Perceiving visually presented objects: recognition, awareness, and modularity Anne M Treisman* and Nancy G Kanwisherf
Object perception may involve seeing, recognition,
preparation of actions, and emotional responses-functions
that human brain imaging and neuropsychology suggest are
localized separately. Perhaps because of this specialization,
object perception is remarkably rapid and efficient.
Representations of componential structure and interpolation
from view-dependent images both play a part in object
recognition. Unattended objects may be implicitly registered,
but recent experiments suggest that attention is required to
bind features, to represent three-dimensional structure, and to
Addresses *Department of Psychology, Princeton University, Princeton, New Jersey 08544-1010, USA; e-mail: firstname.lastname@example.org tDepartment of Brain and Cognitive Sciences, El O-243, Massachusetts Institute of Technology, Cambridge, Massachusetts 02138, USA; e-mail: email@example.com
Current Opinion in Neurobiology 1998, 8:218-226
0 Current Biology Ltd ISSN 0959-4388
ERP event-related potential fMRl functional magnetic resonance imaging IT inferotemporal cortex
Introduction It is usually assumed that perception is mediated by specific patterns of neural activity that encode a selective
description of what is seen, distinguishing it from other
similar sights. When we perceive an object, we may form
multiple representations, each specialized for a different
purpose and therefore selecting different properties to
encode at different levels of detail. There is empirical
evidence supporting the existence of six different types
of object representation. First, representation as an ‘object
token’-a conscious viewpoint-dependent representation
of the object as currently seen. Second, as a ‘structural de-
scription’- a non-visually-conscious object-centered rep-
resentation from which the object’s appearance from other
angles and distances can be predicted. Third, as an
‘object type’-a recognition of the object’s identity (e.g. a
banana) or membership in one or more stored categories.
Fourth, a representation based on further knowledge
associated with the category (such as the fact that the
banana can be peeled and what it will taste like). Fifth, a
representation that includes a specification of its emotional
and motivational significance to the observer. Sixth, an
‘action-centered description’, specifying its “affordances”
[l], that is, the properties we need in order to program
appropriate motor responses to it, such as its location,
size and shape relative to our hands. These different
representations are probably formed in an interactive
fashion, with prior knowledge facilitating the extraction of
likely features and structure, and vice versa.
Evidence suggests that the first four types of encoding
depend primarily on the ventral (occipitotemporal) path-
way, the fifth on connections to the amygdala, and the
sixth on the dorsal (occipitoparietal) pathway; however,
object tokens have also been equated with action-centered
descriptions [PI. Dorsal representations appear to be
distinct from those that mediate conscious perception;
for example, grasping is unaffected by the Titchener
size illusion . Emotional responses can also be evoked
without conscious recognition (e.g. see [4**]). Object
recognition models differ over whether the type or identity
of objects is accessed from the view-dependent token or
from a structural description; in some cases, it may also be
accessed directly from simpler features.
The goal of perception is to account for systematic
patterning of the retinal image, attributing features to their
real world sources in objects and in the current viewing
conditions. In order to achieve these representations,
multiple sources of information are used, such as color,
luminance, texture, relative size, dynamic cues from mo-
tion and transformations, and stereo depth; however, the
most important is typically shape. Many challenges arise in
solving the inverse problem of retrieving the likely source
of the retinal image: information about object boundaries
is often incomplete and noisy; and three-dimensional
objects are seen from multiple views, producing different
two-dimensional projections on the retina, and objects in
normal scenes are often partially occluded. The visual
system has developed many heuristics for solving these
problems. Continuity is assumed rather than random varia-
tion. Regularities in the image are attributed to regularities
in the real world rather than to accidental coincidences.
Different types of objects and different levels of specificity
require diverse discriminations, making it likely that
specialized modules have evolved, or developed through
learning, to cope with the particular demands of tasks
such as face recognition, reading, finding our way through
places, manipulating tools, and identifying animals, plants,
minerals and artifacts.
Research on object perception over the past year has made
progress on a number of issues. Here, we will discuss
recent advances in our understanding of the speed of
object recognition, object types and tokens, and attention
and awareness in object recognition. In addition, we will
Perceiving visually presented objects Treisman and Kanwisher 219
review evidence for cortical specializations for particular
components of visual recognition.
