The trait of sensory processing sensitivity and
neural responses to changes in visual scenes
Jadzia Jagiellowicz,1Xiaomeng Xu,1Arthur Aron,1Elaine Aron,1Guikang Cao,2Tingyong Feng,2and
1Department of Psychology, Stony Brook University, Stony Brook, NY 11794-2500, USA,2School of Psychology, Southwest University,
Chongqing, 400715, and3Laboratory for Higher Brain Function, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101,
This exploratory study examined the extent to which individual differences in sensory processing sensitivity (SPS), a tempera-
ment/personality trait characterized by social, emotional and physical sensitivity, are associated with neural response in visual
areas in response to subtle changes in visual scenes. Sixteen participants completed the Highly Sensitive Person questionnaire, a
standard measure of SPS. Subsequently, they were tested on a change detection task while undergoing functional magnetic
resonance imaging (fMRI). SPS was associated with significantly greater activation in brain areas involved in high-order visual
processing (i.e. right claustrum, left occipitotemporal, bilateral temporal and medial and posterior parietal regions) as well as in
the right cerebellum, when detecting minor (vs major) changes in stimuli. These findings remained strong and significant after
controlling for neuroticism and introversion, traits that are often correlated with SPS. These results provide the first evidence of
neural differences associated with SPS, the first direct support for the sensory aspect of this trait that has been studied primarily
for its social and affective implications, and preliminary evidence for heightened sensory processing in individuals high in SPS.
Keywords: sensory processing sensitivity; temperament; personality; fMRI; visual processing; highly sensitive person scale
Sensory processing sensitivity (SPS) is a temperament/per-
sonality trait characterized by sensitivity to both internal and
external stimuli, including social and emotional cues. The
standard measure of SPS in adults is the 27-item Highly
Sensitive Person Scale (HSP Scale), validated using a variety
of methods and populations (Aron and Aron, 1997). Items
include being aware of subtleties, bothered by intense stimuli
and strongly affected by caffeine, pain and time pressures;
startling easily, being more aware of others’ moods; and per-
forming poorly when observed (due to over arousal).
The SPS concept adopts the view from biology that most
species have evolved ‘personality’ types; for example, shy or
bold, aggressive or nonaggressive and sensitive or not (Sih
and Bell, 2008); that represent two underlying strategies. One
is ‘pausing before acting’ (or being ‘responsive’: Wolf et al.,
2008) in order to allow neural processes to assess
survival-related subtleties in the environment. The other is
‘acting first’ so as to respond quickly to opportunities and
discover survival-relevant cues through motor exploration.
For example, in fruit flies, there are two types, sitters and
rovers, representing two strategies of locating food (Renger
et al., 1999).
These two types determine behaviors such as feeding,
harm avoidance, mating, affiliating and seeking higher
status. The two strategies remain, because they each can suc-
ceed under different but normal variations in habitat
(Wilson et al., 1993; Sih and Bell, 2008).
SPS is closely related behaviorally to traits characterized by
pausing before acting. These include behavioral inhibition
(Carver and White, 1994; Kagan et al., 1994), shyness
(Jones et al., 1986), introversion and neuroticism (Aron
and Aron, 1997) and, most recently, biological sensitivity
to context (Ellis et al., 2005).
High-behavioral inhibition is usually interpreted as a
greater sensitivity to punishment or threat (Carver and
White, 1994), resulting from an especially active behavioral
inhibition system (BIS). As originally conceived, greater
behavioral inhibition was associated with a strategy of
taking time to process stimuli more thoroughly, especially
in novel situations, whether these were threatening stimuli or
not (Gray, 1981, 1986). While the point is often missed, Gray
did not view behavioral inhibition as only a greater aware-
ness of the threat of punishment. Indeed, Gray’s revised
model (McNaughton and Gray, 2000) makes the BIS a medi-
ator between the urge to proceed, coming from the BAS and
the fear system in the amygdala.
Aron and colleagues (2005) found support in four studies
for a model in which the interaction of SPS and a troubled
childhood predicted negative affectivity/neuroticism. Liss
Received 9 March 2009; Accepted 11 January 2010
This work was partially supported by the Chinese Ministry of Science and Technology Grant 2007CB512300
to X. W.
Correspondence should be addressed to Jadzia Jagiellowicz, Department of Psychology, SUNY?Stony Brook,
1 Nicolls Road, Stony Brook, NY 11794-2500, USA. E-mail: email@example.com.
? TheAuthor (2010).Publishedby OxfordUniversity Press.For Permissions,pleaseemail:firstname.lastname@example.org
Social Cognitive and Affective Neuroscience Advance Access published March 4, 2010
and colleagues (2005) found a similar interaction which, in
turn, predicted shyness. These findings suggest that shyness,
as a reason for pausing before acting, is related to sensitivity
but not identical to it.
