Testing Category-Specific Spatial Frequency Adaptation

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17 Νοε 2013 (πριν από 3 χρόνια και 11 μήνες)

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Testing Category
-
Specific Spatial Frequency Adaptation





Kara B. Grubb





Honors
Thesis

completed in fulfillment of the requirements of the
Honors Program in Psychological Sciences






Under the direction of Dr. Isabel Gauthier, PhD and Olivia Cheung
,
Graduate Student







Vanderbilt University





April, 2009

Abstract


2


This study examined representational overlap between faces and scenes by means o
f spatial
frequency adaptation.
The results show that adaptation to faces and scenes in either low or h
igh
spatial frequencies affect the subsequent perception of a face hybrid or a scene hybrid (consisting
of low and high spatial frequencies) in different ways: for face hybrids, the spatial frequency of the
adaptor is more important, while low spatial freq
uency faces and high spatial frequency scenes
cause more perception of low spatial freq
uency scenes for scene hybrids.
This research provides
insight for using spatial frequency adaptation to explore the overlap between face processing and
other object pro
cessing, specifically processing for objects of expertise.
















For the last few decades, face processing has been a major area of interest in psychological
research. In the beginning, researchers focused on whether or not faces are processed di
fferently

3

than other objects and sought behavioral measures to quantify the differences; through these
efforts, the face inversion effect and holistic processing for faces were discovered (Davidoff &
Donnelley, 1990; Tanaka & Farah, 1993; Valentine, 1988;
Yin 1969; Young, Hellawell, & Hay, 1987;
Yovel, Paller, & Levy, 2005). Further research and subsequent advances in brain imaging
technology led to the discovery of the fusiform face area, the FFA, which is an area on the fusiform
gyrus that shows significa
nt activation for faces compared to the activation for non
-
face objects
such as houses, hands, and schematic faces (Kanwisher, McCermott, & Chun, 1997; Tong,
Mascovitch, Weinrib, & Kanwisher, 2000). The mounting evidence that faces are processed
differentl
y than most other objects led researchers to hypothesize why faces might be special in the
human brain. Two main hypotheses rule the field: the domain
-
specific hypothesis (McKone,
Kanwisher, & Duchaine, 2006; Kanwisher, 2000) and the expertise hypothesis (
Diamond & Carey,
1986; Gauthier & Logethetis, 2000; Gauthier, Skudlarski, Gore, & Anderson, 2000). Because of the
disagreement between proponents of each hypothesis (McKone et al., 2006; Gauthier & Bukach,
2007), researchers are searching for novel ways in

which to test the validity of the hypotheses.

I will first review the literature that suggests that faces are processed differently than other
objects as well as outline the current standing in the domain
-
specific versus expertise hypotheses.
One of the
major differences noted between the processing of faces and other objects is the effect of
inversion. When participants are asked to recognize previously studied faces and objects, the
participant’s accuracy is higher and reaction time is faster for faces
than for objects (Yin, 1969;
Valentine, 1988). However, when participants are asked to recognize previously studied faces and
other objects that are presented upside
-
down during the test phase, the participants have a much
lower accuracy and a much longer
response time for inverted faces than for inverted objects (Yin,
1969;
Kanwisher, Tong, & Nakayama, 1998;

Valentine, 1988). Yin proposed that this difference of
inversion effects between faces and objects might be because humans may use a special mechanism

for processing faces such that the mechanism is greatly affected by being shown inverted faces; the

4

mechanisms for processing other objects are not so affected by inversion, explaining why the
participants were worse at recognizing the inverted faces than

the inverted objects. The face
inversion effect has been found in numerous studies with many different procedures and materials
used (Valentine, 1988), suggesting that humans have some different or special mechanism for
processing faces than for processin
g objects. Another difference that has been found between face
processing and object processing is holistic processing, which means that an object is processed as
a whole rather than piece by piece. Faces are processed holistically, meaning that people hav
e
trouble attending to and processing just one portion of a face while ignoring the rest; this
phenomenon is not shown to be as robust for other objects as for faces (
Tanaka & Farah, 1993
;
Davidoff & Donnelley, 1990; Young et al., 1987; Yovel et al., 2005)
.


