ArticlePDF Available

Baby schema modulates the brain reward system in nulliparous women


Abstract and Figures

Ethologist Konrad Lorenz defined the baby schema ("Kindchenschema") as a set of infantile physical features, such as round face and big eyes, that is perceived as cute and motivates caretaking behavior in the human, with the evolutionary function of enhancing offspring survival. The neural basis of this fundamental altruistic instinct is not well understood. Prior studies reported a pattern of brain response to pictures of children, but did not dissociate the brain response to baby schema from the response to children. Using functional magnetic resonance imaging and controlled manipulation of the baby schema in infant faces, we found that baby schema activates the nucleus accumbens, a key structure of the mesocorticolimbic system mediating reward processing and appetitive motivation, in nulliparous women. Our findings suggest that engagement of the mesocorticolimbic system is the neurophysiologic mechanism by which baby schema promotes human caregiving, regardless of kinship.
Content may be subject to copyright.
Baby schema modulates the brain reward system
in nulliparous women
Melanie L. Glocker
, Daniel D. Langleben
, Kosha Ruparel
, James W. Loughead
, Jeffrey N. Valdez
Mark D. Griffin
, Norbert Sachser
, and Ruben C. Gur
aBrain Behavior Laboratory and cCenter for Studies of Addictions, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104;
bDepartment of Behavioral Biology, University of Muenster, 48149 Muenster, Germany; and dPhiladelphia Veterans Administration Medical
Center, Philadelphia, PA 19104
Edited by Marcus E. Raichle, Washington University School of Medicine, St. Louis, MO, and approved April 10, 2009 (received for review November 17, 2008)
Ethologist Konrad Lorenz defined the baby schema (‘‘Kindchen-
schema’’) as a set of infantile physical features, such as round face
and big eyes, that is perceived as cute and motivates caretaking
behavior in the human, with the evolutionary function of enhanc-
ing offspring survival. The neural basis of this fundamental altru-
istic instinct is not well understood. Prior studies reported a pattern
of brain response to pictures of children, but did not dissociate the
brain response to baby schema from the response to children.
Using functional magnetic resonance imaging and controlled ma-
nipulation of the baby schema in infant faces, we found that baby
schema activates the nucleus accumbens, a key structure of the
mesocorticolimbic system mediating reward processing and ap-
petitive motivation, in nulliparous women. Our findings suggest
that engagement of the mesocorticolimbic system is the neuro-
physiologic mechanism by which baby schema promotes human
caregiving, regardless of kinship.
caregiving functional MRI social cognition infant accumbens
Ethologist Konrad Lorenz defined the baby schema (‘‘Kind-
chenschema’’) as a set of infantile physical features, such as
large head, big eyes, high and protruding forehead, chubby
cheeks, small nose and mouth, short and thick extremities, and
plump body shape, that is perceived as cute and motivates
caretaking behavior in the human (1, 2). In a species whose
young depend on care, such bias could be evolutionary adaptive
and enhance offspring survival (3–5). The behavioral effects of
the baby schema have been experimentally confirmed (6–14),
with implications for infant-caretaker interactions (15, 16). In
ethological terms, baby schema is classified as a ‘‘releaser’’ (or
‘‘key stimulus’’ in the context of social communication), which is
defined as a set of specific stimulus features sufficient to
selectively elicit a particular pattern of behavior (2, 17). This
abstract concept accounts for the generalization of the human
response to baby schema: We not only respond positively to
infants, but also to the baby schema features in adults (18),
animals (12, 19), and even objects (20). Additional support for
the motivating force of the baby schema comes from film, toy,
and advertisement industries who capitalize on our nurturing
reaction [i.e., Walt Disney’s Mickey Mouse (21)].
Although the baby schema response is a fundamental social
instinct that may be at the basis of human caregiving and altruism
(22), its underlying neural mechanism is not well understood.
Ethologists postulate that a releaser unlocks a hypothetical
neurophysiologic ‘‘releasing mechanism’’ to trigger the respec-
tive behavioral response (2, 17), providing an early articulation
of a putative brain-behavior relationship for the baby schema.
Imaging studies demonstrated a differential brain response to
child faces when compared to adult faces, with activation in
multiple brain regions, including reward-related areas, such as
the orbitofrontal cortex (23, 24). Together with the behavioral
studies on the motivating effects of the baby schema (7, 8, 10),
these findings suggest that the mesocorticolimbic system under-
lying reward processing and appetitive motivation may mediate
the baby schema response. However, the baby schema is an
abstract concept of infantile features that is distinct from chil-
dren as a semantic category (1, 2). Previous studies reported a
pattern of brain response to pictures of children (23, 24), but they
did not dissociate the response to baby schema from the response
to children. We used functional MRI (fMRI) and controlled
manipulation of baby schema in infant faces to test the hypoth-
esis that baby schema activates the mesocorticolimbic system,
comprised of the dopaminergic midbrain, nucleus accumbens,
amygdala, and ventromedial prefrontal cortex (25).
The majority of baby schema features are in the head and the
face and most prior research has focused on these infant
characteristics (6, 7, 9, 11–13). Using anthropometric (26) and
morphing techniques, we manipulated photographs of 17 infants
[originals courtesy Katherine Karraker, West Virginia Univer-
sity; (11)] to produce high (round face, high forehead, big eyes,
small nose and mouth), low (narrow face, low forehead, small
eyes, big nose and mouth), and unmanipulated baby schema
portraits of each infant (Fig. 1). We previously demonstrated in
a sample of 122 undergraduate students that the high baby
schema infants are perceived as cuter and elicit stronger moti-
vation for caretaking than the unmanipulated and the low baby
schema infants (10). In the present study, 16 nulliparous female
participants viewed a pseudorandom sequence of these infant
faces while we measured their brain activity with event-related
blood oxygenation level-dependent (BOLD) fMRI at high field
(3 Tesla). During the session, participants rated the pictures for
cuteness (1 ‘‘not very cute,’’ 2 ‘‘cute,’’ 3 ‘‘very cute’’) with
a button press on a fiber-optic response pad (FORP Current
Design, Inc.).