Introverts have been found to have a greater awareness of
subtle stimuli, more attentional vigilance (Koelega, 1992)
and greater sensory reactivity (Stelmack, 1990; Doucet and
Stelmack, 1997, 2000). Introversion has been related to
reflectivity, defined as a slow and accurate response style
(Kagan et al., 1964) and a contemplative cognitive process
(Patterson et al., 1987). Compared to extraverts, introverts
respond more slowly following a punished trial and evidence
learning more from it (Patterson et al., 1987), all of which
suggests a preference for more elaborate processing of
We suggest that, in humans, learning history interacts
with the sensitive, pausing-first-to-observe strategy to
create a range of social behaviors (from leadership to with-
drawal) and emotional valences (from negative to positive).
This would explain moderate correlations of the HSP Scale
with introversion and neuroticism (Aron and Aron, 1997).
In the case of introversion, we suspect that some, but not all,
of those high in SPS have learned to avoid sensory overload,
a self-reported problem on the HSP Scale, by choosing inter-
actions with intimates over meeting strangers or being in
large groups. Regarding neuroticism or negative affectivity,
as noted earlier, there is some evidence (Aron et al., 2005)
that those higher in SPS have stronger emotional responses
overall, but the type of affect is determined by life history.
Indeed, with good parenting, sensitive or ‘emotionally reac-
tive’ children are healthier (Ellis et al., 2005) and ‘reactive’
primates more likely to be troop leaders (Suomi et al., 1991)
compared to those without the trait.
As previously mentioned, individuals with traits related to
SPS are characterized by a reflective response style (Patterson
et al., 1987). Additionally, individuals high in SPS report
having rich, complex inner lives, as well as noticing subtleties
in their environment (Aron and Aron, 1997), all of which
suggests they process stimuli more elaborately and/or pay
more attention to stimuli.
Given the above, the present study investigated the rela-
tionships between SPS and the perceptual and cognitive pro-
cesses underlying the tendency to pause before acting.
Specifically, we investigated the possibility that individuals
high in SPS pay more attention to stimuli and/or process
stimuli more elaborately. That is, we investigated the extent
to which individual differences in SPS are associated with
neural activation in primary, secondary and high-order
visual areas in response to gross vs subtle changes in visual
Sensory information is transformed into cognition by
associative elaboration modulated by attention (Mesulam,
1998). The processing of visual information, specifically,
takes place by means of neural networks ranging in com-
plexity from unimodal areas encoding features of sensation
through to transmodal areas such as the limbic and para-
limbic areas, which integrate information from the unimodal
areas (Mesulam, 1998). Attention is critical for noticing
change (Rensink et al., 1997; Kelley et al., 2003) and can
be measured using a change detection task (Rensink, 2002)
in which the participant is shown a stimulus, then the stim-
ulus is changed, and the behavioral response to the change is
measured, generally in terms of response time (Rensink,
Neuroimaging has been increasingly used to investigate
individual differences. The majority of studies have investi-
gated the relationship between differences in personality or
temperament and cognition or emotion (e.g. Gray and
Braver, 2002; Canli et al., 2004; Henderson and Wachs,
2007). Although a few researchers (Childers and Jiang,
2008; Sergerie et al., 2008) have studied individual differ-
ences in sensory perception/processing, the literature is not
a large one, nor does it address differences in personality/
temperament. The conceptualizations reviewed above sug-
gest that the way sensory information is processed is the
key to the temperamental difference characterized as SPS
and related constructs. This investigation is the first to exam-
ine the brain mechanisms that might underlie such a
In our study, participants compared a photograph of a
visual scene with a preceding scene and indicated, with a
button press, whether the scene had changed from the pre-
ceding scene. The primary manipulated variable was level of
detail of change (major/minor) in visual scenes; as an addi-
tional exploratory variable, we also manipulated speed of
presentation (fast/slow) of the scenes. Since the study was
exploratory with respect to neural activation, we did not
have specific hypotheses. However, we asked the following
research questions: ‘Will there be a relationship between SPS
and brain activation in response to subtle changes in visual
stimuli?’ and ‘If there are differences between individuals
high and low in the trait, what specific brain regions will
show these differences?’
Participants were 18 healthy, right-handed students with
normal or corrected-to-normal vision. They were a sample
of convenience recruited from the Chinese Agricultural
University, all in Beijing, China. Participants gave informed
consent. All procedures were approved by the Institutional
Review Boards at the Chinese Academy of Sciences, where
the scanning took place, and at Stony Brook University.
Imaging data from two participants had to be discarded
due to scanner error. The remaining 16 participants (8
women) were 19–25 years old (M¼21.6, s.d.¼1.63).
Mean SPS was 5.00 (s.d.¼0.60), somewhat higher than
two recent U.S. samples (3.88, 4.33; s.d.s, 0.58, 0.83)
recruited in about the same way, which we have studied
2 of10 SCAN (2010)
for other purposes. This higher mean is probably due to our
participants being more comfortable giving somewhat higher
ratings to scale items, because Chinese cultural norms appear
to be relatively more positive about sensitivity (Chen et al.,
1992). However, we have no reason to believe North
Americans would show a different association of SPS with
response to our task.
Stimuli were 16 black and white original photographs of
natural and man-made scenes (see Figure 1 for example).
Each photograph was also altered with either a major
change or a minor change using Photoshop software.