The speed of object recognition Evolutionary pressures have given high priority to speed
of visual recognition, and there is both psychological and
neuroscientific evidence that objects are discriminated
within one or two hundred milliseconds. Behavioral
studies have demonstrated that we can recognize up to
eight or more objects per second, provided they are
presented sequentially at fixation, making eye movements
unnecessary [S]. Although rate measurements cannot tell
us the absolute amount of time necessary for an individual
object to be recognized, physiological recordings reveal
the latency at which the two stimulus classes begin to
be distinguished. Thorpe et al. [6”] have demonstrated significant differences in event-related brain potential
(ERP) waveforms for viewing scenes containing animals
versus scenes not containing animals at 150 ms after stim-
ulus onset. Several other groups [7,8*,9-111 have found
face-specific ERPs and magnetoencephalography (MEG)
waveforms with latencies of 155-190 ms. DiGirolamo and
Kanwisher (G DiGirolamo, NG Kanwisher, abstract in
Psychonom Sot 1995, 305) found ERP differences for line drawings of familiar versus unfamiliar three-dimensional
objects at 170 ms (see also [S]).
Parallel results were found in the stimulus selectivity
of early responses of cells in inferotemporal (IT) cortex
in macaques, initiated at latencies of 80-looms. On
the basis that IT cells are selective for particular faces
even in the first 50ms of their response, Wallis and
Rolls  conclude that “visual recognition can occur
with largely feed-forward processing”. The duration of
responses by these face-selective cells was reduced from
250ms to 25 ms by a backward mask appearing 20ms
after the onset of the face, a stimulus onset asynchrony
at which human observers can still just recognize the
face. The data suggest that “a cortical area can perform
the computation necessary for the recognition of a visual
stimulus in ZO-30ms”. Thus, a consensus is developing
that the critical processes involved in object recognition
are remarkably fast, occurring within lOO-200ms of
stimulus presentation. However, it may take another
1OOms for subsequent processes to bring this information
Object tokens How then does the visual system solve the problems of
object perception with such impressive speed and accu-
racy? A first stage must be a preliminary segregation of the
sensory data that form separate candidate objects. Even
at this early level, familiarity can override bottom-up cues
such as common region and connectedness, supporting
an interactive cascade process in which “partial results of
the segmentation process are sent to higher level object
representations”, which, in turn, guide the segmentation
process [ 13.1.
Kahneman, Treisman, and Gibbs  have proposed
that conscious seeing is mediated by episodic ‘object
files’ within which the object tokens defined earlier
are constructed. Information about particular instances
currently being viewed is selected from the sensory
array, accumulates over time, and is ‘bound’ together in
structured relations. Evidence for this claim came partly
from the observation of ‘object-specific’ priming- that
is, priming that occurs only, or more strongly, when the
prime and probe are seen as a single object. This occurs
even when they appear in different locations, if the
object is seen in real or apparent motion between the
two. Object-specific priming occurs between pictures and
names when these are perceptually linked through the
frames in which they appear (RD Gordon, DE Irwin,
personal communication), suggesting that object files
accumulate information not only about sensory features
but also about more abstract identities. However, priming
between synonyms or semantic associates is not object
specific , that is, it occurs equally whether they
are presented in the same perceptual object or in
different objects. It appears that object files integrate
object representations with their names, but maintain
a distinct identity from other semantically associated
objects. Priming at this level would be between object
types rather than tokens. Irwin [ 161 has reviewed evidence on transsaccadic integration, suggesting that it is limited to
about four object files.
A similar distinction between tokens and types has
emerged from the study of repetition blindness, a failure
to see a second token of the same type, which was
attributed to refractoriness in attaching a new token to
a recently instantiated type . Recent research has
further explored this idea. One role of object tokens is
to maintain spatiotemporal continuity of objects across
motion and change. Chun and Cavanagh [18”] confirmed
that repetition blindness is greater when repeated items
are seen to occur within the same apparent motion
sequence and hence are integrated as the same perceived
object. They suggest that perception is biased to minimize
the number of different tokens formed to account for the
sensory data. Objects that appear successively are linked
whenever the spatial and temporal separations make
this physically plausible. This generally gives veridical
perception because in the real world, objects seldom
appear from nowhere or suddenly vanish. Arnell and
Jolicoeur [ 191 have demonstrated repetition blindness for novel objects for which no pre-existing representations
existed. According to Kanwisher’s account [ 171, this implies that a single presentation is sufficient to establish
an object type to which new tokens will be matched.