Repeated-measures ANOVA revealed a significant main ef-
fect of baby schema on participants’ cuteness ratings (F
(2, 14)
60.00, P0.001). The high baby schema infants were rated
as cuter than the unmanipulated and the low baby schema
infants (Bonferroni corrected pair-wise comparisons: high vs.
low P0.001, high vs. unmanipulated P0.001, unmanipu-
lated vs. low P0.001), confirming the behavioral validity of
our paradigm (10).
Neuroimaging results showed that infant faces (versus cross-
hair) across baby schema levels activated the fusiform gyrus,
thalamus, cingulate gyrus, insula, and orbitofrontal cortex (see
Table 1 for a complete list of regions), in agreement with
previous reports on the perception of children’s faces (23, 24,
27–29). A whole-brain voxel-wise test of linear increase in BOLD
fMRI signal with increased baby schema revealed significant
Author contributions: M.L.G., D.D.L., K.R., J.W.L., N.S., and R.C.G. designed research; M.L.G.,
J.W.L., and J.N.V. performed research; M.L.G., K.R., J.W.L., and M.D.G. analyzed data; and
M.L.G., D.D.L., K.R., J.W.L., N.S., and R.C.G. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1To whom correspondence should be addressed. E-mail:
www.pnas.orgcgidoi10.1073pnas.0811620106 PNAS
June 2, 2009
vol. 106
no. 22
clusters of activation in 4 brain regions: the right nucleus
accumbens (10, 12, – 8), left anterior cingulate cortex (–4, 24, 38),
left precuneus (–24, –68, 30), and left fusiform gyrus (–42, – 62,
–9 and –32, –52, –14; z threshold 3.1, cluster probability P
0.001) (Fig. 2A). A test for quadratic effects did not reveal
differences between the manipulated and unmanipulated faces,
ruling out any potential confound of the manipulation procedure
To further probe our finding in the mesocorticolimbic system, we
extracted the mean BOLD percent signal change from the activated
cluster in the nucleus accumbens, which was significantly higher for
high baby schema compared to unmanipulated and low baby
schema infants (repeated-measures ANOVA F
(2, 14)
12.96, P
0.001; Bonferroni corrected pair-w ise comparisons: high vs. low P
0.001, high vs. unmanipulated P0.05, unmanipulated vs. low ns)
(Fig. 2B).
A psychophysiological interactions (PPI) analysis showed that
signal in the bilateral nucleus accumbens (left, z 4.98, – 8, 12,
8; right, z 4.67, 10, 12, –8) covaried with the signal in the left
fusiform gy rus within the context of increasing baby schema (P
0.0001, uncorrected) (Fig. 3). There was no covariation in the
precuneus and anterior cingulate regions.
Our results are unique in experimental demonstration that baby
schema modulates the mesocorticolimbic system. These findings
suggest a neurophysiologic mechanism by which baby schema
could promote human caregiving. The nucleus accumbens is a
key structure of the mesocorticolimbic system that is linked to
the anticipation of reward (25, 30, 31). Its activation suggests that
baby schema is a positive incentive that provides motivational
drive to caretaking behavior (10). By activating the nucleus
accumbens, baby schema could release approach behavior to-
ward infants (32, 33), which may reflect the urge to hold and
cuddle an infant, as described by Lorenz (1, 2). The striatum, of
which nucleus accumbens is part, has also been associated with
more complex altruistic and affiliative processes, such as mutual
cooperation (34), charitable donation (35, 36), and social bond-
ing (27, 29). The anterior cingulate cortex projects onto the
nucleus accumbens and is activated during reward-based deci-
sion making (37, 38).
Activation of the precuneus, an area associated with attention
(39), suggests that baby schema allocates increased attention
resources to infant faces, which might be reflected in the
attention-capturing effects of infant faces in behavioral tasks
(40). The fusiform gyrus is important to face perception (41, 42),
with a particular role in processing invariant facial features (43).
The fusiform gyrus may perceptually encode baby schema
features in a face and forward this information to the nucleus
accumbens for assignment of motivational value (33, 44). Sup-
port for this conclusion comes from our finding of covariation of
the BOLD fMRI signal between the fusiform gyrus and nucleus
accumbens. This is consistent with an emerging role of the
fusiform gyrus as a major entry node in the ventrally located
extended limbic and prefrontal face network (45–47). The more
dorsally located precuneus and anterior cingulate cortex may
also receive input from visual areas other than the fusiform
gyrus, such as the inferior occipital gyri or superior temporal
sulcus (45). This may explain why we did not observe signal
covariation between the fusiform gyrus and precuneus or ante-
rior cingulate regions.
From an evolutionary perspective, recruitment of ‘‘hard-
wired’’ motivational brain mechanisms in response to baby
schema in nonparents could be adaptive, as human ancestors
likely evolved as cooperative breeders, a social system charac-
terized by the spread of the caretaker role to group members
other than the mother (5). By engaging the mesocorticolimbic
system, baby schema could motivate caring for any infant by any
potential caregiver in a group, regardless of kinship. Our study
cannot determine to what extent the baby schema response is an
evolutionary-selected mechanism brought about by natural se-
lection, and the extent to which it is modulated by cultural
contributions. There is evidence that responsiveness to baby
schema occurs in children (48) as young as 4 months of age (49),
suggesting an evolutionary-shaped basis. However, responsive-
ness to infants is also modulated by experience and learning
(50–52). Such experiences, including exposure to media and
cultural artifacts in the toy industry, may therefore also modulate
the individual reaction to baby schema.