Major changes consisted of easily noticeable alterations
(e.g. inserting a second fencepost into a prominent fence);
minor changes, of more subtle alterations (e.g. inserting half
a hay bale in front of an existing line of hay bales). Stimuli
were pilot-tested to be sure pilot subjects could detect both
major and minor changes presented both quickly and slowly.
SPS was measured using 26 items from the 27-item HSP
Scale (Aron and Aron, 1997), translated into Chinese (by
two graduate students at The Chinese Academy of
Sciences) and then back-translated (by two different gradu-
ate students) into English to ensure accuracy of translation.
Discrepancies were resolved by discussion among the four
graduate students. One item was omitted from the scale due
to clerical error. The HSP Scale has been shown to have
strong discriminant and convergent validity (Aron and
Cronbach’s alphas in previous studies have typically been
0.85 or higher (e.g. Aron and Aron, 1997; Aron et al.,
2005; Benham, 2006; Hofmann and Bitran, 2007).
Alpha in this study was 0.77. (Note that our slightly lower
Example items are ‘Are you deeply moved by the arts or
music?’, ‘Do other people’s moods affect you?’, ‘Do you
seem to be aware of subtleties in your environment?’, ‘Are
you easily overwhelmed by strong sensory input?’, and ‘Do
you startle easily?’
Neuroticism and introversion were measured with a
50-item short form of the NEO Personality Inventory?
Revised (NEO-PI-R; Costa and McCrae, 1992) translated
and back-translated as per the HSP Scale procedure. The
NEO-PI-R is a valid and widely used measure of the ‘Big
Five’ normal personality traits. However, in the present con-
text, we were able to use only a subset of items for each of
our focal scales. Due to clerical errors, only 50 of the usual 60
items were administered. Additionally, subsequently dis-
covered translation errors eliminated some of the items we
did have, and testing of the items that seemed reasonable
showed that a few actually reduced the alpha. The final
scales we used included four items each. For neuroticism,
these were NEO-PI-R items 8, 10, 36 and 47 (alpha¼0.62).
good internal consistency.
Example items: ‘I often worry about things that might go
wrong’ and ‘Frightening thoughts sometimes come into my
head’. For introversion, these were items 3, 19, 29 and 31
(alpha¼0.44). Example items: ‘I shy away from crowds of
people’ and ‘I prefer jobs that let me work alone without
being bothered by other people’.
Correlations of neuroticism and introversion with the
HSP Scale were 0.10 (n.s.) and 0.16 (n.s.), respectively.
These are lower than previous correlations found in North
American samples (Aron and Aron, 1997), possibly because
the measures were weaker due to translation issues. Also, this
discrepancy could be due, as noted earlier, to sensitivity
being more highly valued in Chinese societies, so that sen-
sitive individuals are not seen as introverted or neurotic.
Design and procedure
The task was adapted from a change detection task used by
Rensink and colleagues (1997). Stimuli were presented in
16 blocks, comprising 72 image presentations in total.
Each block contained images with either minor changes or
major changes presented either slowly or quickly. Blocks of
quickly presented stimuli (i.e. fast condition) consisted of
five (i.e. one original, four target) images presented for
1.20s each. The four target images were separated by fixation
crosses of 4.79s (see Figure 2). Blocks of slowly presented
stimuli (i.e. slow condition) consisted of four (i.e. one ori-
ginal, three target) images presented for 2.0s each. Target
images were separated by fixation crosses of 4.0s. Conditions
were presented in random order, and images were presented
in random order within each condition.
When viewing the images, participants performed a
change detection task while in a functional magnetic reso-
nance imaging (fMRI) scanner, indicating their responses by
pressing buttons on a button box. While in the scanner, a
participant viewed a fixation cross, the original image, then
the same image changed or unchanged from the preceding
image, with fixation crosses between each image. The par-
ticipant was instructed to respond during the fixation
crosses, starting in each block during the fixation cross fol-
lowing the second image. The participant pressed the left
button to indicate the image was the same as, and the
right button to indicate the image was different from, the
preceding image. After the initial image, on average, half the
images in each block were the same, and half different, from
the preceding image.
Visual stimuli were projected on a screen placed directly
outside the MRI tube, subtending a visual angle of 178.
Participants viewed images via an angled mirror mounted
on the RF coil of the scanner. The participants were pre-
sented with a box containing two response buttons con-
nected to a personal computer running Eprime software
We acquired functional images on a 3T GE Signa LX
MRI scanner (General Electric, Waukesha, WI, USA) at
SPS and Visual ProcessingSCAN (2010) 3 of10
the Beijing MRI Center for Brain Research and recorded
blood oxygen level-dependent responses. We acquired func-
tional images using T2-weighted gradient-echo echo-planar
sequence (repetition time 2000ms, echo time 30ms, 908 flip
angle, field of view 240?240mm, 64?64 matrix). The
images consisted of 30 contiguous axial slices of 4-mm thick-
ness. Voxel size was 3.8?3.8?4.00mm. Four volumes were
introduced before beginning the set of blocks for the exper-
iment and discarded from analysis. Not including the four
discarded volumes, 218 volumes were acquired during the
7.2-min functional scan. We also acquired anatomical, axial
T1-weighted Spin-Echo Scans (repetition time 3700ms, echo
time 92ms, 256?256 matrix, 908 flip angle, 240mm?