The ‘attentional blink’ [ZO] describes a failure to de-
tect the second of two different targets when it is
presented soon after the first. Chun (21’1 sees both
repetition blindness and the attentional blink as failures
of tokenization, although for different reasons, because
220 Cognitive neuroscience
they can be dissociated experimentally. Attentional blinks
(reduced by target-distractor discriminability) reflect a
Di I,ollo, JT Enns, personal communication). The account proposed
is that awareness depends on a match between re-entrant
information and the current sensory input at early
visual levels. A mismatch erases the initial tentative
representation. “It is as though the visual system treats the
trailing configuration as a transformation or replacement
of the earlier one.” Conversely, repetition blindness for
locations (R Epstein, NG Kanwisher, abstract in Psychononz
Sot 1996, 593) may result when the representation of an
earlier-presented letter prevents the stable encoding of
a subsequently presented letter appearing at the same
Attention and awareness in object perception Attention seems, then, to be necessary for object tokens
to mediate awareness. However, there is evidence (see
[Z-l’]) that objects can be identified without attention
and awareness. If this is so, do the representations differ
from those formed with attention? Activation (shown
by brain-imaging) in specialized regions of cortex for
processing faces  and visual motion  is reduced
when subjects direct attention away from the faces or
moving objects (respectively), even when eye movements
are controlled to guarantee identical retinal stimulation
(see also ), consistent with the effects of attention
on single units in macaque visual cortex. Unattended
objects are seldom reportable. However, priming studies
suggest that their shapes can be implicitly registered
[?.9,30**], although there are clear limits to the number of
unattended objects that will prime . Representations
formed without attention may differ from those that
receive attention: they appear to be viewpoint-dependent
[32’], two-dimensional, with no interpretation of occlusion
or amodal completion [30”]. On the other hand, in
clinical neglect, the ‘invisible’ representations formed in
a patient’s neglected field include illusory contours and
filled-in surfaces [33-l, suggesting that neglect arises at
stages of processing beyond those that are suppressed in
normal selective attention. With more extreme inattention,
little explicit information is available beyond simple
features such as location, color, size, and gross numerosity;
even these simple features may not be available, produc-
ing ‘inattentional blindness’ [34’]. Again, however, some
implicit information is registered: unseen words may prime
word fragment completion, and there is clear selectivity
for emotionally important objects such as the person’s own
name and happy (but not sad) faces.
Binding of features to objects is often inaccurate unless
attention is focused on the relevant locations .
Although the parietal lobes are usually thought to be
associated with the processing of space and of action, they
may also be intimately involved, through spatial attention,
in binding and individuating object tokens in displays
with more than one object present, and therefore in
allowing conscious access to normal scenes . Bilateral
damage to the parietal lobes results in Balint’s syndrome,
with its accompanying simultanagnosia (i.e. an inability
to see more than one object at a time) and dramatic
failures in binding features correctly. Binding is also
disrupted by transcranial magnetic stimulation of the
parietal lobes . Extinction following unilateral parietal
lesions may result from a similar attentional problem
[2’,38]; there is often evidence of implicit knowledge
of extinguished items, perhaps through direct access
from features to types. Individuating objects in ‘crowded’
displays is more difficult in the lower than upper visual
field [39**], consistent with the greater parietal projection
from the lower visual field.
Other studies have investigated what is perceived with
attention distributed globally rather than specifically
excluding the critical object. Global attention allows
amodal completion for homogeneous displays . Studies
of visual search suggest that displays are automatically
parsed into preattentive object files, acting as holders
for collections of attributes but not for their structural
relations (with the exception of the part-whole relation;
[41*]). Wolfe  has collected surprising evidence that
previously attended object tokens revert to a similar
unstructured state once attention is withdrawn, concluding
that “Vision exists in the present tense. It remembers
nothing”. Experiments on change detection in natural
scenes show that focused, rather than global, attention
is necessary for the identification of even quite dramatic
changes between saccades (; RD Gordon, DE Irwin,
personal communication) or between alternating versions
of a scene with one object changed, added, or deleted
[44,45”,46]. Thus, attention seems critical at least for the
explicit voluntary storage and retrieval of objects.
