex. Cereb. Cortex 11, 384–399 (2001).
148. Sugase, Y. et al. Global and fine information coded by single
neurons in the temporal visual cortex. Nature400, 869–873
149. Antes, J. R. Recognizing and localizing features in brief
picture presentations. Mem. Cogn. 5, 155–161 (1977).
150. Nowak, L. G. & Bullier, J. in Cerebral Cortex: Extrastriate
Cortex in Primate (eds Rockland, K., Kaas, J. & Peters, A.)
205–241 (Plenum, New York, 1997).
151. Torralba, A. & Oliva, A. Statistics of natural image categories.
Network 14, 391–412 (2003).
152. Rensink, R., O’Regan, J. & Clark, J. To see or not to see: the
need for attention to perceive changes in scenes. Psychol.
Sci. 8, 368–373 (1997).
153. Simons, D. J. & Levin, D. T. Change blindness. Trends
Cogn. Sci. 1, 261–267 (1997).
154. Haber, R. N. & Schindler, R. M. Errors in proofreading:
evidence of syntactic control of letter processing. J. Exp.
Psychol. Hum. Percept. Perf. 7, 573–579 (1981).
155. Morris, A. L. & Harris, C. L. Sentence context, word
recognition, and repetition blindness. J. Exp. Psychol. Learn.
Mem. Cogn. 28, 962–982 (2002).
156. Kanwisher, N. G. Repetition blindness: type recognition
without token individuation. Cognition 27, 117–143
156. Green, R. T. & Courtis, M. C. Information theory and figure
perception: the metaphor that failed. Acta Psychol. (Amst.)
25, 12–35 (1966).
I would like to thank members of my lab, E. Aminoff, H. Boshyan,
M. Fenske, A. Ghuman, N. Gronau and K. Kassam, as well as A.
To rralba, N. Donnelly, M. Chun, B. Rosen and A. Oliva for help with
this article. Supported by the National Institute of Neurological
Disorders and Stroke, the James S. McDonnell Foundation (21st
Century Science Research Award in Bridging Brain, Mind and
Behavior) and the MIND Institute.
Competing interests statement
The author declares no competing financial interests.
Online links
Bar laboratory:
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The use of prior knowledge to guide perception is fundamental to human vision, especially under challenging viewing circumstances. Underpinning current theories of predictive coding, prior knowledge delivered to early sensory areas via cortical feedback connections can reshape perception of ambiguous stimuli, such as 'two-tone' images. Despite extensive interest and ongoing research into this process of perceptual reorganisation in the adult brain, it is not yet fully understood how or when the efficient use of prior knowledge for visual perception develops. Here we show for the first time that adult-like levels of perceptual reorganisation do not emerge until late childhood. We used a behavioural two-tone paradigm to isolate the effects of prior knowledge on visual perception in children aged 4 - 12 years and adults, and found a clear developmental progression in the perceptual benefit gained from informative cues. Whilst photo cueing reliably triggered perceptual reorganisation of two-tones for adults, 4- to 9-year-olds' performed significantly poorer immediately after cueing than within-subject benchmarks of recognition. Young childens' behaviour revealed perceptual biases towards local image features, as has been seen in image classification neural networks. We tested three such models (AlexNet, CorNet and NASNet) on two-tone classification, and while we found that network depth and recurrence may improve recognition, the best-performing network behaved similarly to young children. Our results reveal a prolonged development of prior-knowledge-guided vision throughout childhood, a process which may be central to other perceptual abilities that continue developing throughout childhood. This highlights the importance of effective reconciliation of signal and prediction for robust perception in both human and computational vision systems.
... According to the previous literature, a familiar or congruent objectcontext association (e.g. spoon in a cup of tea on a table) in a scene can enhance the visual performance whereas an unfamiliar or an incongruent object-context association (a toothbrush instead of the spoon in a cup of tea on a table) could often lead to performance reduction in object identification/categorization tasks although "out of context objects" are better remembered or easily detected (Bar, 2004;Bar and Ullman, 1996;Ganis and Kutas, 2003;Mudrik et al., 2011;Ohman et al., 2001;Rémy et al., 2014;Simpson et al., 2000)). Similarly, in visual search tasks where a unique target must be found among distractors, the detection times were faster when the target had some emotional value, such as an angry or happy face among neutral faces or a snake or spider among flowers (Eastwood et al., 2001;Fox, 2002;Ohman et al., 2001). ...