Our study was limited to nulliparous women and the brain
response to baby schema in men, or women in other phases of
the life cycle, may differ. Some fMRI studies on the perception
of nonvisual infantile cues, such as vocalization, demonstrated
sex-dependent activation patterns in brain regions associated
with emotion and motivation (i.e., amygdala and anterior cin-
gulate cortex) (53, 54). Behavioral studies produced inconclusive
results (7, 8, 10–14), but when sex differences were found,
women were usually more responsive to baby schema than men
(7, 8, 10, 12). We recently found that baby schema in infant faces
induces stronger motivation for caretaking in women (10). This
would suggest a stronger mesocorticolimbic response in women,
who are the primary caregivers in most societies (4). However,
in the same study, there were no sex differences in the perception
of cuteness (10), suggesting that men and women may process
baby schema similarly.
Our findings may have also been influenced by perceptual
attributions to the baby schema other than cuteness (1, 2). For
example, in infants attractiveness and cuteness ratings are highly
correlated (55), and attractive infants are rated as more smart,
likeable, healthy, friendly, and cheerful (55, 56), effects that may
themselves be mediated by the baby schema. It is possible that
these attributions covaried with our manipulations of baby
schema in infant faces, contributing to the observed brain
response. While examining the perceptual consequences of the
baby schema in infant faces other than cuteness perception is
outside the realm of this study, this idea could be an interesting
topic for future research.
Fig. 1. Examples of low (narrow face, low forehead, small eyes, big nose and
mouth), unmanipulated, and high (round face, high forehead, big eyes, small
nose and mouth) baby schema faces. (Modified from ref. 10, copyright Black-
well Verlag GmbH.)
www.pnas.orgcgidoi10.1073pnas.0811620106 Glocker et al.
The behavioral generalization of the baby schema response to
the perception of adults, animals, and objects with baby schema
features (12, 18–21) suggests that our neurophysiological find-
ings may extend beyond the female-infant context. Involvement
of the brain substrates for appetitive motivation may explain the
success of high baby schema icons, such as the Teddy Bear (20)
and the Volkswagen Beetle in popular culture.
In conclusion, our findings offer a previously unrecorded
insight into the biological foundations of human caregiving and
provide a neurobiological explanation to why we feel the urge to
care for anything that resembles a baby.
Materials and Methods
Subjects were 16 nulliparous, right-handed women (14 Caucasian, 2 Asian)
aged 20 to 28 years (mean, 24.2). Eleven of the subjects were taking hormonal
contraceptives and 15 were single. All had experience with child care (i.e.,
baby sitting or having a younger sibling). Candidates with psychiatric, neuro-
logical, endocrinological, and cardiovascular disorders were excluded. The study
protocol was approved by the University of Pennsylvania Institutional Review
Board. All subjects gave written informed consent before participating.
fMRI task stimuli were 51 infant faces parametrically manipulated for their
amount of baby schema. Using antropometric (26) and morphing techniques,
we manipulated photographs of 17 Caucasian infants [8 boys and 9 girls aged
7–13 months with a neutral facial expression on black background (10, 11)] to
produce high (round face, high forehead, big eyes, small nose and mouth), low
(narrow face, low forehead, small eyes, big nose and mouth), and unmanipu-
lated baby schema portraits of each infant (see Fig. 1). Techniques and
procedures used to create the baby schema stimuli are reported in detail in
Glocker et al. (10). Briefly, we operationalized baby schema in infant faces
using facial features that comprise the baby schema (1, 2, 6, 9, 11, 13, 57): face
Table 1. Maximally activated voxels in brain regions that respond to infant faces versus crosshair across baby schema levels
(task-related activation)
Hem Volume
Global maxima location Local maxima location Coordinates
Z value
L 2,530 Thalamus (medial dorsal nucleus) 6, 15, 8 14
L Midbrain/red nucleus 2, 27, 2 11.2
L Thalamus (ventral lateral nucleus) 14, 11, 8 13.1
L Caudate head 10, 12, 2 12.7
L Lateral geniculate body 24, 25, 4 12.6
R Midbrain/red nucleus 2, 28, 2 11.2
L 632 Insula (BA 13) 34, 16, 1 13.3
L Superior temporal gyrus (BA 22) 48, 9, 7 9.5
L Claustrum 30, 19, 1 9.46
L Inferior frontal gyrus (BA 47) 32, 19, 6 8.33
L Inferior frontal gyrus (BA 13) 36, 11, 12 8.06
L 869 Superior frontal gyrus (BA 6) 2, 14, 51 10.1
R Cingulate gyrus (BA 32) 4, 21, 38 8.08
R Superior frontal gyrus (BA 6) 12, 14, 47 7.55
L Anterior cingulate (BA 24) 2, 26, 24 7.32
R 5,189 Cerebellum 4, 56, 29 9.95
L Fusiform gyrus (BA 37) 36, 51, 16 9.79
L Cuneus (BA 30) 16, 69, 9 8.78
L 73 Cingulate gyrus (BA 24) 4, 3, 24 9.51
R Cingulate gyrus (BA 24) 6, 1, 22 7.75
R Anterior cingulate (BA 33) 6, 9, 22 5.05
R 1,078 Inferior frontal gyrus (BA 47) 34, 19, 6 8.59
R Insula (BA 13) 28, 25, 3 8.21
R Inferior frontal gyrus (BA 47) 32, 21, 3 8.15
R Insula (BA 47) 38, 15, 6 7.67
R Insula (BA 13) 36, 11, 20 7.45
R Claustrum 36, 4, 5 7.33
L 240 Precuneus (BA 7) 26, 50, 39 7.79
L Inferior parietal lobule (BA 40) 32, 47, 41 6.97
L Superior parietal lobule (BA 7) 28, 54, 39 6.79
L 265 Postcentral gyrus (BA 2) 49, 19, 47 6.61
L Postcentral gyrus (BA 3) 44, 21, 45 6.36
L Inferior parietal lobule (BA 40) 44, 31, 40 6.18
L Postcentral gyrus (BA 2) 40, 25, 40 5.77
L 76 Precentral gyrus (BA 6) 42, 0, 29 6.52
L Inferior frontal gyrus (BA 9) 46, 6, 33 6.41
L Middle frontal gyrus (BA 9) 51, 8, 36 5.08
R 77 Caudate (head) 14, 12, 3 6.36
R Putamen 14, 13, 4 5.77
R Lateral globus pallidius 12, 8, 4 5.51
R Caudate head 14, 16, 1 5.33
R Lateral globus pallidius 12, 6, 3 4.87
R 244 Precuneus (BA 7) 28, 64, 33 6.32
R Superior parietal lobule (BA 7) 24, 62, 40 6.1
R Inferior parietal lobule (BA 40) 36, 43, 41 5.99
aVolumes are given as number of active 3.0 3.44 3.44 mm voxels.
bCoordinates (X,Y,Z) in Talairach space.