Perceiving visually presented objects Treisman and Kanwisher 221
Striking dissociations between conscious access and im-
plicit measures of object processing are found in patients
with localized brain injuries. These dissociations suggest
multiple systems, each forming representations of objects
for specific purposes, only some of them conscious. For
example, damage to the fusiform gyrus results in loss
of conscious face recognition, or prosopagnosia, whereas
emotional assessment depends on the amygdala, and
may be selectively impaired in Capgras syndrome, where
patients show normal face recognition but no emotional
skin conductance responses . Conversely, functional
magnetic resonance imaging (fR/IRI) activation of the
amygdala for emotionally expressive faces compared to
neutral ones occurs even when the emotional expres-
sions are masked and unseen . Separate pathways
may be responsible for conscious perception of objects
and for the object representations chat control actions,
including the metric information necessary for grasping
and manipulating . For example, patient D.F. has severe
agnosia as a result of damage in ventral visual areas,
but can still manipulate objects appropriately, presumably
through an intact dorsal route. Survival of action-related
object coding has also been shown by a hemianopic
patient in his blind field . Another patient, with
damage in the ventral route, shows a striking dissociation
in expressing his perceptual knowledge, interpreting a
picture of a clarinet verbally as “Perhaps a pencil” while at
the same time his fingers clearly mimic playing a clarinet
(D Margolin et al., abstract in J Clin Exp Neuropsychol 1985, 6). Recent findings with patient D.F. suggest,
however, that shape processing in the dorsal route may be
restricted to measures of orientation, size and motion .
Positron emission tomography (PET) studies have also
failed to find the sharp dissociation between areas involved
in grasping and in perceptual matching that would be
predicted [SO] for a complete segregation of perceptual and
Object types Formal theories of object perception have dealt primarily
with object recognition-that is, the identification of
object types, rather than the formation of object tokens.
‘l’hey fall into two classes: those that base recognition
on a structural description specifying parts and their re-
lationships (e.g. see [Sl]), and those that use more holistic
viewpoint-dependent representations [SZ-551. Structural
descriptions specify the relations between volumetric parts
or ‘gcons’ (e.g. ‘above’. ‘smaller than’, or ‘perpendicular
to’), which, in turn, are defined by features signaling
their cross section, axis shape, rough aspect ratio and
whether they arc truncated. View-dependent models
differ in how they solve the recognition problem for
novel views, whether by interpolation between stored
views , by ‘blurred’ template-matching [55,57], by
linear combination:; of stored views , or by mental
The debate between those supporting the ‘structural
descriptions’ model versus those supporting the view-
dependent models continued over the past year; recent
evidence suggests that both accounts play a role and
clarifies the conditions in which each may be used. View-
based representations predict the observed specificity
of learning, with gradients of generalization around the
particular views experienced [60’], even when the objects
were novel and clearly composed of geons. Learned views
were shown also to influence the appearance of an object
in motion, determining whether or not it was seen as
rigid [61*]. Apparent motion between rotated views of
novel objects demonstrated the psychological reality of
an induced interpolation process [62”]: both intermediate
views and views just beyond the second view were
primed, but not views that preceded the first. Priming was
abolished when the interval between the two views was
too long to induce apparent motion.
Outside the laboratory, we normally experience dynami-
cally changing views of objects, through either our own
motion or the motion of the object. This could be
an important perceptual learning mechanism in object
recognition. Physiological evidence consistent with the
view-based account comes from single-unit recordings
in IT of macaque monkeys , showing neurons that
respond selectively to different views of novel objects,
firing most to one view, with a gradually decreasing
response as the object rotates away from the preferred
view. The results closely parallel the generalization
gradients shown in human priming experiments. Only
a few cells were found to respond selectively to one
object regardless of the view from which it was seen.
The existence of IT columns systematically coding similar
object components  may contribute to perceived
invariance across different views and locations of the same
The geon-based account has also received considerable
empirical support (reviewed in ). Its proponents have
shown that simple filters cannot account for the types
of errors that humans make . In recent applied
research on distinguishing military vehicles in infra-red
photos , a geon-based conditional tree predicted
perceptual confusions much better than a deformable
template account (671, although the latter did better
with faces. Identification can be dissociated from the
conscious perception of orientation: two studies have
reported that three patients with right or bilateral parietal
lesions correctly identified objects or letters without being
able to name or copy their orientations [39**,68].
