Cerebral Cortex March 2010;20:612--621
Advance Access publication June 25, 2009
Regional Response Differences Across the
Human Amygdaloid Complex during Social
F. Caroline Davis1, Tom Johnstone2, Emily C. Mazzulla3,
Jonathan A. Oler4and Paul J. Whalen1
1Department of Psychological & Brain Sciences, Dartmouth
College, Hanover, NH 03755, USA,2School of Psychology and
Clinical Language Sciences, University of Reading, Earley Gate,
Whiteknights, Reading RG6 6AL, UK,3Department of
Psychology, University of Vermont, Burlington, VT 05405, USA
and4Department of Psychiatry, University of Wisconsin-
Madison, Madison, WI 53719, USA
The amygdala is consistently implicated in biologically relevant
learning tasks such as Pavlovian conditioning. In humans, the
ability to identify individual faces based on the social outcomes
they have predicted in the past constitutes a critical form of
associative learning that can be likened to ‘‘social conditioning.’’ To
capture such learning in a laboratory setting, participants learned
about faces that predicted negative, positive, or neutral social
outcomes. Participants reported liking or disliking the faces in
accordance with their learned social value. During acquisition, we
observed differential functional magnetic resonance imaging
activation across the human amygdaloid complex consistent with
previous lesion, electrophysiological, and functional neuroimaging
data. A region of the medial ventral amygdala and a region of the
dorsal amygdala/substantia innominata showed signal increases to
both Negative and Positive faces, whereas a lateral ventral region
displayed a linear representation of the valence of faces such that
Negative > Positive > Neutral. This lateral ventral locus also
differed from the dorsal and medial loci in that the magnitude of
these responses was more resistant to habituation. These findings
document a role for the human amygdala in social learning and
reveal coarse regional dissociations in amygdala activity that are
consistent with previous human and nonhuman animal data.
Keywords: arousal, dorsal amygdala, fMRI, habituation, valence, ventral
Studies of Pavlovian conditioning in both nonhuman animals
(LeDoux 1996; Davis and Whalen 2001) and humans (Buchel
et al. 1998, 1999; LaBar et al. 1998; Morris et al. 2001; Gottfried
et al. 2002; Phelps et al. 2004) suggest that the amygdala plays
an important role in learning the predictive value of biologically
relevant stimuli. Facial expressions of emotion have also been
shown to elicit increases in amygdala activity, presumably
because they are salient, biologically relevant stimuli that have
predicted important events in our environment (Whalen 1998).
Indeed, the amygdala is more responsive to facial expressions
embedded within an associative learning context than when
presented in isolation (Hooker et al. 2006). Moreover, patients
with bilateral amygdala damage judge strangers as being more
approachable and trustworthy than controls (Adolphs et al.
1998) suggesting that the amygdala plays an important role in
representing the social reinforcement value of other individ-
uals (Adolphs 2001). Given that we form preferences for other
people based on our previous experiences with them, the goal
of the present study was to characterize the human amygdala’s
role in social conditioning, defined here as the associative
process whereby we learn to identify individuals that have
predicted threats or rewards in the past.
Work in nonhuman animals suggests that subnuclei within
the amygdaloid complex differentially contribute to associa-
tive learning (see Discussion), and functional magnetic
resonance imaging (fMRI) research has begun to use
functional response profiles to spatially dissociate regions of
the human amygdaloid complex. For example, Morris et al.
(2001) showed that although responses within a ventral
region of the amygdala did not habituate across trials during
a traditional Pavlovian conditioning study, responses in
a dorsal region did. These findings could be consistent with
animal data showing that although some cells in the amygdala
show early and transient increases in activity during learning,
other cells maintain sustained representations of conditioned
stimuli (Repa et al. 2001; Radwanska et al. 2002). In a separate
line of inquiry, Whalen and colleagues reported that a lateral
ventral portion of the human amygdala showed the greatest
responses to facial expressions communicating negative
valence, whereas responses in a medial ventral and a dorsal
amygdala region were equally responsive to facial expressions
communicating both positive and negative valence (Kim et al.