In the early 1990s, when imaging became more widely used in research, researchers began
to try to locate the specific neural substrates that are responsible for or that show preference for
face processing. Kanwisher et al. (1997) found an area on the f
usiform gyrus that showed
significant activation for faces and named it the fusiform face area, or FFA. This area showed
selective activation for upright faces but not for other objects, inverted faces, or scrambled faces.
Prosopagnosic patients, who cann
ot recognize faces because of damage to the FFA and surrounding
areas, support these neuroimaging and behavioral data (
Farah, Wilson, Drain, & Tanaka, 1995
).

FMRI studies and behavioral studies show that faces may be processed in a different way
than most
other objects (Davidoff & Donnelley, 1990; Farah et al., 1995; Kanwisher et al., 1997;
Kanwisher et al., 1998; Tanaka & Farah, 1993; Tong et al., 2000; Yin, 1969; Young et al., 1987; Yovel
et al., 2005). The prominent two hypotheses to explain why human br
ains might process faces in a
different way than most other objects are the expertise hypothesis and the domain
-
specific
hypothesis. The expertise hypothesis states that faces are processed in a different way because
humans are face “experts” (humans are e
xposed to them multiple times a day and much of human
life depends on recognizing
and interpreting human faces).
The domain
-
specific hypothesis

5

postulates that there is a neural substrate that processes human faces in a different area of the
brain and in a

different nature than other objects. Proponents of the domain
-
specific hypothesis
claim that if the expertise hypothesis were true, experts in some category would show similar
processing effects (FFA activation, holistic processing, inversion effects, etc
) for their objects of
expertise and for faces. Kanwisher (2000) and McKone et al. (2006) both claim that no
neuroimaging or behavioral effects have been found that demonstrate an expertise effect.


A main critique of studies supporting the domain
-
specifi
c hypothesis is that the studies fail
to fully differentiate between basic level and subordinate level of categorization (
See
Gauthier, et al.,
1997
for a review)
. Novices exhibit a basic level of categorization (i.e. to a novice, a bird is a bird),
wherea
s experts exhibit subordinate levels of categorization (i.e. to an expert, a bird is an eagle, or a
sparrow). Therefore, simply having plenty of hours or days or even years worth of exposure to a
certain category does not make someone an expert; a person n
eeds to exhibit subordinate level
processing as quickly as basic level processing in that category along with repeated exposure in
order to be classified as an expert (Gauthier et al., 2000b; Gauthier et al., 1998; Tanaka & Taylor,
1991). According to this

criterion, humans process faces differently than other objects because
humans are face experts, but with repeated exposure and the acquired ability to categorize at the
subordinate level, humans can become experts with other objects as well (Gauthier & Ta
rr, 1997;
Gauthier et al., 2000b; Tanaka & Taylor, 1991); similar processing effects would be acquired for
faces and objects of expertise.


In accordance with the expertise hypothesis, Gauthier et al.,
(2000a) found that bird and car
experts show signific
ant activation in the FFA and other known face areas when looking at their
objects of expertise. Also, when subjects were trained to become experts of lab
-
created novel
objects called Greebles (Gauthier & Tarr, 1997; Gauthier et al., 1998), they showed a s
ignificant
increase in activation in the FFA from novices (
Gauthier, Tarr, Anderson, Skudlarski, & Gore, 1999).
Behaviorally, many processing effects of expertise have been found that are similar to processing

6

effects for faces (Diamond & Carey, 1986; Gaut
hier & Tarr, 1997; Gauthier et al., 1999; Gauthier et
al., 1998). Diamond and Carey were among the first to demonstrate these effects; they found that
dog experts (judges in dog shows) showed significant inversion effects as compared to novices
when shown
inverted pictures of dogs. Similarly, when Gauthier et al. and Gauthier and Tarr
trained people to become Greeble experts, the experts subsequently showed inversion effects when
shown Greebles. Also, the Greeble experts showed signs of holistic processing
for the Greebles
(Gauthier et al., 1998; Gauthier & Tarr, 1997).
Because of the lack of conclusive evidence for either
hypothesis (
McKone, Kanwisher, & Duchaine, 2006; Gauthier & Bukach, 2007)
, researchers are
looking for other ways besides the traditional

behavioral and fMRI studies to investigate why faces
are special, and to look at the overlap between face processing and other object processing,
specifically, objects of expertise processing.