Studies comparing priming and recognition also sug-
gest that both structural descriptions and more specific
viewpoint-dependent representations are retained in vi-
sual memory. Whereas implicit priming suggests invari-
ance across changes in location, color, orientation and size,
222 Cognitive neuroscience
explicit tests of recognition show much more specificity
[69,70]. Srinivas  confirmed that for attended objects,
priming was invariant with left-right orientation, although
it was reduced by changes in size if the task made size
relevant. Short-term matching of temporally contiguous
stimuli suggested equivalence across views and seems,
like priming, to tap an invariant representation .
Similarly, repetition blindness for pictures across very short
lags shows complete invariance to size, orientation, and
The general conclusion is emerging that both mechanisms
are used at different stages of processing, and/or on
different classes of objects . A recent model of object
perception [75*] combines an initial view-dependent
representation of geons followed by a ‘dynamic binding’
process that creates a structural description of their
relations while retaining their independence as separable
parts. Distinctive features or parts contribute when they
are present, ruling out a pure template-matching mech-
anism . Structural descriptions based on geons may
be good for accessing basic level categories for the many
objects that are naturally decomposable into distinct parts,
but cannot succeed for discriminations within classes of
objects that share parts and differ only in metric properties.
Faces are a clear case where more holistic template models
can capture subtle differences between instances, all of
which share the same basic geon structure. The task
may also play a part in determining the kind of analysis
that is carried out; in speeded naming, subtle differences
within categories are irrelevant, whereas in same-different
matching tasks, metric comparison processes may be
invoked. Finally, there may also be a shift with experience.
Experts with extensive encounters with different instances
may base their recognition on matching to multiple stored
views, giving the impression of invariant representation.
Gauthier and Tarr  gave subjects prolonged training
in recognizing novel objects with shared parts (‘greebles’)
varying along a few specified dimensions, and found
that with experience, they became sensitive to configural
qualities as well as to specific features.
Striking examples of perceptual plasticity in form per-
ception have recently been reported. Implicit traces
can mediate priming for novel nonsense shapes across
several weeks delay after a single presentation [29,30”].
Analogously, rapid learning has been demonstrated in
single-unit recordings in monkeys [78**]: when exposed
to binarized faces, face-sensitive cells gave little response,
but after the animal was given a few seconds of viewing
gray-scale versions of the same faces, the cells responded
equally to the binarized images. A similar result has been
shown in humans using fMR1 . Logothetis and Pauls
[SO] found IT cells that, with experience, became selective
for novel objects that previously did not excite them;
these cells also showed some viewpoint dependency.
Other examples of very rapid perceptual learning have
been reported [81,82], and a reverse hierarchical system,
to account for perceptual learning effects, has been
Cortical specializations for visual recognition Evidence from neuropsychology, cognitive psychology,
and brain imaging suggests that the remarkable speed
and accuracy of visual recognition are achieved through
the operation of a set of special-purpose mechanisms
instantiated in at least partially segregated brain regions.
The shape of an object is usually the most important
cue to its identity. Humphrey et al.  have reported
that although patient D.E could discriminate the ap-
parent three-dimensional structure of shapes defined by
shading gradients, she was unable to discriminate similar
shapes in which the edges were depicted as luminance
discontinuities or lines, suggesting that extracting shape
from shading is a distinct process from extracting shape
from edges. Humphrey et al.  used fMR1 on nor-
mal subjects to show that shape-from-shading processes
produce activation in primary visual cortex. Evidence
from a variety of sources indicates that a large region of
lateral occipital cortex just anterior to retinotopic cortex
(but posterior to the visual motion area MT) responds
more strongly to stimuli depicting shapes than to stimuli
with similar low-level features that do not depict shapes
[B&86]. Common areas within this lateral occipital region
are activated by structure from motion, structure from
texture, and luminance silhouettes (K Grill-Spector it
al., Sot Neurosci Abstr 1997, 23:868.12). Whereas simple forms defined by differences in luminance, color, or
direction of motion largely activate regions in retinotopic
cortex, stereoscopic and illusory-contour displays primarily
activate the lateral occipital region (J Mendola et al., Sor Neurosci Abstr 1997, 23550.11). Thus, although some of the necessary computations take place in retinotopic cortex,
lateral occipital cortex may contain regions specialized
for some aspect of visual shape analysis. However, three
important questions remain to be answered. First, what
specific aspect of shape analysis is computed in this region
(e.g. edge extraction or figure-ground segmentation or
implied depth)? Second, would the areas activated by
different shape cues in different studies overlap exactly
if run on an individual subject, or would different but
adjacent regions within lateral occipital cortex be activated
by different shape cues? Third, might the activations,
in part, reflect attentional artifacts, as all of the stimuli
depicting shapes are likely to be more attention-capturing
than the control stimuli depicting random texture fields?