2003). These data were interpreted to suggest that different
portions of the human amygdaloid complex show greater
specialization for processing valence versus arousal, respec-
Taken together, these studies predict that it should be
possible to dissociate regional differences in responses across
the human amygdaloid complex during learning at routinely
utilized fMRI spatial resolutions. In this experiment, we
monitored these regional differences in amygdala response
during a social conditioning paradigm. We chose this modifi-
cation of more traditional Pavlovian conditioning paradigms for
several reasons. Our primary aim was to relate an extensive
literature on the amygdala’s role in associative learning tasks
(i.e., Pavolovian conditioning) to a growing literature that
implicates the amygdala in human social and emotional
learning (Ohman and Mineka 2001; Hooker et al. 2006; Olsson
et al. 2007; Olsson and Phelps 2007; Schiller et al. 2009). In
humans, the ability to identify individual faces based on the
social outcomes they have predicted in the past constitutes
a critical form of association that, at a very basic level, mirrors
other learning processes such as associating certain cues with
biologically relevant outcomes. Moreover, the social reinforcers
employed in the current paradigm allow us to manipulate
valence while controlling for arousal. This is more difficult to
do in traditional Pavlovian learning studies because primary
reinforcers (such as the presence or absence of shock) elicit
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different levels of emotional arousal, making it difficult to
dissociate amygdala response to valence vs. arousal.
During fMRI scanning, participants viewed faces that
predicted either negative, positive, or neutral social out-
comes. We predicted that dorsal and medial ventral regions
would show signal increases to faces predicting negative and
positive outcomes, consistent with previous human (Breiter
et al. 1996; Whalen 1998; Kim et al. 2003; Somerville et al.
2006; Hare et al. 2008; Levita et al. 2009) and nonhuman
animal studies (Gallagher et al. 1990; Baxter and Murray
2002; Paton et al. 2006). Additionally, we predicted that
a lateral ventral region of the amygdala would differentially
represent the learned valence of faces, with greatest
sensitivity to faces predicting negative social outcomes
(Repa et al. 2001; Radwanska et al. 2002; Kim et al. 2003).
Finally, because human amygdala fMRI responses tend to
habituate over time (Buchel et al. 1998; LaBar et al. 1998;
Buchel et al. 1999; Morris et al. 2001; Phelps et al. 2001;
Cheng et al. 2007; Hare et al. 2008), and animal data suggest
it is possible to detect both habituating and nonhabituating
responses within the amygdala (Repa et al. 2001; Radwanska
et al. 2002), we also assessed variations in habituation rates
within these regions of the amygdaloid complex.
Materials and Methods
Forty-seven healthy adult participants were recruited for this exper-
iment. Three participants were excluded because of technical
problems during data collection and 2 were excluded based on
excessive head motion. Thus, 42 healthy adult participants (21 males,
mean age: 24.3 ± 3.96) were included in this analysis. All subjects were
right-handed as assessed by the Edinburgh Handedness Inventory
(Oldfield 1971) and free from psychiatric, neurological, and medical
illness. Psychiatric history was assessed using an abbreviated version of
the Non-Patient edition of the Structured Clinical Interview for DSM
Disorders (First et al. 1995), which assessed for current and past history
of major depressive disorder, dysthymia, hypomania, bipolar disorder,
specific phobia, social anxiety disorder, generalized anxiety disorder,
and obsessive compulsive disorder. Neurological and medical histories
were assessed through self-report. This investigation was conducted in
accordance with the guidelines of the Human Subjects Committee of
the University of Wisconsin-Madison.
Visual stimuli were presented using E-Prime software (Psychology
Software Tools, Pittsburgh, PA) through a fiber-optic goggle system
(AVOTEC, Stuart, FL) mounted on a quadrature head coil. Face stimuli
(Ekman and Friesen 1976) consisted of 3 male faces with neutral
facial expressions (Identities: G.S., J.J., P.E.). Sentence stimuli were
derived from an independent pilot study in which participants rated
the valence (–4, very negative to +4, very positive) and arousal (1, ‘‘the
least amount of emotional arousal I have ever felt’’ to 9, ‘‘the greatest
amount of emotional arousal I have ever felt’’) of self-relevant
sentences. Based on these ratings, we selected 4 negative (e.g., He
thinks you’re stupid), 4 positive (e.g., He thinks you’re smart), and 4
neutral (e.g., He thinks your shirt is blue) sentences (see Supple-
mentary Materials) that were matched on several dimensions. The
Negative and Positive sentences were matched for opposing content
(i.e., stupid vs. smart), and were not significantly different in the
absolute mean value of valence (Negative: –2.12 ± 0.30; Positive: 2.21 ±
0.05; t(6) = 0.545, P >0.05) or arousal (Negative: 4.44 ± 0.29; Positive:
4.48 ± 0.16; t(6) = 0.245, P >0.05). The Neutral sentences were rated
as neither negative nor positive (valence: 0.17 ± 0.07) and were less
emotionally arousing than both Negative and Positive sentences
(arousal: 1.81 ± 0.14).