The current study examined the functional overlap between f
ace processing and scene
processing by using spatial frequency adaptation. The aim of this study is to demonstrate that
adaptation to one category of stimuli, such as faces, should not affect perception of another
category, such as scenes, that have been s
hown to have dissociated processing systems. If we can
show this to be true, subsequent research can be done using faces and objects of expertise, such as
cars. In these future studies, any cross
-
adaptation effects will suggest overlaps between face and ca
r
processing, which provides support for the expertise hypothesis.


Adaptation in visual processing can be used when any category of visual information has
different components that are s
eparated in the visual system.
Many low
-
level adaptation effects
have

been shown, such as the red
-
green phenomenon in color perception, or the tilt effect, or most
other aftereffects in which a person adapts to one thing, resulting in subsequent perception
becoming repelled or biased away from the adapting stimulus.

Adaptat
ion effects like this can occur
when there are neurons in the visual system that respond to the same category of stimuli, but
respond to diffe
rent aspects of that category.
For example, the red
-
green phenomenon is fairly well
-

7

known, and involves a subject
looking at a red screen for a certain amount of time, and then looking
at a white screen and seeing a green tint
.
This happens because neurons that respond to the color
green are closely linked to neurons that respond to the color red in the visual system,

and fatiguing
the neurons that respond to red by looking at the red screen results in perception of the green
(Thompson & Burr, 2009).


Higher
-
level visual adaptations have been demonstrated for things like face identity or
category judgment (
Kovacs et
al., 2006; Sigala & Rainer, 2006), implying that adaptation can be
used to study the nature of the higher
-
level visual system. Adaptation studies using faces have
already been done for direction of gaze (Calder et al., 2007), gender, race, emotion (Webster
,
Kaping, Mizokami, & Duhamel, 2004), and attractiveness (Rhodes et al., 2003). While these studies
do provide important information about the visual system, these traditional adaptation studies
cannot be used to test face processing in comparison with oth
er object processing because faces
and other objects have very different shapes and characteristics, and therefore no studies like the
above mentioned can be done for faces and cars, for instance. For this reason, other facets of the
visual information tha
t comprise both faces and other objects need to be used in adaptation studies.


One such facet that could be used in higher
-
level adaptation studies is the separation of high
and low spatial frequencies throughout the visual system to the FFA. High spatia
l frequencies look
like outlines and contain fine scale information useful for judgments such as age, wrinkles, and the
detailed information of the face; low spatial frequencies look like blurred images and contain coarse
information that makes up the gene
ral shape of the face [Figure 3] (Schyns & Oliva, 1999; Gauthier,
Curby, Skudlarski, & Epstein, 2005)
.
Spatial frequency studies were conducted to explore how these
different spa
tial frequencies are used in

visual processing, and have shown that different
ranges of
spatial frequencies are important to the visual processing of faces during different categorization
judgments (gender, expression identification).
Schyns & Oliva (1999) examined which range of
spatial frequencies are used for gender discriminatio
ns versus expression discriminations, and they

8

researched how spatial frequencies are used in scene processing (Schyns & Oliva, 1994); their
research shows that spatial frequencies, a low
-
level property, can be used to research high
-
level
categorical effec
ts for faces and scenes.