Shape analysis can be carried out on virtually any visually
presented object. Other processing mechanisms appear
to be recruited by exemplars of just one stimulus class.
Evidence has been presented for special-purpose cortical
machinery for the recognition of words, tools, biological
motion [87,88], and other object categories. In the past
year, the already strong evidence for the case of face
perception  has received further support. First, a recent
Perceiving visually presented objects Treisman and Kanwisher 223
study of patient C.K. [90”] presents perhaps the most
compelling evidence that face and object recognition are
separated at a relatively early stage. C.K.‘s general visual
abilities are drastically disrupted, and he has great diffi-
culty recognizing objects and words, yet he is absolutely
normal at face recognition. Second, intracranial recordings
from epileptic patients have demonstrated single cells
in the human hippocampus, amygdala, and entorhinal
cortex that respond selectively to faces, particular facial
expressions, or gender , or to familiar versus unfamiliar
faces [91,92]. Third, human brain imaging studies have
shown that regions within the fusiform gyrus are not only
responsive to faces [93-951, but also respond in a highly
specific fashion to faces compared to a wide range of other
kinds of objects [96’,97].
The accumulating evidence for cortical specialization
for specific components of visual recognition raises a
number of important questions. Does this fine-grained
specialization of function arise from experience-dependent
self-organizing properties of cortex , or are cortical
specializations innately specified? For the case of faces,
this question is hard to answer because both experiential
and evolutionary arguments are plausible. However,
evidence for cortical specializations for visually presented
words (T Polk et (I/., Sot Newosci Abstr 1996, 22:291.2) and letters (M Farah et al., Sot Neurosci Abstr 1996, 22:291.1) suggests that experience may be sufficient, at least in some
cases. Further evidence for experience-induced cortical
specialization comes from Logothetis and Pauls , who
found that after training monkeys with a specific class of
stimuli, small regions in anterior IT (AIT) contained cells
selectively responsive to these stimuli.
What are the implications of cortical specialization for
theories of visual recognition? Does the selectivity of
certain cortical areas for the recognition of different
stimulus classes imply that qualitatively distinct processing
mechanisms are involved in each? Connectionist re-
searchers have noted the computational efficiency gained
by the decomposition of a complex function into natural
parts . Cortical specializations for components of visual
recognition are plausible candidates for such task decom-
position. On the other hand, a shallower account might
argue that cells selective for particular specialized features
happen to land together in a cortical surface organized
by feature columns [lOO]. Support for this interpretation
comes from a recent report that localized regions in human
extrastriate cortex are selectively responsive to apparently
arbitrary categories, such as chairs and houses (A Ishai
et a/., abstract in Neuroimage 1997, 5.4:S149). It remains for future research to determine whether the functional
organization of visual recognition is better characterized
as ‘shallow specialization’ or a deeper form of modularity
in which a small number of functionally specific regions
each carries out a qualitatively distinct computation in the
service of an evolutionarily or experientially fundamental
Conclusions Behavioral and physiological work has provided a rich
characterization of the multiple representations that are
extracted in the first quarter of a second of viewing
a complex visual stimulus. Both structural descriptions
and viewpoint-dependent representations sufficient for
discriminating between objects are extracted within about
200ms. The phenomena of repetition blindness, at-
tentional blink, attentional masking, and inattentional
blindness reveal some of the heuristics by which the
visual system decides which of these representations to
incorporate into the developing stable representation of
visual experience. Functional imaging and patient studies
complement this picture by revealing some of the funda-
mental components of the machinery of visual recognition.
Persuasive evidence exists for a special-purpose ‘module’
mediating face perception, and ongoing research suggests
the existence of several other dissociable components of
Acknowledgements this rcvicw supported by National Science Foundation
grant #SBR-9511633 to AM ‘licisman, and a Human Frontiers Grant and National Institute of hfentnl Health grant 56037 to NG Kanwisher.
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