Results are family-wise error corrected for multiple comparisons across the whole brain (spatial extent 50 voxels).
Glocker et al. PNAS
June 2, 2009
vol. 106
no. 22
width, forehead height and eye, nose and mouth size. These features were
captured by 6 facial parameters: Absolute face width (fw) in pixels with head
length fixed at 500 pixels and 5 proportion indices: forehead length/face
length (fol/fal); eye width/face width (ew/fw); nose length/head length (nl/hl);
nose width/face width (nw/fw), and mouth width/face width (mw/fw). Using
Photoshop (Adobe Systems), we measured these facial parameters in a sample
of 40 unmanipulated infant photographs [20 boys and 20 girls aged 7–13
months with a neutral facial expression (10, 11)] and calculated the mean
values of each facial baby schema parameter in this sample. Using morphing
software (Morph Age, eX-cinder; Face Filter Studio, Reallusion Inc.), we then
manipulated these facial parameters in 17 infants, randomly selected from the
sample of the 40 unmanipulated infants, to produce high (fw, fol/fal, ew/fw
mean, nl/hl, nw/fw, mw/fw mean) and low (fw, fol/fal, ew/fw mean, nl/hl,
nw/fw, mw/fw mean) baby schema portraits of each infant. The normalized
mean values (z-scores) for each facial parameter from the sample of the 40
unmanipulated infants served as a guide for our manipulations: to maintain
normal facial appearance (26), the range of manipulations for each facial
parameter was restricted to a z-score range of /–2 SD. Manipulated faces
were remeasured and the new facial parameter z-scores calculated. The total
baby schema content within an infant’s face was quantified as the average
facial parameter z-score. This resulted in 17 high (mean total baby schema
z-score 1.0, SD 0.2), 17 low (mean total baby schema z-score –1.1, SD
0.1), and 17 unmanipulated baby schema infant portraits (mean total baby
schema z-score 0, SD 0.3).
During fMRI scan acquisition, participants were presented with a pseudoran-
dom, event-related sequence (optseq2,
optseq) of the low, high, and unmanipulated baby schema faces. Each image was
presented for 3 s, followed by a variable interstimulus interval (6–18 s) during
which a crosshair-fixation point was displayed on a black background (total
543 s). Participants rated each face for cuteness (1 ‘‘not very cute,’’ 2 ‘‘cute,’’
and 3 ‘‘very cute’’) with a button-press using a fiber-optic response pad (FORP
Current Design, Inc.). No stimulus picture was presented twice. Total task dura-
tion was 11 min and 36 s.
BOLD fMRI was acquired with a Siemens Trio 3 Tesla system using a
whole-brain, single-shot gradient-echo echoplanar sequence with the follow-
ing parameters: repetition time/echo time 3,000/30 ms, field of view 220
mm, matrix 64 64, slice thickness/gap 3/0 mm, 40 slices, effective voxel
resolution of 3.4 3.4 3 mm. To reduce partial voluming in orbital frontal
regions, the echoplanar sequence was acquired obliquely (axial/coronal).
Before time-series acquisition, a 5-min magnetization-prepared, rapid acqui-
sition gradient-echo T1-weighted image (MPRAGE, repetition time 1,620 ms,
echo time 3.87 ms, field of view 250 mm, matrix 192 256, effective voxel
resolution of 1 11 mm) was collected for anatomic overlays of functional
data and to aid spatial normalization to a standard atlas space (58).
The fMRI data were subjected to quality control, preprocessing, and sta-
tistical analysis using FEAT (fMRI Expert Analysis Tool) version 5.63, part of
FMRIB’s Software Library. Subject-level preprocessing included slice-time cor-
rection, motion correction to the median image using trilinear interpolation,
high-pass temporal filtering (100 s), spatial smoothing (6 mm FWHM, isotro-
pic), and scaling using mean-based intensity normalization. The median func-
tional image was coregistered to the corresponding high-resolution T1-
weighted structural image and transformed into standard anatomical space
(T1 Montreal Neurological Institute template) using trilinear interpolation.
Transformation parameters were applied to statistical maps before group
analyses. The brain extraction tool was used to remove nonbrain areas.
Subject-level time-series analyses were carried out using FMRIB’s Improved
Linear Model with local autocorrelation correction (59). Voxels showing in-
creased BOLD signal as a function of baby schema level were tested with a
parametric statistical model (60) using orthogonal basis functions up to the
second order. The zero-order term modeled the mean (constant term) effect
of infant faces to the crosshair irrespective of the baby schema level. The
first-order term modeled a parametric linear increase in baby schema level
(1, 0, 1) and a second-order term modeled a quadratic relationship [all
manipulated vs. unmanipulated baby schema (–1, 2, –1)]. The parametric
model used a stepwise forward-model selection approach with 3 sequential
analyses, each with the respective order terms serving as the covariates of
interest. All covariates were convolved with a canonical hemodynamic re-
sponse function before inclusion in the general linear model (GLM). Contrast
maps of the covariates of interest were entered into a group-level 1-sample
T-test. Group Z statistic maps were generated and corrected for spatial extent
using Monte Carlo simulation (AFNI AlphaSim, R.W. Cox, National Institute of
Health) with a minimum z threshold 3.1 and cluster probability 0.001.