The current experiment uses spatial frequency adaptation to examine whether the
adaptation effects can be obtained between different objects within a category but not between
obje
cts from different categories.
In particular, we use
d faces and scenes, two types of stimuli that
recruit distinct visual areas (the FFA for faces and the
parahippocampal place area, the
PPA for
scenes,
Epstein, Harris, Stanley, & Kanwisher, 1999).
Gauthier,
Curby, Skudlarski, & Epstein (2005)
showed that f
aces and cars in car experts in high vs. low spatial frequency ranges independently
activate neurons in the FFA. Because of the separation of high and low spatial frequencies in the
FFA, we predict that adaptation to faces in a particular spatial frequency

range would lead to an
aftereffect where other faces will be more easily perceived in other ranges of spatial frequencies.
This experiment explores the effects of spatial frequency adaptation to faces and scenes with the
presumption that adaptation to a p
articular spatial frequency of faces should not affect scene
perception and that adaptation to a particular spatial frequency for scenes should not affect face
perception. That is, if a person is adapted to a certain spatial frequency for faces (i.e. adapt
ed to low
spatial frequency faces), then that person’s perception of scenes should not be affected (and vice
versa). This is because any fatigue to the neural network that processes faces should not fatigue or
even recruit the neural network that processes

scenes, as the two substrates have been dissociated
(Epstein et al., 1999). Thus, adaptation effects of spatial frequencies may be used to reveal the
functional differences between face and scene processing, and eventually, the functional overlap
between
face processing and object of expertise processing. This research is important not only
because it could possibly lead to a way to investigate the expertise versus the domain
-
specific
hypotheses, but also because it can help illuminate the way that faces a
re processed in general and
can specifically help us understand how spatial frequencies are used in visual processing.


9

After this initial research, subsequent research on spatial frequency adaptation to faces and
objects of expertise, such as cars, can b
e done with the assumption that cross
-
category adaptation
effects would be found in car experts. That is, a car expert adapted to a particular spatial frequency
for cars should show similar adaptation effects for both cars and faces, while a car novice ada
pted
to the same spatial frequency for cars should not show any cross
-
adaptation effects for faces. If the
data support these hypotheses, the expertise hypothesis will also be supported.

In this experiment, the participants were adapted to either faces or

scenes in either low or
high spatial frequencies. Because no research has been done on spatial frequencies in the PPA, and
because this is the first study of its kind, specific predictions are hard to make. I did posit, however,
that ideal results would h
ave shown that perception of one category of stimuli was not influenced
by adaptation in the other category of stimuli. That is, the ideal expected results would have shown
evidence of adaptation effects with no cross
-
category effects.

Methods

Participan
ts.
Fifty students at Vanderbilt University with normal or corrected to normal
vision were recruited for this experiment; because of data problems (subjects using the wrong keys
or incomplete data), 4 participants’ data was excluded, resulting in 46 partic
ipating. Participants
were given credit for psychology courses that require experiment participation or were paid for
their participation. The participants were divided into 4 groups of 11 or 12, and each group was
adapted to a different spatial frequency
and a different category of stimuli, resulting in
a 2x2
between
-
subjects design.
The groups were as follows: Group 1
-

adapted to high spatial frequency
faces; Group 2
-

adapted to low spatial frequency faces; Group 3
-

adapted to high spatial frequency
sc
enes; Group 4
-

adapted to low spatial frequency scenes.

Stimuli.
The stimuli were taken from 80 color photographs of adult male and female faces
and 80 photographs of scenes. Forty faces and forty scenes were used to create the adapting
stimuli, and the
remaining forty faces and forty scenes were used to create the test stimuli. The

10

faces were front views with happy and disgusted expressions (40 of each); they were taken from
the Karolinska face database. The particular faces were chosen using a validatio
n study that tested
the effectiveness of the faces in the Karolinska
database in representing to
participants the emotion
that they were meant to represent (Goeleven, Raedt, Leyman, & Verschuere, 2008). The scenes
were black
-
and
-
white of two catego
ries: ho
use and highway

scenes. The faces and scenes were
converted into grayscale using Adobe Photoshop CS
2. The canvas sizes of the
photographs were
trimmed down to 160x160 pixels also using Adobe Photoshop CS2.