240mm field of view, slice thickness 4mm) in the same
session. Voxel size was 0.9?0.9?4.00mm.
Behavioral data (i.e. accuracy and response time) were ana-
lyzed using a 2 (level of detail of change)?2 (speed of pre-
sentation) repeated measures design.
Fig. 1 Example of (A and B) original stimuli, (C) stimulus with a major change and (D) stimulus with a minor change.
4 of10 SCAN (2010) J. Jagiellowiczetal.
fMRI data were processed using SPM2 (Statistical
Parametric Mapping) software (Wellcome Department of
Cognitive Neurology, London, UK; http://www.fil.ion.ucl
.ac.uk/spm). Anatomical images were transformed stereotac-
tically for each subject using linear rigid transformations.
Functional scans were corrected for head motion and then
realigned with reference to the first functional file. They were
then coregistered with in-plane anatomical images and nor-
malized to a Montreal Neurological Institute template. Next,
images were spatially smoothed using a 6-mm full-width-at
maps were then computed, using a random effects model,
on overall group contrasts of major minus minor, fast minus
slow and the 2?2 interaction. In separate, standard
between-subject general linear model regressions, HSP
mean score and HSP residual (i.e. HSP mean score after
partialling out neuroticism and introversion) were each
used as predictors for each contrast.
Regions of interest (ROI) were defined as 10-mm spheres.
The center of the spheres were based at the peak coordinates
of activation clusters identified from the literature to be
relevant to visual attention and oculomotor processes as
well as motion processing (Petersen et al., 1985; Tootell
et al., 1995; Corbetta et al., 1998). These consisted of the
right midbrain tegmentum, left intralaminar thalamic
nucleus, right pulvinar nucleus of the thalamus, right intra-
parietal sulcus (IPS), junction of the intraparietal/transverse
occipital sulcus, middle temporal complex, right inferior
parietal lobule, the right superior temporal gyrus and the
right precentral sulcus (i.e. middle frontal gyrus).
Accuracy and response time were analyzed for only 12 of the
16 participants. The remaining four were clear outliers on
missing values (no response or response not recorded,
because it was after the allotted time). The four excluded
each had >20 missing values (vs?3 for each of the others).
Participants were more accurate when images were pre-
sented slowly than quickly, F(1,11)¼22.86, P¼0.001. There
was also a trend toward being more accurate at spotting
majorthan minor changes,F(1,11)¼3.83,P¼0.08.
Fig. 2 Schematic of the change detection task. The design included four conditions: quickly presented major changes, quickly presented minor changes, slowly presented major
changes, and slowly presented minor changes. Each condition consisted of presentation of an original image, followed by three (slow presentation condition) or four (fast
presentation condition) either changed or unchanged images.
SPS and Visual ProcessingSCAN (2010) 5 of10
Finally, there was a significant interaction, F(1,11)¼5.38,
P¼0.04. Participants were most accurate when major
changes were presented slowly, least when minor changes
were presented slowly. Regarding response time, participants
were actually somewhat faster when images were presented
slowly, F(1,11)¼4.99, P¼0.05, plus evidenced a trend
toward being slightly faster at responding to major than
minor changes, F(1,11)¼3.25, P¼0.10. There was no inter-
action. (See Supplementary Table 1 for mean accuracy and
response time by condition.)
Regarding SPS (and SPS residuals controlling for N and I),
there were no significant associations with accuracy for fast
minus slow, major minus minor, or interactions. For
response time, however, there was a significant correlation
of SPS with the minor-minus-major difference. The higher a
participant was on SPS, the longer time the participant spent
before responding to minor changes (relative to time spent
on major changes), r¼0.64, P¼0.02. For example, on aver-
age, subjects overall took about 60ms longer for minor than
major changes. However, for individuals low in SPS, there
was almost no difference; but those high in SPS took 132ms
longer to respond to minor than major changes. (Figures
calculated from overall regression equation at one s.d.
below and one s.d. above the SPS mean.) For SPS residuals,
the same strong association remained as a near-significant
trend, r¼0.51, P¼0.09. There was also a trend for SPS to
correlate with responding relatively more quickly on slow
than on fast trials, r¼0.52, P¼0.08; for SPS residuals,
r¼0.44, P¼0.16. There were no significant correlations
with the interaction.
Overall Group-level Contrasts
Overall, group-level contrasts (i.e. not considering associa-
tions with individual differences in SPS) indicated signifi-
cantly greater brain activation in several regions when
viewing images with minor vs major changes. In this overall
group analysis, there was greater activation in the right lin-
gual gyrus and cuneus of the occipital lobe, as well as in the
insula, in response to minor changes than in response to
major changes in the visual scenes (see Supplementary
Table 2). In the overall group analysis, there was also sig-
nificantly greater brain activation for major vs minor change
in a number of areas in the occipital and frontal lobes, as well
as in the cerebellum. The most extensive brain activation in
response to the major vs minor change contrast was in the
sublobar areas of the insula, globus pallidus, thalamus and
caudate (see Supplementary Table 3). Finally, in the overall
group analysis, there was significantly more brain activation
in the inferior occipital gyrus and the globus pallidus in
response to slow vs fast presentation of visual scenes (see
Supplementary Table 4). No other overall contrasts were
significant. (Supplementary Figure 1 shows brain slices for
Imaging results for associations with individual
differences in SPS and SPS residuals
Contrasts of minor greater than major change conditions
had strong and significant associations with individual
differences in SPS in brain areas in the temporal lobe, the
claustrum and the cerebellum (see Table 1). Figure 3 shows
brain slices for selected associations of mean SPS scores with
brain activation contrasts.