Participants initially viewed each face 20 times in a pseudorandomized
order and separated by a fixation crosshair on an otherwise blank screen.
This was done to acclimate subjects to the ‘‘to be conditioned’’ faces.
Next, each face identity was paired with one category of self-relevant
sentences (Negative, Positive, Neutral) and these pairs were presented
in a pseudorandomized order. The face presentation always preceded
the sentence presentation. Each of the 4 face--sentence pairings per
valence was repeated 3 times, for a total of 12 trials per condition. Thus,
one individual face always predicted negative social outcomes (i.e.,
insults), another always predicted positive social outcomes (i.e.,
compliments), whereas the third always predicted neutral social
outcomes. Face identity--sentence category pairings were counter-
balanced across participants. Participants were told that they would see
a series of faces and sentences, and that each sentence provided
information about the preceding face. Participants were not required to
make any responses during this learning phase.
In each trial, a face was presented for 1 s, followed by a sentence
presented for 2 s (Fig. 1). The interval between the onset of the face
(CS) and the onset of the sentence (US) varied between 2.5 and 3.5 s
(mean = 3.0 s), and the interval between the onset of the sentence and
the onset of a face in the next trial varied between 4.5 and 5.5 s (mean =
5.0 s). This ‘‘jittered’’ timing allowed us to separately model overlapping
hemodynamic responses to face and sentence stimuli (Buckner et al.
1996; Friston et al. 1999).
To document that subjects associated the intended social value with
the face identities, subjects viewed each face and provided likeability
and arousal ratings after each phase (i.e., Orienting, Conditioning) of
the task. Each face was presented for 1 s in the absence of the
sentences, followed by a rating screen asking, ‘‘How much do you like
this person?’’ (–4, ‘‘not at all’’ to +4 ‘‘very much’’) and ‘‘How much
emotional arousal do you feel when looking at this face?’’ (1, ‘‘the least
amount of emotional arousal I have ever felt’’ to 9 ‘‘the most amount of
emotional arousal I have ever felt’’).
fMRI Image Acquisition
Images were acquired on a 3.0 Tesla MRI scanner (General Electric
Signa; Waukesha, WI) with high speed imaging gradients and
a quadrature head coil. Anatomical images were whole brain high-
resolution T1-weighted scans (3D Inversion Recovery [IR]; 256 3 256
in-plane resolution; 240-mm field of view (FOV); 124 3 1.1 mm axial
slices). Functional scans consisted of an echo planar sequence with
a 2000-ms repetition time, 33-ms echo time, and a 60? flip angle with 18
contiguous 3-mm-thick interleaved coronal slices (0.5-mm interslice
gap; 64 3 64 in-plane resolution, 180-mm FOV). Due to our a priori
focus on the amygdala, slices were centered over the medial temporal
lobe, covering most of the frontal cortex (missing only the most
anterior aspects of the frontal pole) and temporal cortex (including the
amygdala and hippocampus), but did not include the majority of the
parietal and occipital cortices. This acquisition scheme utilizes a voxel
size that strikes a balance between adequate signal to noise ratio while
reducing voxel-dephasing which contributes to susceptibility signal
dropout. These parameters were based on our previous studies
demonstrating strong functional signal reliability (Johnstone et al.
Figure 1. Experimental paradigm. In a single trial, participants viewed each face for
1 s, followed by a variable interstimulus interval, and then a sentence for 2 s. Trials
were separated by a variable intertrial interval averaging 3 s.
Cerebral Cortex March 2010, V 20 N 3 613
2005) and high signal to noise ratios (Whalen et al. 2004) in the
Behavioral Data Analysis
To assess whether subjects learned about the predictive nature of the 3
neutral face identities, we computed a within subjects ANOVA
(Condition (Negative, Positive, Neutral) 3 Time (Before conditioning,
After conditioning) on self-reported likeability and arousal ratings of
the faces. Because subjects provided these ratings both before and after
the conditioning phase of the experiment, we were able to examine
changes due to learning. Because the faces all had neutral facial
expressions, we predicted no differences in either arousal or likeability
before conditioning, but specific differences in these ratings after
conditioning. That is, we predicted that after conditioning subjects
would report not liking the Negative faces, liking the Positive faces, and
have no real preference for the Neutral faces. In addition, we predicted
that after conditioning subjects would rate both the Negative and
Positive faces as more arousing than the Neutral faces. Such changes
would be consistent with pilot ratings of the sentences, and would
indicate that subjects successfully imbued the individual faces with the
social value associated with the sentences.
fMRI Data Analysis
All fMRI data analyses were performed using AFNI (Cox 1996). Raw
functional blood-oxygen-level--dependent (BOLD) images were slice
time-corrected, motion-corrected, and smoothed using a Gaussian
kernel of 6-mm full width at half maximum (FWHM). Based on the
matched filter theorem the smoothing kernel was chosen to match the
size of expected activations as small as approximately 100--200 mm3,
which corresponds to a sphere of diameter 6--7 mm. Subsequent
measurement of the resulting spatial smoothness of data yielded
a FWHM of 7 mm. All subjects included in these analyses yielded
estimated head motion of less than 1.5 mm.