Adapting Stimuli.
The face adapting stimuli came

from 20 happy photographs and 20
disgust photographs of different individuals. The scene adapting stimuli came from 20 house
photographs and 20 highway photographs. The 40 adapting faces and 40 adapting scenes were
filtered into both low and high spatial
frequencies and were used as adapting stimuli as both spatial
frequencies. For example, a picture of a happy face was filtered into both low and high spatial
frequencies, resulting in 2 different stimuli. Low spatial frequencies were defined as <8 cycles p
er
image, and high spatial frequencies were defined as >32 cycles per image, which is consistent with
previous studies using spatial frequency filtering (Gauthier, Curby, Skudlarski, & Epstein, 2005;
Schyns & Oliva, 1999). After filtering, there were 80 st
imuli faces (40 high spatial frequency and 40
low spatial frequency) and 80 stimuli scenes (40 high spatial frequency and 40 low spatial
frequency). Each participant was only adapted to those denoted by the group assignments and each
participant saw all of

the adapting stimuli for his/her group such that each participant saw 40
adapting stimuli during the adaptation phase.


Test Stimuli.
The face test stimuli came from the happy and disgust photographs of
20 individuals. The happy and disgusted faces were
filtered into both high and low spatial
frequencies (using the same criterion for high and low spatial frequencies as above) and happy and
disgusted faces were matched to create hybrids with the same person of the opposite expression
and opposite spatial f
requency. For example, there was a photograph of Female 1 with a disgusted

11

face and another photograph of Female 1 with a happy face. Both the happy and the disgusted face
were filtered into high and low spatial frequencies and then the low spatial frequen
cy of one
emotion was combined into a hybrid with the high spatial frequency of another emotion. This
resulted in 40 face hybrids to be used during the testing phase. Similarly, the scene hy
brids were
composed of a house

scene of either high or low spatia
l

frequency paired with a highway

scene of
the opposite spatial frequency. Each test scene picture was filtered into both high and low spatial
frequencies (using the same criterion for high and low spatial frequencies as above) so that each
test scene pictu
re was in 2 different hybrids: one in which it holds the high spatial frequency
information and one in which it holds the low spatial frequency information. This resulted in 40
scene hybrids that were used during the testing phase. None of the photographs
used to make the
adapting stimuli were used to make the test stimuli, and vice versa.

Procedures.
This experiment asked participants to make judgments about which category of
the types of stimuli they perceived when they were shown the hybrids. For faces,
the participants’
responses were either “positive” or “negative”, and for scenes, their responses were either “house”
or “highway”. The study had a pre
-
adaptation and a post
-
adaptation test phase in order to
demonstrate the effects of adaptation to differe
nt spatial frequencies for scenes and faces and an
originals phase and a practice phase to give the subjects some exposure to the stimuli.


Originals phase.
In the originals phase, the participants were shown 10 examples of
each of the types of stimuli to
familiarize them with the types of pictures that they would be looking
at. They were shown positive faces (happy), nega
tive faces (disgusted), houses, and highways

[Figure 1]. The pictures during this part of the experiment had not been filtered into diffe
rent
spatial frequencies. The participants were not asked to make judgments during this phase, only to
pay attention because they would be asked to categorize the faces and scenes in the same way at a
later point in the experiment.


12


Practice phase.
In the
practice phase, the participants were given a chance to
practice classifying the faces and scenes as either positive, negative, house, or highway. During this
phase, the participants looked at 2 examples of the adapting stimuli from each category and were
asked to make judgments and to classify the pictures according to what they had seen in the
originals phase. The purpose of this phase was to familiarize the participants with the stimuli, to
make sure that the participants could differentiate between the
different types of stimuli, and to
give the participants practice with switching keys from the faces to the scenes.