As shown in Table 2, these associations remained strong
and significant after controlling for neuroticism (N) and
introversion (I) scores, with additional strong and sig-
nificant associations in the left temporal lobe as well as in
the left temporooccipital junction. Additional activation was
observed bilaterally in the inferior parietal lobule/precuneus.
(See Figure 4 for brain slice for selected coordinates.)
Note that all significant associations were checked for
outliers and were positive, such that those high on SPS,
compared to those low in SPS, showed greater activation
in these regions during the minor change blocks than
during the major change blocks. There were no negative
associations that met our significance threshold (P?0.001,
cluster size?25 voxels) for SPS or SPS residuals with this
contrast. Nor were there any positive or negative associations
with the fast vs slow contrast or with the interaction of
major/minor with fast/slow.
Region of interest results for associations with SPS
and SPS residuals
Both not controlling and controlling for neuroticism and
introversion, contrasts of minor greater than major change
conditions had strong and significant associations with SPS
scores in functional areas related to visual attention and
oculomotor control. As seen in Table 3, there were strong
and significant associations in the right hemisphere in the
temporoparietal cortical junction (TPJ; inferior parietal
lobule and superior temporal gyrus), the intraparietal
sulcus (IPS; lying between the superior and inferior parietal
lobes) and the middle frontal gyrus (i.e. precentral sulcus).
No significant associations met our threshold of 25 or more
Table 1 MNI coordinates of brain regions showing significant activation
after the regression of SPS on the contrast of minor less major changes in
Cluster location HemisphereBACluster
Middle temporal gyrusL 37154
Sub-gyral temporal lobe
Declive of cerebellum
T–contrasts thresholded (uncorrected) at P¼0.001. Activation at 25 or more voxels,
6 of10SCAN (2010)J. Jagiellowiczetal.
voxels (false discovery rate of P¼0.05) in the middle tem-
poral (MT/V5) area, the thalamic nuclei or the right
We report FDR thresholded data for the ROIs in Table 3
and not in Tables 1 and 2 (where we used an overall
P<0.001 with a 25 voxel minimum). This is because the
results reported in Tables 1 and 2 derive from an exploratory
analysis, whereas those reported in Table 3 are ROIs based
on known functional areas of brain activation relevant to
visual attention and oculomotor processes as well as
motion processing (Petersen et al., 1985; Tootell et al., 1995).
Our study focused on the association of individual differ-
ences in the temperament/personality trait of SPS with
Fig. 3 BOLD in the left middle temporal gyrus and the right subgyral temporal lobe. Group average activation data for the association of HSP mean with the minor less the
major change condition in the (A) left middle temporal lobe and the (B) right subgyral temporal lobe. Lighter color corresponds to greater activation. MNI co-ordinates for the
center of the left (second peak) and right activation clusters were ?56, ?54 and ?4 and 48, ?46, ?12, respectively.
Fig. 4 BOLD in the left middle temporal gyrus. Group average activation data for the
association of the standardized residual of the HSP mean and the minor less the
major change. Lighter color corresponds to greater activation. MNI co-ordinates were
–52, ?56, ?6.
Table 2 MNI coordinates of brain regions showing significant activation
after the regression of standardized SPS residual on the contrast of minor
less major changes in visual scenes
Cluster location Hemisphere BA Cluster
Sub-gyral temporal lobe R
Middle occipital gyrus
Middle temporal gyrus
Inferior parietal lobule
Declive of posterior
T–contrasts thresholded (uncorrected) at P¼0.001. Activation for clusters of 25 or
more voxels. P<0.001.
SPS and Visual ProcessingSCAN (2010) 7 of10
neural activation in a change detection task. Our results sup-
port a relationship between SPS and both increased response
time and increased brain activation in relevant regions in
response to subtle changes in stimuli. SPS is correlated
with the minor-minus-major difference for both RT and
activation in visual attentional areas. Conceptually, such
results suggest that individuals high in SPS take longer to
respond to minor changes in a scene and show more acti-
vation in visual attentional areas when responding to minor
changes, because they are attending more closely to the
subtle details of that scene.
There was a significant relationship between SPS and
brain activation in the left middle temporal gyrus, the
right claustrum, the right subgyral temporal lobe and the
right declive of the cerebellum in response to minor vs
major changes in stimuli. After controlling for the associa-
tion of measures of neuroticism and introversion with SPS,
this relationship remained significant. There was also activa-
tion in the bilateral inferior parietal lobe.
We interpreted the functionality of the activated brain
areas based on previous fMRI studies with co-ordinates in
approximately similar areas, as listed in the AMAT (http://
coordinate database as well as literature searches of theoret-
ically relevant brain functions.