Identified clusters were assigned anatomic labels using the Talairach Daemon
To further examine our finding in the mesocorticolimbic system, we ex-
tracted the mean BOLD fMRI percent signal change from the activated cluster
in the nucleus accumbens. Scaled beta coefficients (percent signal change) for
the 3 baby schema levels were estimated using a second single-subject anal-
ysis. The 3 condition events (low, unmanipulated, and high baby schema) were
convolved with canonical hemodynamic response function and modeled,
along with their temporal derivative, in a standard GLM. Mean percent signal
change values were extracted from the significant cluster in the nucleus
accumbens, identified by the linear parametric model (first order) for off-line
analysis and graphic presentation. A repeated-measures ANOVA, where baby
schema was a within-subject factor and mean percent signal change the
outcome variable, was calculated, followed by pair-wise comparisons employ-
ing Bonferroni corrections using SPSS (SPSS Inc.).
An exploratory analysis examined the physiological connectivity between the
left fusiform gyrus [seed region of interest (ROI)] and the target regions identified
by the first-order term of the parametric model described above (precuneus,
anterior cingulate cortex, and nucleus accumbens). A whole-brain PPI analysis
Fig. 2. Brain response to baby schema. (A) Linear increase in activation with increasing baby schema in the left anterior cingulate cortex (ACC; – 4, 24, 38), left
precuneus (PCu; –24, –68, 30), left fusiform gyrus (FG; –42, –62, –9 and –32, –52, –14) and right nucleus accumbens (NAcc; 10, 12, – 8; z threshold 3.1, cluster
probability P0.001). (B) The mean BOLD percent signal change from baseline in the right nucleus accumbens was greatest for high baby schema, followed
by the unmanipulated and low baby schema infants (repeated-measures ANOVA F (2, 14) 12.96, P0.001, Bonferroni corrected pair-wise comparisons *P
0.05 and ***P0.001). Error bars show SEM.
Fig. 3. PPI results showing voxels in the bilateral nucleus accumbens (left, z
4.98, – 8, 12, –8; right, z 4.67, 10, 12, – 8) that covaried with the left fusiform
gyrus seed region within the context of increasing baby schema (P0.0001,
www.pnas.orgcgidoi10.1073pnas.0811620106 Glocker et al.
was implemented in the FMRIB Software Library using procedures described by
Friston et al. (61). Briefly, each subject’s mean preprocessed time-series (see
above) was extracted from the left fusiform gyrus region. This seed region was
identified as the peak cluster in the whole-brain linear model. Similarly, target
regions were identified functionally using this contrast, but at a more liberal
threshold (P0.01, uncorrected) that allowed inclusion of bilateral ROIs for the
precuneus, anterior cingulate cortex, and nucleus accumbens.
Subject-level PPI regressors were generated using the following design
matrix: (i) physiological term (mean time series in the fusiform gyrus); (ii)
psychological term (contrast vector for increasing baby schema convolved
with a canonical hemodynamic response function); (iii) PPI term (time series
contrast vector); and (iv) effect of no interest regressor (unmanipulated baby
schema). The model vectors were orthogonalized such that the PPI regressor
did not correlate with the main effects or nuisance variables. Then, subject-
specific PPI GLM models were run using the PPI regressor and movement
parameters. The contrast image generated for positive PPI indicates the extent
of covariation between the seed ROI (fusiform gyrus) and target brain regions
within the context of the first-order term of the parametric model. Single-
subject PPI images were entered into a group level, 1-sample T-test and the
resulting z statistic image (Gaussianized T) was thresholded at P0.0001,
ACKNOWLEDGMENTS. We thank Dr. Katherine Karraker from the Depart-
ment of Psychology at West Virginia University for providing the original set
of infant photographs. This research was supported by a stipend of the
‘‘Studienstiftung des deutschen Volkes’’ (German National Academic Foun-
dation) (to M.L.G.), and a grant from the Center for Functional Neuroimaging
of the University of Pennsylvania and National Institute of Health Grant
MH-60722 (to R.C.G.).
1. Lorenz K (1943) Innate forms of potential experience. Z Tierpsychol 5:235– 409 (in
2. Lorenz K (1971) Studies in Animal and Human Behavior (Harvard Univ Press, Cam-
bridge, MA).
3. Bowlby J (1969) Attachment and Loss: Vol. 1 Attachment (Hogarth, London).
4. Eibl-Eibesfeldt I (1989) Human Ethology (De Gruyter, New York).
5. Hrdy SB (2005) in Attachment and Bonding, eds Carter CS, et al. (MIT Press, Cambridge),
pp 9–32.
6. Alley TR (1981) Head shape and the perception of cuteness. Dev Psychol 17:650– 654.
7. Alley TR (1983) Infantile head shape as an elicitor of adult protection. Merrill Palmer
8. Alley TR (1983) Growth-produced changes in body shape and size as determinants of
perceived age and adult caregiving. Child Dev 54:241–248.
9. Brooks V, Hochberg J (1960) A psychophysical study of ‘‘Cuteness.’’ Percept Mot Skills
10. Glocker ML, et al. (2009) Baby schema in infant faces induces cuteness perception and
motivation for caretaking in adults. Ethology 115:257–263.
11. Hildebrandt KA, Fitzgerald HE (1979) Facial feature determinants of perceived infant
attractiveness. Infant Behav Dev 2:329–339.
12. Hueckstedt B (1965) Experimental investigations on the baby schema. Z Exp Angew
Psychol 12:421–450 (in German).
13. Sternglanz SH, Gray JL, Murakami M (1977) Adult preferences for infantile facial
features: An ethological approach. Anim Behav 25:108–115.
14. McKelvie SJ (1993) Perceived cuteness, activity level, and gender in schematic baby-
faces. J Soc Behav Pers 8:297–310.
15. Volk A, Quinsey VL (2002) The influence of infant facial cues on adoption preferences.
Hum Nat 13:437–455.
16. Langlois JH, Ritter JM, Casey RJ, Sawin DB (1995) Infant attractiveness predicts maternal
behaviors and attitudes. Dev Psychol 31:464– 472.