A general linear model was then used to estimate each regressor’s
unique contribution to variance. Twelve regressors of interest were
included in the model. For both early (first half) and late (last half) trials,
we separately modeled Negative, Positive, and Neutral faces, as well as
Negative, Positive, and Neutral sentences, using a train of stimulus
square waves convolved with a gamma-variate ideal hemodynamic
response function (see Supplementary Materials for a detailed discus-
sion of our ability to separate responses to the faces and sentences).
Individual motion estimates were included in the general linear model
as 6 covariates of no interest to control for variability in the signal that
was highly correlated with head motion (Friston et al. 1996; Johnstone
et al. 2006). We calculated a separate baseline comprising the fixation
periods between trials and those at the beginning and end of each
functional run using a second degree polynomial to allow for slow
signal drift over the course of the run.
Contrast images were then transformed into percent signal change
maps and spatially normalized into standard space for group analysis.
Spatial normalization was performed using AFNI software according to
Cox (1996) by manually identifying 11 landmarks on each subject’s
high-resolution image, then using a linear interpolation algorithm to
transform the images to Talairach space (Talairach and Tournoux
1988). Functional images aligned with the anatomical images were
warped to Talairach space and resampled to 2 mm 3 2 mm 3 2 mm
voxels using parameters from the anatomical transformation, and were
then masked with a 3-dimensional binary volume created from the
subject-averaged baseline signal in order to remove activation in
regions without strong signal in all subjects.
Drawing from previous human and nonhuman animal studies, we
performed planned a priori voxelwise contrasts based upon 3 specific
Hypothesis 1: Regions of the amygdala will show differential
habituation rates. Numerous studies show that human amygdala fMRI
responses tend to habituate over time (Buchel et al. 1998, 1999; LaBar
et al. 1998; Morris et al. 2001; Phelps et al. 2001; Cheng et al. 2007; Hare
et al. 2008), and animal data suggest that it should also be possible to
detect responses that do not habituate within the lateral ventral
amygdala (Repa et al. 2001; Medina et al. 2002; Radwanska et al. 2002).
Therefore, all data were contrasted as Early versus Late responses to
capture any differences over time, a strategy that has proven useful in
previous neuroimaging studies across numerous laboratories (e.g.,
Buchel et al. 1998, 1999; Cheng et al. 2007; LaBar et al. 1998; Morris
et al. 2001; Phelps et al. 2001).
We chose this strategy of modeling Early vs. Late trials compared
with specifically modeling time-related changes as a covariate in order
to increase our ability to detect gross changes over time. This strategy
reduces our ability to detect subtle individual differences in habituation
rates. However, BOLD response in the amygdala is not sufficiently
robust to permit modeling of nonlinear trends on a trial-by-trial basis
due to the inherently low signal to noise ratio in this region (Johnstone
et al. 2005). Averaging over 6 trials increases reliability and allows us to
detect gross changes in activity Early in learning as compared with Late
in learning. In addition, modeling time-related change as a covariate is
especially advantageous when decreases in amygdala activation are
uniformly linear (for a linear covariate) or otherwise conform to an
a priori nonlinear model. We do not necessarily expect to see a linear
decrease in activity over time because it is possible that activity in the
dorsal and ventral medial amygdala will remain flat or even increase
while subjects are learning the contingencies between stimuli, but that
once these contingencies are learned, activity will begin to habituate.
Moreover, because the dorsal and ventral medial subregions will likely
represent different aspects of learning, we would not expect
habituation patterns in these areas to show similar response patterns
(e.g., response habituation consistent with some additional measure of
learning such as electrodermal activity).
Hypothesis 2: Areas of the dorsal amygdala/substantia innomi-
nata region will show similar sensitivity to negatively and positively
valenced stimuli. We tested this using the contrast Negative and
Positive > Neutral, based upon studies showing that the amygdala is
responsive to both negatively and positively valenced stimuli (Breiter
et al. 1996; Whalen 1998; Kim et al. 2003), and that this sensitivity to
negative and positive stimuli is increased compared with the amygdala’s
response to neutral stimuli (Somerville et al. 2006).