Our findings from both whole-brain and region of interest
analyses are in regions similar to those found in functional
areas discussed in the visual processing literature, supporting
the validity of the study. For example, we found strong and
significant brain activation in the claustrum when individ-
uals high in SPS viewed minor differences in natural scenes.
Although the function of the claustrum is unknown in
humans, it has connections with the sensory and motor
areas of the neocortex (Yamamoto et al., 2007) and contrib-
utes to the processing of visual stimuli in the forebrain of the
cat (Olson and Graybiel, 1980). Yamamoto and colleagues
(2007) suggested that Lewy bodies found in the claustrum
were related to visual misidentification, including visual hal-
lucinations, which also implicates the claustrum in visual
Our findings of associations of individual differences in
SPS with activation in occipital and temporal regions and the
precuneus relate to findings in the literature outlining vari-
ous combinations of these areas as part of neural networks
for object recognition (Kanwisher et al., 1997) and for cate-
gorization and discrimination (Pernet et al., 2004). Within
these networks, the occipitotemporal junction (BA 19 and
37) is related to shape analysis (Kanwisher et al., 1997), spe-
cifically to deviations in item shape (Piazza et al., 2004).
SPS was also associated with activation in the declive of
the vermis of the right cerebellum, an area implicated in
oculomotor guidance. Previous research reports that the
vermis codes eye position relative to the orbits, which is
implicated in fixing the location of objects in space irrespec-
tive of changes in eye position (Law et al., 1998). In a task
such as ours, activation could be implicated in locating the
original scene image in space and then remapping the scene
on the changed image.
Individuals high in SPS evidenced greater brain activation
in an additional network of functional brain areas that
appear to be involved in visual attention and oculomotor
processes [see review by Behrmann et al. (2004) and Small
et al. (2003)]. Functional (i.e. IPS and TPJ) and anatomical
(i.e. precentral sulcus) brain areas in our ROI analyses are
implicated in the shifting of attention, both when individuals
attend to peripheral visual stimuli and when they move their
eyes and their attention to the same stimuli (Corbetta et al.,
1998, 2000). Participants in our study would have needed to
attend to both overt and peripheral visual stimuli in order to
detect changes in a visual scene. Our findings would also
follow from the literature linking attentional processes to
change detection (Rensink et al., 1997). Since individuals
high in SPS report an ability to notice subtle changes
(Aron and Aron, 1997), it comes as no surprise that they
have greater activation in attentional areas in response to a
change detection task than do individuals low on the trait.
Although we found increased localized brain activation
and response time differences linked to SPS, we did not
find significant associations of SPS with accuracy. This
may be due to low statistical power.
The finding that the basic pattern of results remains
unchanged when controlling for measures of introversion
and neuroticism is also important. It supports the idea
that SPS makes a unique contribution to individual
Table 3 MNI Coordinates for brain ROIs showing activation in response to
correlation of SPS residual with minor less major changes in visual scenes
Cluster location Hemisphere BA Cluster
Inferior parietal lobule
Superior temporal gyrus R
R 40 60 56
Superior occipital gyrus R
Inferior parietal lobule
19 155 34
Middle frontal gyrusR6 3802
0Middle frontal gyrusR633
FDR¼0.05. Activation at 25 or more voxels.TPJ, tempero-parietal cortical junction;
SPL, superior parietal lobule; TOS, transverse occipital sulcus.
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differences in brain response, over and above major person-
ality variables with which it has been found to be correlated
in North American samples. This finding should be consid-
ered somewhat tentative, however, since there was little cor-
neuroticism and SPS in this sample. This may have been
due to translation and reliability limitations of our measures
of these personality variables or to the specificity of the rela-
tionship between SPS and neuroticism and introversion to a
North American population.
The major limitations of our study are that it was explor-
atory and that the measures of the two questionnaire control
variables may have been less than optimal. Another potential
limitation was that we used a sample of convenience and not
a sample selected on the basis of their SPS scores. The sample
of convenience took advantage of an opportunity to include
our task in an fMRI study being done for a different purpose.
We realized that our study would require a substantial effect
to be significant. However, given previous related research
and the theoretical background of the SPS trait, we had some
reason to expect that even with such a small, unselected
sample, the effects might be large enough to be significant
(as they were).
A strength of our study is that it is the first to investigate
the neural correlates of SPS, adding to the growing literature
with respect to this temperament/personality trait in adults.
In addition, it begins to address the question of whether indi-
viduals high in SPS process sensory information more elabo-
rately than individuals low in SPS, that is, with a greater
attention to detail and with more attention to subtleties.
Such ‘more elaborate processing’ is, we postulate, related to
a greater degree of integration of various components of the
neurological processes underlying visual processing.
The extent to which our results support that conclusion
leads to a deeper understanding of the mechanisms through
which SPS appears to influence a variety of important social
and affective behavioral phenomena, including for example,
the ways in which SPS creates greater vulnerability to poor
parenting and other stressors leading to neuroticism and
shyness (Aron et al., 2005).