Pre
-
adaptation phase.
In the pre
-
adaptation phase, participants were shown the face
and scene hybrids [Figure 2] and were asked to judge if

they saw a positive or negative emotion for
faces or a house or a highway for scenes; this was done to obtain a pre
-
adaptation baseline for each
participant. All participants were shown all of the test stimuli regardless of which group they were
in. A fix
ation point was shown on the screen at the beginning of each trial followed by a test hybrid,
either a face hybrid or a scene hybrid. The test hybrid was shown for 50 ms and then participants
were given an indefin
ite amount of time to respond.
Participants

were shown all 40 face hybrids
and all 40 scene hybrids. After each test hybrid, the participants pressed keys to indicate what they
saw.


Adaptation phase.
The adaptation phase differed for each group of participants in
that each participant was adapted

to stimuli according to his or her group. The participants were
shown all adapting stimuli for their group [Figure 3]. The adapting stimuli was presented in a
random order for 2 s each, with a 200 ms interval between the stimuli. Participants were not ask
ed
to make judgments or press any keys during this time, but were asked to pay careful attention to
the stimuli.


Test and re
-
adaptation phase.
The test and re
-
adaption phase was similar to the pre
-
adaptation phase but also included 6 seconds of re
-
adapt
ation before each trial.
Participants were
asked to initiate each trial by pressing a key. At the beginning of each trial, the participants were

13

shown 4 re
-
adapting stimuli (presented for the same duration and with the same between
-
stimuli
duration as duri
ng the adaptation phase); the re
-
adapting stimuli were chosen randomly according
to the participant’s group. After the re
-
adapting stimuli were presented, the participants saw a
fixation point followed by a test hybrid. The participants were then asked to
make a judgment on
what they saw, using the same keys that were used during the pre
-
adaptation phase. Each trial
contained one cycle of re
-
adapting stimuli and a test stimulus. The test stimuli were chosen
randomly from the test hybrids and
each
was presen
ted for 50ms. Participants were shown all 40
face hybrids and all 40 scene hybrids during the test and re
-
adaptation phase.

Results

Data Analysis.
Each participant’s post
-
adaptation trials were compared to his/her pre
-
adaptation trials to obtain the amou
nt of change because of adaptation. Each participant’s data
were analyzed in terms of the rate of change of low spatial frequency from pre
-
test to post
-
test for
both the category that the participant was adapted to and the category that the participant was

not
adapted to. The ideal pre
-
test values would have been if participants saw 50% high spatial
frequency and 50% low spatial frequency for both faces and scenes, and if participants saw 20
positive faces, 20 negative faces, 20 scenes, and 20 highways. The
se values were ideal because they
would have ensured that any changes from pre
-
test to post
-
test were actually effects of adaptation
and not just regression to the mean.

Results.

The average pre
-
test values are very close to the 50
-
50% ideal range: for low

spatial
frequency faces: 57.826% (values ranged from 72.5%
-

40%), for high spatial frequency faces:
42.174% (values ranged from 60%
-

27.5%), for low spatial frequency scenes: 48.533% (values
ranged from 67.5%
-

30%), and for high spatial frequency scene
s: 51.467% (values ranged from
65%
-

22.5%). The average pre
-
test values for number of positive faces seen and number of houses
seen are 24 and 26, respectively, which is also
very close to the ideal range.
The average changes of

14

low spatial frequency from

pre
-
test to post
-
test are graphed in Figure 4, and range from
-
5.25% to
7.5%.

The data were analyzed using 2 ANOVAs, one for each category of hybrids, with spatial
frequency and category of adaptors as between
-
subjects factors. For face hybrids, the mai
n effect

of
adapting spatial frequency wa
s significant, F(1,42) = 6.906, p = .012. The main
effect of category of
adaptors wa
s not significant, F(1,42) = .002, p = .965. The interaction of adapting spatial frequency
x category of
adaptors wa
s not significa
nt, F(1,42) = .083, p = .775. When adapted to low spatial
frequency faces or low spatial frequency scenes, participants were more likely to see high spatial
frequency face hybrids (
-
5.255 and
-
4.258 respectively), and when adapted to high spatial
frequency

faces or high spatial frequency scenes, participants were more likely to see low spatial
frequency face hybrids (3.542 and 2.8 respectively).
For scene hybrids, the main effect