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Object-context associations and valence are two unavoidable stimulus characteristics when it comes to the processing of natural visual scenes. In line with our previous studies exploring the parallel processing of context-congruity and valence, in the current study, we investigated the valence-specific differences in functional connectivity between congruent-incongruent picture pairs during binocular rivalry using high-density EEG. The functional connectivity measure was calculated using sLORETA during the perceptual dominance of congruent and incongruent stimuli in a time window of 400 ms before the response and compared within and between positive, negative, and neutral valence categories (84 Brodmann’s areas across 7 frequency bands) using t-tests. A significant difference in functional connectivity between congruent-incongruent picture pairs was seen only when associated with negative valence and a maximum number of area pairs showed differences in lower alpha 1 (7.1 - 9 Hz), upper alpha (11.1 - 13 Hz), and beta (13.1 - 30 Hz) frequency bands. The functional connectivity was significantly lower during incongruent perception between the area pairs which process mainly emotion, attention, memory, and semantic relations compared to their corresponding congruent stimuli. Similarly, negative incongruent percepts were found to have significantly lower connectivity between areas processing attention, emotion, and incongruence in the lower alpha 2 (9.1 -11 Hz) band when compared to positive incongruent percepts. Together, these results suggest that the perception of negative incongruence is associated with lower functional connectivity and this could be a possible reason for the increased error rates when faced with incongruity and negative affect during visual tasks.
Some neurodivergent people prioritize visual details over the “big picture”. While excellent attention to detail has many advantages, some contexts require the rapid integration of global and local information. A local processing style can be so strong that local details interfere with the fluid integration of global information required for processing of information rapidly displayed on user interfaces. This disconnect between context of an interaction and processing style can be termed local interference. Personalization of visual stimuli can promote a more accessible computing experience. We describe how technological interventions can support shifting of visual attention from local to global features to make them more accessible. We present two empirical studies. One study with one autistic adult revealed a significant shift in eye gaze fixation, and the other study with 20 autistic children revealed filters that visually emphasize primary aspects encouraged more global comments about image content.
Efficient processing of the visual environment necessitates the integration of incoming sensory evidence with concurrent contextual inputs and mnemonic content from our past experiences. To examine how this integration takes place in the brain, we isolated different types of feedback signals from the neural patterns of non-stimulated areas of the early visual cortex in humans (i.e., V1 and V2). Using multivariate pattern analysis, we showed that both contextual and time-distant information, coexist in V1 and V2 as feedback signals. In addition, we found that the extent to which mnemonic information is reinstated in V1 and V2 depends on whether the information is retrieved episodically or semantically. Critically, this reinstatement was independent on the retrieval route in the object-selective cortex. These results demonstrate that our early visual processing contains not just direct and indirect information from the visual surrounding, but also memory-based predictions.
Background: The environments that we live in impact on our ability to recognise objects, with recognition being facilitated when objects appear in expected locations (congruent) compared to unexpected locations (incongruent). However, these findings are based on experiments where the object is isolated from its environment. Moreover, it is not clear which components of the recognition process are impacted by the environment. In this experiment, we seek to examine the impact real world environments have on object recognition. Specifically, we will use mobile electroencephalography (mEEG) and augmented reality (AR) to investigate how the visual and semantic processing aspects of object recognition are changed by the environment. Methods: We will use AR to place congruent and incongruent virtual objects around indoor and outdoor environments. During the experiment a total of 34 participants will walk around the environments and find these objects while we record their eye movements and neural signals. We will perform two primary analyses. First, we will analyse the event-related potential (ERP) data using paired samples t-tests in the N300/400 time windows in an attempt to replicate congruency effects on the N300/400. Second, we will use representational similarity analysis (RSA) and computational models of vision and semantics to determine how visual and semantic processes are changed by congruency. Conclusions: Based on previous literature, we hypothesise that scene-object congruence would facilitate object recognition. For ERPs, we predict a congruency effect in the N300/N400, and for RSA we predict that higher level visual and semantic information will be represented earlier for congruent scenes than incongruent scenes. By collecting mEEG data while participants are exploring a real-world environment, we will be able to determine the impact of a natural context on object recognition, and the different processing stages of object recognition.