Potentially fruitful future research directions could
include replication in a North American sample to examine
generalizability of the results. It will also be important to
examine potential alternative explanations for the self-
reported sensitivity to subtleties of individuals high in SPS.
In a review of the literature, Stelmack (1990) reports the
enhanced sensory reactivity of introverts to punctate stimuli,
as measured by electrodermal and electrocortical recordings.
He attributes this reactivity to peripheral sensory processes.
In a change detection task with natural scenes, such as ours,
it is difficult to control changes in sensory thresholds while
keeping all other variables constant. It will be important to
try to administer some of the measures he reported (startle
reflexes, event-related potentials and electrodermal activity)
to investigate whether individuals high in SPS may have a
lower threshold for screening out sensory stimuli, alongside
their ability to process stimuli more elaborately. Another
important direction for future research is exploring the
links of the visual and related brain regions identified here
with areas directly implicated in the social and affective
dimensions of SPS.
In conclusion, this research, the first neural investigation
of SPS, lays a foundation for future studies of how cognitive
and perceptual processes are affected by high levels of SPS.
On a broader level, it could suggest that a greater under-
standing of personality might be gained by borrowing from
the observation of biology that there are strategies behind
‘personality differences’ that involve a preference for pausing
to process information more elaborately before acting.
Supplementary data are available at SCAN online.
Aron, E.N., Aron, A. (1997). Sensory-processing sensitivity and its relation
to introversion and emotionality. Journal of Personality and Social
Psychology, 73, 345–68.
Aron, E.N., Aron, A., Davies, K.M. (2005). Adult shyness: the interaction of
temperamental sensitivity and an adverse childhood environment.
Personality and Social Psychology Bulletin, 31, 181–97.
Behrmann, M., Geng, J.J., Shomstein, S. (2004). Parietal cortex and atten-
tion. Current Opinion in Neurobiology, 14, 212–17.
Benham, G. (2006). The highly sensitive person: stress and physical symp-
tom reports. Personality and Individual Differences, 40, 1433–40.
Canli, T., Amin, Z., Haas, B., Omura, K., Constable, R.T. (2004). A double
dissociation between mood states and personality traits in the anterior
cingulate. Behavioral Neuroscience, 118, 897–904.
Carver, C.S., White, T.L. (1994). Behavioral inhibition, behavioral activa-
tion, and affective responses to impending reward and punishment: the
BIS/BAS Scales. Journal of Personality and Social Psychology, 67, 319–33.
Childers, T.L., Jiang, Y. (2008). Neurobiological perspectives on the nature
of visual and verbal processes. Journal of Consumer Psychology, 18, 264–9.
Chen, X.Y., Rubin, K.H., Sun, Y.R. (1992). Social reputation and peer
relationships in Chinese and Canadian children: a cross-cultural study.
Child Development, 63, 1336–43.
Corbetta, M., Akbudak, E., Conturo, T.E., et al. (1998). A common network
of functional areas for attention and eye movements. Neuron, 21, 761–73.
Corbetta, M., Kincade, J.M., Ollinger, J.M., McAvoy, M.P., Shulman, G.L.
(2000). Voluntary orienting is dissociated from target detection in human
posterior parietal cortex. Nature Neuroscience, 3, 292–7.
Costa, P.T., McCrae, R.R. (1992). Revised NEO Personality Inventory (NEO-
PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual.
Odessa, FL: Psychological Assessment Resources.
Doucet, C., Stelmack, R.M. (1997). Movement time differentiates extraverts
from introverts. Personality and Individual Differences, 23, 775–86.
Doucet, C., Stelmack, R.M. (2000). An event-related potential analysis of
extraversion and individual differences in cognitive processing speed and
response execution. Journal of Personality and Social Psychology, 78,
Ellis, B.J., Essex, M.J., Boyce, W.T. (2005). Biological sensitivity to context.
II. Empirical explorations of an evolutionary-developmental theory.
Development and Psychopathology, 17, 303–28.
Gray, J.A. (1981). A critique of Eysenck’s theory of personality.
In: Eysenck, H.J., editor. A Model for Personality. New York: Springer,
Gray, J.A. (1986). Anxiety, personality and the brain. In: Gale, A.,
Edwards, J., editors. Physiological Correlates of Human Behaviour, Vol.
SPS and Visual Processing SCAN (2010) 9 of10
3: Individual Differences and Psychopathology. New York: Academic Press, Download full-text
Henderson, H.A., Wachs, T.D. (2007). Temperament theory and the study
of cognition-emotion interactions across development. Developmental
Review, 27, 396–427.
Hofmann, S.G., Bitran, S. (2007). Sensory-processing sensitivity in social
anxiety disorder: relationship to harm avoidance and diagnostic sub-
types. Journal of Anxiety Disorders, 21, 944–54.
Jones, W.H., Cheek, J.M., Briggs, S.R., editors (1986). Shyness: Perspectives
on Research and Treatment. New York: Plenum.
Kagan, J., Rosman, B.L., Day, D., Albert, J., Phillips, W. (1964). Information
processing in the child: significance of analytic and reflective attitudes.
Psychological Monographs: General and Applied, 78, 1–37.