of adapting
spatial frequency wa
s not significant, F(1,42) = .666, p = .419. T
he main eff
ect of category of the
adaptor wa
s not significant, F(1,42) = .016, p = .8998. The interaction of the adapting spatial
fr
equency x category of adaptors wa
s significant, F(1,42) = 3.914, p = .054. When adapted to high
spatial frequency scenes and

low spatial frequency faces, participants were significantly more likely
to see low spatial frequency scenes (
5.264 and 7.5 respectively), and were slightly more likely to
see low spatial frequency scenes when adapted to low spatial frequency scenes (1.49
2). When
adapted to high spatial frequency faces, participants were more likely to see high spatial frequency
scenes (
-
1.567).

Discussion

I hypothesized that the ideal results would show adaptation effects within each category
with no cross
-
adaptation eff
ects. That is, I predicted that being adapted to a particular spatial
frequency of scenes would cause participants to see more of the opposite spatial frequency of scene
hybrids, but would not change face hybrid perception. The results, however, suggest a
cross
-
over
effect for face hybrids. For face hybrids, the data does show the predicted pattern for face adaptors,

15

but this pattern is also found for scene adaptors, which is not expected. For scene hybrids, the only
category of adaptor that showed the pred
icted pattern was high spatial frequency scenes, and
somewhat high spatial frequency faces because the change was very small. These results suggest
that some effects of adaptation to spatial frequencies between faces and scenes may not be limited
within ca
tegory. However, the very different patterns on faces and scene hybrids support the idea
that the adaptation is sensitive to the type of stimuli.

Although the data does not show the predicted patterns for distinctions between faces and
scenes, subsequent

research needs to be done before my
hypotheses should be discarded.

One
possible problem with this experiment is that the face stimuli included a lot of “background
information” [Figures 1
-
3], such as ears, hair, and even background, all of which are not
part of the
core facial features that activate the FFA. These background features could have somehow been
encoded as “scene
-
like” features, leading to more general adaptation effects. One way to fix this is to
crop the faces in order to only show the disti
ngu
ishing facial features [Figure 6
]. Another possible
problem is that the task itself may not have sufficiently engaged the FFA, and might have activated
other areas where the role of spatial frequencies has not been explored, and therefore the outcomes
c
ould not be predicted. The task, an emotion discrimination task, does not typically activate the FFA
the way that a gender task or identity discrimination task would (Winston, Henson,

Find
-
Goulden,
& Dolan, 2004).
Subsequent research should address these p
roblems using a different task. For
instance, a gender discrimination task similar to the one in Schyns & Oliva (1999) could be done
using the same stimuli and the same experiment set
-
up as this experiment.

The current research has important implications.

Because no studies have explored the
effects of spatial frequency adaptation across categories, the results are still informative and show
researchers how to improve upon and change the experiment in order to really tease apart the
relationship between vi
sual processing of faces, scenes and other objects. Also, this research has
implications for spatial frequency adaptation studies that will test the relationship between face

16

processing and objects of expertise. If a clear distinction can be made between a
daptation effects
for faces and for other (non
-
expertise) objects, then any cross
-
adaptation effects found between
faces and objects of expertise can be attributed to the overlaps in processing between faces and
objects of expertise.





















Figure 1.

An example of an original positive face, negative face, house, and highway.


17

Figure 2.

Examples of a
face hybrids and scene hybrids.

Figure 3.

An example of a high spatial frequency face, a low spatial frequency face, a high spatial
frequency sce
ne, and a low spatial frequency scene.

Figure 4.

The results graph for face hybrids. The results show the percentage of change of low
spatial frequency for each category of adaptor. The error bars show a confidence interval of 95% of
the interaction.

Figu
re 5.

The results graph for scene hybrids. The results show the percentage of change of low
spatial frequency for each category of adaptor. The error bars show a confidence interval of 95% of
the interaction.

Figure 6.
An original face with an oval.














Figure 1.



18















Figure 2.




19










Figure 3.



20











Figure 4.



21







Figure
5.




22






Figure 6.



23


















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