17. Tinbergen N (1951) The Study of Instinct (Clarendon Press, Oxford).
18. Zebrowitz LA (1997) Reading Faces: Window to the soul? (Westview Press, Boulder,
19. Fullard W, Reiling AM (1976) An investigation of Lorenz’s ‘‘babyness’’. Child Dev
20. Hinde RA, Barden LA (1985) The evolution of the teddy bear. Anim Behav 33:1371–
21. Gould SJ (1979) Mickey Mouse meets Konrad Lorenz. Nat Hist 88:30–36.
22. Eibl-Eibesfeldt I (1974) Love and Hate: The Natural History of Behavior Patterns
(Schocken, New York).
23. Leibenluft E, Gobbini MI, Harrison T, Haxby JV (2004) Mothers’ neural activation in
response to pictures of their children and other children. Biol Psychiatry 56:225–232.
24. Kringelbach ML, et al. (2008) A specific and rapid neural signature for parental instinct.
PLoS ONE 3:e1664.
25. O’Doherty JP (2004) Reward representations and reward-related learning in the
human brain: insights from neuroimaging. Curr Opin Neurobiol 14:769–776.
26. Farkas LG (1994) Anthropometry of the Head and Face. (Raven, New York).
27. Bartels A, Zeki S (2004) The neural correlates of maternal and romantic love. Neuro-
image 21:1155–1166.
28. Nitschke JB, et al. (2004) Orbitofrontal cortex tracks positive mood in mothers viewing
pictures of their newborn infants. Neuroimage 21:583–592.
29. Strathearn L, Li J, Fonagy P, Montague PR (2008) What’s in a smile? Maternal brain
responses to infant facial cues. Pediatrics 122:40–51.
30. Knutson B, Adams CM, Fong GW, Hommer D (2001) Anticipation of increasing mon-
etary reward selectively recruits nucleus accumbens. J Neurosci 21:RC159.
31. Berns GS, McClure SM, Pagnoni G, Montague PR (2001) Predictability modulates
human brain response to reward. J Neurosci 21:2793–2798.
32. Alcaro A, Huber R, Panksepp J (2007) Behavioral functions of the mesolimbic dopami-
nergic system: an affective neuroethological perspective. Brain Res Rev 56:283–321.
33. Panksepp J (1998) Affective Neuroscience. The Foundations of Human and Animal
Emotions (Oxford Univ Press, New York).
34. Rilling J, et al. (2002) A neural basis for social cooperation. Neuron 35:395–405.
35. Harbaugh WT, Mayr U, Burghart DR (2007) Neural responses to taxation and voluntary
giving reveal motives for charitable donations. Science 316:1622–1625.
36. Moll J, et al. (2006) Human fronto-mesolimbic networks guide decisions about chari-
table donation. Proc Natl Acad Sci USA 103:15623–15628.
37. Bush G, et al. (2002) Dorsal anterior cingulate cortex: a role in reward-based decision
making. Proc Natl Acad Sci USA 99:523–528.
38. Rushworth MF, Behrens TE, Rudebeck PH, Walton ME (2007) Contrasting roles for
cingulate and orbitofrontal cortex in decisions and social behaviour. Trends Cogn Sci
39. Le TH, Pardo JV, Hu X (1998) 4 T-fMRI study of nonspatial shifting of selective attention:
cerebellar and parietal contributions. J Neurophysiol 79:1535–1548.
40. Brosch T, Sander D, Scherer KR (2007) That baby caught my eye. Attention capture by
infant faces. Emotion 7:685–689.
41. Kanwisher N, McDermott J, Chun MM (1997) The fusiform face area: a module in
human extrastriate cortex specialized for face perception. J Neurosci 17:4302–4311.
42. Grill-Spector K, Knouf N, Kanwisher N (2004) The fusiform face area subserves face
perception, not generic within-category identification. Nat Neurosci 7:555–562.
43. Hoffman EA, Haxby JV (2000) Distinct representations of eye gaze and identity in the
distributed human neural system for face perception. Nat Neurosci 3:80– 84.
44. Berridge KC, Robinson TE (1998) What is the role of dopamine in reward: hedonic
impact, reward learning, or incentive salience? Brain Res Brain Res Rev 28:309–369.
45. Haxby JV, Hoffman EA, Gobbini MI (2002) Human neural systems for face recognition
and social communication. Biol Psychiatry 51:59– 67.
46. Fairhall SL, Ishai A (2007) Effective connectivity within the distributed cortical network
for face perception. Cereb Cortex 17:2400–2406.
47. Ishai A (2008) Let’s face it: it’s a cortical network. Neuroimage 40:415–419.
48. Sanefuji W, Ohgami H, Hashiya K (2007) Development of preference for baby faces
across species in humans (Homo sapiens). J Ethol 25:249–254.
49. McCall RB, Kennedy CB (1980) Attention of 4-month infants to discrepancy and
babyishness. J Exp Child Psychol 29:189–201.
50. Frodi AM, Murray AD, Lamb ME, Steinberg J (1984) Biological and social determinants
of responsiveness to infants in 10-to-15-year-old girls. Sex Roles 10:639– 649.
51. Lovejoy MC, Graczyk PA, O’Hare E, Neuman G (2000) Maternal depression and par-
enting behavior: a meta-analytic review. Clin Psychol Rev 20:561–592.
52. Leavitt LA (1998) Mothers’ sensitivity to infant signals. Pediatrics 102:1247–1249.
53. Seifritz E, et al. (2003) Differential sex-independent amygdala response to infant crying
and laughing in parents versus nonparents. Biol Psychiatry 54:1367–1375.
54. Sander K, Frome Y, Scheich H (2007) FMRI activations of amygdala, cingulate cortex,
and auditory cortex by infant laughing and crying. Hum Brain Mapp 28:1007–1022.
55. Stephan CW, Langlois JH (1984) Baby beautiful: Adult attributions of infant compe-
tence as a function of infant attractiveness. Child Dev 55:576–585.