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The arrangement of objects in scenes follows certain rules (“Scene Grammar”), which we exploit to perceive and interact efficiently with our environment. We have proposed that Scene Grammar is hierarchically organized: scenes are divided into clusters of objects (“phrases”, e.g., the sink phrase); within every phrase, one object (“anchor”, e.g., the sink) holds strong predictions about identity and position of other objects (“local objects”, e.g., a toothbrush). To investigate if this hierarchy is reflected in the mental representations of objects, we collected pairwise similarity judgments for everyday object pictures and for the corresponding words. Similarity judgments were stronger not only for object pairs appearing in the same scene, but also object pairs appearing within the same phrase of the same scene as opposed to appearing in different phrases of the same scene. Besides, object pairs with the same status in the scenes (i.e., being both anchors or both local objects) were judged as more similar than pairs of different status. Comparing effects between pictures and words, we found similar, significant impact of scene hierarchy on the organization of mental representation of objects, independent of stimulus modality. We conclude that the hierarchical structure of visual environment is incorporated into abstract, domain general mental representations of the world.
Objective: On continuous recognition tasks, changing the context objects are embedded in impairs memory. Older adults are worse on pattern separation tasks requiring identification of similar objects compared to younger adults. However, how contexts impact pattern separation in aging is unclear. The apolipoprotein (APOE) ϵ4 allele may exacerbate possible age-related changes due to early, elevated neuropathology. The goal of this study is to determine how context and APOE status affect pattern separation among younger and older adults. Method: Older and younger ϵ4 carriers and noncarriers were given a continuous object recognition task. Participants indicated if objects on a Repeated White background, Repeated Scene, or a Novel Scene were old, similar, or new. The proportions of correct responses and the types of errors made were calculated. Results: Novel scenes lowered recognition scores compared to all other contexts for everyone. Younger adults outperformed older adults on identifying similar objects. Older adults misidentified similar objects as old more than new, and the repeated scene exacerbated this error. APOE status interacted with scene and age such that in repeated scenes, younger carriers produced less false alarms, and this trend switched for older adults where carriers made more false alarms. Conclusions: Context impacted recognition memory in the same way for both age groups. Older adults underutilized details and over relied on holistic information during pattern separation compared to younger adults. The triple interaction in false alarms may indicate an even greater reliance on holistic information among older adults with increased risk for Alzheimer's disease.
Prior studies have found that, despite the intentions of the participants, objects automatically activate their semantic representations; however, this research examined only objects presented in isolation without a background context. The present set of experiments examined the automaticity issue for objects presented in isolation as well as in scenes. In Experiments l and 2, words were categorized more slowly when they were embedded inside incongruent objects (e.g., the word chair in a picture of a duck) than inside neutral nonobjects, suggesting that the meanings of the objects were activated despite participants' intentions. A new interference task was introduced in Experiment 3. When the same objects and words from the first 2 experiments were inserted into scenes in which those objects were probable or improbable, interference occurred from probable pictured objects but not from improbable pictured objects. Implications for theories of automaticity and models of object identification are discussed.
Cryptomnesia, or inadvertent plagiarism, was experimentally examined in three investigations. Subjects were required to generate category exemplars, alternating with 3 other subjects in Experiments 1 and 2 or with a standardized, written list in Experiment 3. After this generation stage, subjects attempted to recall those items which they had just generated and an equal number of completely new items from each category. Plagiarism of others' generated responses occurred in all three tasks (generation, recall own, and recall new) in each experiment, despite instructions to avoid such intrusions. The amount of plagiarism was greater under more complex generation sequences and for items produced from orthorgraphic relative to semantic categories. The most likely source of plagiarized responses was the person who had responded just before the subject in the generation sequence. Directions for future research are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways it each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somato sensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainties in the reported assignment.