Kagan, J., Snidman, N., Arcus, D., Reznick, J.S. (1994). Galen’s Prophecy:
Temperament in Human Nature. New York: Basic Books.
Kanwisher, N., Woods, R.P., Iacoboni, M., Mazziotta, J.C. (1997). A locus in
human extrastriate cortex for visual shape analysis. Journal of Cognitive
Neuroscience, 9, 133–42.
Kelley, T.A., Chun, M.M., Chua, K.P. (2003). Effects of scene inversion on
change detection of targets matched for visual salience. Journal of Vision,
Koelega, H.S. (1992). Extroversion and vigilance performance: 30 years of
inconsistencies. Psychological Bulletin, 112, 239–58.
Law, I., Svarer, C., Rostrup, E., Paulson, O.B. (1998). Parieto-occipital
cortex activation during self-generated eye movements in the dark.
Brain, 121, 2189–200.
Liss, M., Timmel, L., Baxley, K., Killingsworth, P. (2005). Sensory pro-
cessing sensitivity and its relation to parental bonding, anxiety, and
depression. Personality and Individual Differences, 39, 1429–39.
McNaughton, N., Gray, J.A. (2000). Anxiolytic action on the behavioural
inhibition system implies multiple types of arousal contribute to anxiety.
Journal of Affective Disorders, 61, 161–76.
Mesulam, M.M. (1998). From sensation to cognition. Brain, 121, 1013–52.
Olson, C.R., Graybiel, A.M. (1980). Sensory maps in the claustrum of the
cat. Nature, 288, 479–81.
Patterson, C.M., Kosson, D.S., Newman, J.P. (1987). Reaction to punish-
ment, reflectivity, and passive-avoidance learning in extroverts. Journal of
Personality and Social Psychology, 52, 565–75.
Pernet, C., Franceries, X., Basan, S., Cassol, E., Demonet, J.F., Celsis, P.
(2004). Anatomy and time course of discrimination and categorization
processes in vision: an fMRI study. Neuroimage, 22, 1563–77.
Petersen, S.E., Robinson, D.L., Keys, W. (1985). Pulvinar nuclei of the
behaving rhesus-monkey-visual responses and their modulation. Journal
of Neurophysiology, 54, 867–86.
Piazza, M., Izard, V., Pinel, P., Le Bihan, D., Dehaene, S. (2004). Tuning
curves for approximate numerosity in the human intraparietal sulcus.
Neuron, 44, 547–55.
Renger, J.J., Yao, W.D., Sokolowski, M.B., Wu, C.F. (1999). Neuronal
polymorphism among natural alleles of a cGMP-dependent kinase
gene, foraging, in Drosophila. Journal of Neuroscience, 19, 8.
Rensink, R.A. (2002). Change detection. Annual Review of Psychology, 53,
Rensink, R.A., O’Regan, J.K., Clark, J.J. (1997). To see or not to see: the
need for attention to perceive changes in scenes. Psychological Science, 8,
Sergerie, K., Chochol, C., Armony, J.L. (2008). The role of the amygdala
in emotional processing: a quantitative meta-analysis of functional
neuroimaging studies. Neuroscience and Biobehavioral Reviews, 32,
Sih, A., Bell, A.M. (2008). Insights for behavioral ecology from behavioral
syndromes. In: Brockmann, H.J., Roper, T.J., Naguib, M., Wynne-
Edwards, K.E., Barnard, C., Mitani, J., editors. Advances in the Study of
Behavior, Vol. 38, San Diego: Elsevier Academic Press, pp. 227–81.
Small, D.M., Gitelman, D.R., Gregory, M.D., Nobre, A.C., Parrish, T.B.,
Mesulam, M.M. (2003). The posterior cingulate and medial prefrontal
cortex mediate theanticipatory
Neuroimage, 18, 633–41.
Stelmack, R.M. (1990). Biological basis of extraversion: psychophysiological
evidence. Journal of Personality, 58, 293–311.
Suomi, S.J. (1991). Uptight and laid-back monkeys: individual differences in
the response to social challenges. In: Brauth, S.E., Hall, W.S.,
Dooling, R.J., editors. Plasticity of Development. Cambridge, MA: MIT
Press, pp. 27–56.
Tootell, R.B.H., Reppas, J.B., Kwong, K.K., et al. (1995). Functional analysis
of human MT and related visual cortical areas by functional magnetic
resonance imaging. Neuroscience, 15, 3215–30.
Wilson, D.S., Coleman, K., Clark, A.B., Biederman, L. (1993). Shy-bold
continuum in pumpkinseed sunfish (Lepomis gibbosus): an ecological
study of a psychological trait. Journal of Comparative Psychology, 107,
Wolf, M., van Doorn, G.S., Weissing, F.J. (2008). Evolutionary emergence of
responsive and unresponsive personalities. Proceedings of the National
Academy of Sciences, USA, 105, 15825–30.
Yamamoto, R., Iseki, E., Murayama, N., et al. (2007). Correlation in Lewy
pathology between the claustrum and visual areas in brains of dementia
with Lewy bodies. Neuroscience Letters, 415, 219–24.
allocation ofspatial attention.
10 of10 SCAN (2010)J. Jagiellowiczetal.