56. Ritter JM, Casey RJ, Langlois JH (1991) Adults’ responses to infants varying in appear-
ance of age and attractiveness. Child Dev 62:68– 82.
57. Enlow D (1982) Handbook of Facial Growth (Saunders, Philadelphia).
58. Talairch J, Tournoux P (1988) Co-planar Stereotaxic Atlas of the Human Brain (Thieme,
New York).
59. Woolrich MW, Ripley BD, Brady M, Smith SM (2001) Temporal autocorrelation in
univariate linear modeling of FMRI data. Neuroimage 14:1370–1386.
60. Buchel C, Holmes AP, Rees G, Friston KJ (1998) Characterizing stimulus-response
functions using nonlinear regressors in parametric fMRI experiments. Neuroimage
61. Friston KJ, et al. (1997) Psychophysiological and modulatory interactions in neuroim-
aging. Neuroimage 6:218–229.
Glocker et al. PNAS
June 2, 2009
vol. 106
no. 22
Full-text available
Machine learning (ML) is a subarea of artificial intelligence which uses the induction approach to learn based on previous experiences and make conclusions about new inputs (Mitchell, Machine learning. McGraw Hill, 1997). In the last decades, the use of ML approaches to analyze neuroimaging data has attracted widening attention (Pereira et al., Neuroimage 45(1):S199–S209, 2009; Lemm et al., Neuroimage 56(2):387–399, 2011). Particularly interesting recent applications to affective and social neuroscience include affective state decoding, exploring potential biomarkers of neurological and psychiatric disorders, predicting treatment response, and developing real-time neurofeedback and brain-computer interface protocols. In this chapter, we review the bases of the most common neuroimaging techniques, the basic concepts of ML, and how it can be applied to neuroimaging data. We also describe some recent examples of applications of ML-based analysis of neuroimaging data to social and affective neuroscience issues. Finally, we discuss the main ethical aspects and future perspectives for these emerging approaches.
Full-text available
Several studies have demonstrated sex differences in empathy and social abilities. This chapter reviews studies on sex differences in the brain, with particular reference to how women and men process faces and facial expressions, social interactions, pain of others, infant faces, faces in things ( pareidolia ), living vs. non-living information, purposeful actions, biological motion, erotic vs. emotional information. Sex differences in oxytocin-based attachment response and emotional memory are also discussed. Overall, the female and male brains show some neuro-functional differences in several aspects of social cognition, with particular regard to emotional coding, face processing and response to baby schema that might be interpreted in the light of evolutionary psychobiology.
Full-text available
Embodiment has been discussed in the context of social, affective, and cognitive psychology, and also in the investigations of neuroscience in order to understand the relationship between biological mechanisms, body and cognitive, and social and affective processes. New theoretical models have been presented by researchers considering not only the sensory–motor interaction and the environment but also biological mechanisms regulating homeostasis and neural processes (Tsakiris M, Q J Exp Psychol 70(4):597–609, 2017). Historically, the body and the mind were comprehended as separate entities. The body was considered to function as a machine, responsible for providing sensory information to the mind and executing its commands. The mind, however, would process information in an isolated way, similar to a computer (Pecher D, Zwaan RA, Grounding cognition: the role of perception and action in memory, language, and thinking. Cambridge University Press, 2005). This mind and body perspective (Marmeleira J, Duarte Santos G, Percept Motor Skills 126, 2019; Marshall PJ, Child Dev Perspect 10(4):245–250, 2016), for many years, was the basis for studies in social and cognitive areas, in neuroscience, and clinical psychology.
Full-text available
Emotions modulate behavioral priorities via central and peripheral nervous systems. Understanding emotions from the perspective of specific neurotransmitter systems is critical, because of the central role of affect in multiple psychopathologies and the role of specific neuroreceptor systems as corresponding drug targets. Here, we provide an integrative overview of molecular imaging studies that have targeted the human emotion circuit at the level of specific neuroreceptors and transmitters. We focus specifically on opioid, dopamine, and serotonin systems, given their key role in modulating motivation and emotions, and discuss how they contribute to both healthy and pathological emotions.Keywords Molecular imaging Human emotions Dopamine systemSerotonin systemOpioid system
Full-text available
Emotions play a very important role in moral judgments. Hume argues that morality is determined by feelings that make us define whether an attitude is virtuous or criminal. This implies that an individual relies on their past experience to make a moral judgment, so that when the mind contemplates what it knows, it may trigger emotions such as disgust, contempt, affection, admiration, anger, shame, and guilt (Hume D. An enquiry concerning the principles of morals, 1777 ed. Sec. VI, Part I, para, 196, 1777). Thus, even so-called “basic” emotions can be considered as moral emotions. As Haidt (The moral emotions. In: Handbook of affective sciences, vol 11, 852–870, Oxford University Press, 2003) points out, all emotional processing that leads to the establishment and maintenance of the integrity of human social structures can be considered as moral emotion. Consequently, the construct of “morality” is often characterized by a summation of both emotion and cognitive elaboration (Haidt J. Psychol Rev, 108(4):814, 2001).
Full-text available
Social cognition refers to a wide range of cognitive abilities that allow individuals to understand themselves and others and also communicate in social interaction contexts (Adolphs, Curr Opin Neurobiol 11(2):231–239, 2001). According to Adolphs (Annu Rev Psychol 60(1):693–716, 2009), social cognition deals with psychological processes that allow us to make inferences about what is happening inside other people—their intentions, feelings, and thoughts. Although the term can be defined in many ways, it is clear that it must be safeguarded for the mental operations underlying social interactions. The most investigated cognitive processes of social cognition are emotion recognition and theory of mind (ToM), given that a whole range of socio-affective and interpersonal skills, such as empathy, derive from them (Mitchell RL, Phillips LH, Neuropsychologia, 70:1–10, 2015). Theory of mind is an intuitive ability to attribute thoughts and feelings to other people, and this ability usually matures in children in preschool age (Wellman HM, The child’s theory of mind. Bradford Books/MIT, 1990), whereas emotional recognition refers to an individual’s ability to identify others’ emotions and affective states, usually based on their facial or vocal expressions, it is a critical skill that develops early and supports the development of other social skills (Mitchell RL, Phillips LH, Neuropsychologia, 70:1–10, 2015).
Full-text available
Transcranial brain stimulation (TBS) is a term that denotes different noninvasive techniques which aim to modulate brain cortical activity through an external source, usually an electric or magnetic one. Currently, there are several techniques categorized as TBS. However, two are more used for scientific research, the transcranial magnetic stimulation (TMS) and the transcranial direct current stimulation (tDCS), which stimulate brain areas with a high-intensity magnetic field or a weak electric current on the scalp, respectively. They represent an enormous contribution to behavioral, cognitive, and social neuroscience since they reveal how delimited brain cortical areas contribute to some behavior or cognition. They have also been proposed as a feasible tool in the clinical setting since they can modulate abnormal cognition or behavior due to brain activity modulation. This chapter will present the standard methods of transcranial stimulation, their contributions to social and affective neuroscience through a few main topics, and the studies that adopted those techniques, also summing their findings.
Full-text available
No matter how hard you try—pinching different parts of your body, slapping your face, or moving restlessly in your seat—you cannot prevent your mind from occasionally escaping from the present experience as you enter into a mental navigation mode. Sometimes spontaneously, others deliberately, your mind may move to a different time—you may see yourself running an experiment inspired by the chapter you just finished reading or you may imagine yourself on a quantum leap into the future as you fantasize about the delivery of your Nobel Prize acceptance speech. Your mind may move to a distinct space, for example, as you replay last weekend’s party or anticipate a most desirable date, and may even venture into the mind of another (e.g., as you embody the mind of the author you are currently reading). Our minds can accomplish all this mental navigation in fractions of a second, allowing us to see ourselves or even impersonate different people across space and time. While teleportation and time travel may never be physically possible, our wandering minds are indeed very accomplished “time machines” (Suddendorf T, Corballis MC, Behav Brain Sci 30(3), 2007).
Full-text available
This chapter provides information about facial electromyography (EMG) as a method of investigating emotions and affect, including examples of application and methods for analysis. This chapter begins with a short introduction to emotion theory followed by an operationalisation of facial emotional expressions as an underlying requirement for their study using facial EMG. This chapter ends by providing practical information on the use of facial EMG.KeywordsElectromyographyFacial EMGFacial emotional expressionsFacial muscles
Full-text available
The neurocognitive mechanism of emotion without conscious awareness has long been a subject of great interest (Pribram KH, Gill MM, Freud’s “project” re-assessed: preface to contemporary cognitive theory and neuropsychology. Basic Books, 1976). Several pervious psychological studies have used subliminal presentations of emotional facial expressions in the context of the affective priming paradigm to investigate unconscious emotional processing (e.g., Murphy ST, Zajonc RB, J Person Soc Psychol 64:723–739, 1993; for a review, see Eastwood JD, Smilek D, Conscious Cognit 14:565–584, 2005). In a typical application of this paradigm, a facial expression depicting a negative or positive emotion is flashed briefly as a prime, then an emotionally neutral target (e.g., an ideograph) is presented. Participants are asked to make emotion-related judgments about the target. The studies reported that evaluations of the target were negatively biased by unconscious negative primes, compared to positive primes. This effect has been interpreted as evidence that unconscious emotion can be elicited and that it affects the evaluation of unrelated targets.
What roles do mesolimbic and neostriatal dopamine systems play in reward? Do they mediate the hedonic impact of rewarding stimuli? Do they mediate hedonic reward learning and associative prediction? Our review of the literature, together with results of a new study of residual reward capacity after dopamine depletion, indicates the answer to both questions is 'no'. Rather, dopamine systems may mediate the incentive salience of rewards, modulating their motivational value in a manner separable from hedonia and reward learning. In a study of the consequences of dopamine loss, rats were depleted of dopamine in the nucleus accumbens and neostriatum by up to 99% using 6-hydroxydopamine. In a series of experiments, we applied the 'taste reactivity' measure of affective reactions (gapes, etc.) to assess the capacity of dopamine-depleted rats for: 1) normal affect (hedonic and aversive reactions), 2) modulation of hedonic affect by associative learning (taste aversion conditioning), and 3) hedonic enhancement of affect by non-dopaminergic pharmacological manipulation of palatability (benzodiazepine administration). We found normal hedonic reaction patterns to sucrose vs. quinine, normal learning of new hedonic stimulus values (a change in palatability based on predictive relations), and normal pharmacological hedonic enhancement of palatability. We discuss these results in the context of hypotheses and data concerning the role of dopamine in reward. We review neurochemical, electrophysiological, and other behavioral evidence. We conclude that dopamine systems are not needed either to mediate the hedonic pleasure of reinforcers or to mediate predictive associations involved in hedonic reward learning. We conclude instead that dopamine may be more important to incentive salience attributions to the neural representations of reward-related stimuli. Incentive salience, we suggest, is a distinct component of motivation and reward. In other words, dopamine systems are necessary for 'wanting' incentives, but not for 'liking' them or for learning new 'likes' and 'dislikes'.
A set of four facial stimuli derived from the Bolton standards of craniofacial development representing a human male at 6 months, 3, 8, and 18 years of age were used in a test of Lorenz's concept of babyishness and of the discrepancy hypothesis. Each 4-month-old subject was habituated to a criterion with one of the four stimuli and then presented with one of the four as a new stimulus. The design and analysis permitted the response to a new stimulus to be broken down into a component attributable to the physical characteristics of the new stimulus and a part attributable to its discrepancy from the familiar standard. The data revealed longer looking at the infant facial stimulus, but no difference in a rating of affect accompanying fixation. This lent partial support to the babyishness concept for infant subjects. Both fixation and affect increased monotonically with magnitude of discrepancy. The increasing rather than curvilinear result presumably derived from the failure of these stimuli (which were common to the infant's experience) to generate extreme levels of subjective uncertainty.