Hypothesis 3: The ventral region of the human amygdala will
show greatest sensitivity to negatively valenced stimuli. We tested
this hypothesis using 2 linear contrasts: The first contrast assessed
amygdala responses modeled as Negative >Positive >Neutral, and was
based upon aversive conditioning data in animals showing that
amygdala responses to a CS+ that faithfully predicts shock are greater
than those to a CS– that predicts the absence of shock, and that activity
during CS– presentations is greater than the pretrial baseline periods.
Seligman (1968) and Rescorla (1969) have suggested that the CS– is
usefully considered a positive stimulus because it signals the absence of
shock (see also Ohman 2009). Thus, our prediction of Negative >
Positive > Neutral is based upon the observations that 1) in animal
conditioning studies amygdala activity also increases to the positive CS–,
but to a lesser degree than that observed to the CS+ (see e.g., Kapp et al.
1992) and 2) numerous human neuroimaging studies document signal
increases in the amygdala to positively valenced (compared with
neutral stimuli) that are of a lower magnitude than those observed to
negative stimuli (e.g., Breiter et al. 1996; Whalen et al. 1998; Fitzgerald
et al. 2006). Although the Negative > Positive > Neutral contrast was
the focus of this study of conditioning, other human neuroimaging data
show that when human neural responses to facial expressions are
assessed, amygdala activity can show a Negative > Neutral > Positive
profile (Kim et al. 2003). For completeness, we also assessed this linear
contrast within the current study design.
Evaluation of Gender Effects
In order to ensure that the observed effects did not include gender
differences, we computed post hoc t-tests to determine whether there
were any significant differences between males and females for each of
our planned contrasts.
Relationship between Neural Activity and Self-Report Measures
In order to determine whether individual differences in neural
activity during conditioning predicted subsequent likeability and
emotional arousal reports, we computed contrasts on likeability and
arousal data that mirrored the contrasts that identified amygdala
Amygdala Responses during Social Conditioning
Davis et al.
activity based upon valence (lateral ventral amygdala; Negative >
> Neutral) and arousal (dorsal amygdala; Negative and
Positive > Neutral). These were then correlated with Regions of
interest (ROIs) extracted from the voxelwise statistical maps as
ROIs were extracted from voxelwise statistical maps calculated
using the contrasts outlined above. All amygdala clusters in the
reported results survive statistical thresholding at P <0.05, corrected
for multiple comparisons, stipulated by Monte Carlo simulations using
the AFNI program Alphasim. To determine a correction threshold
appropriate to our a priori ROI, we calculated a search volume of
3500 mm3as measured in Talairach space in the Mai et al. (2004)
atlas. This bilateral volume comprises BLA (lateral and basal nuclei) as
well as the central nucleus as it extends into the substantia
innominata (SI) region of the ventral basal forebrain and intermingles
with other neuronal groups (e.g., nucleus basalis of Meynert; NBM).
We included within this search volume the portion of the SI that is
contiguous and immediately dorsal and medial to the central nucleus,
where 1) NBM is located and 2) we have previously observed
activation to faces (Kim et al. 2003). Thus, we are using the same
correction volume defined in previous studies from our laboratory
(Kim et al. 2003, 2004; Whalen et al. 2004) and peak activations
observed in the present study were within the confines of this search
volume (see Supplementary Fig. 1 for anatomical depictions of these
Because we had strong a priori hypotheses regarding patterns of
activation in different subregions of the amygdala, careful anatomical
localization was performed by examining the clusters obtained from
group data in relation to each individual subjects’ anatomy. First, the
spatial location of each ROI was verified by comparing the Talairach
coordinates of both the peak voxel and cluster boundaries to an atlas in
standard space (Mai et al. 2004). Each ROI was also overlaid on an
averaged anatomical image as well as each subject’s individual high-
resolution anatomical image. These images were compared with the
atlas in order to ensure anatomical consistency between individual data,
group data, and standardized atlas images.
Likeability and arousal ratings of the faces collected before and
after conditioning (see Methods) showed that subjects learned
about the predictive nature of the 3 neutral face identities. A
within subjects ANOVA (Condition (Negative, Positive, Neu-
tral) X Time (Before conditioning, After conditioning) showed
a significant interaction between Condition and Time for both
types of ratings (Arousal: F2,82= 4.433, P < 0.05; Likeability:
F2,82 = 3.176, P
< 0.05). Univariate ANOVAs (Condition:
Negative, Positive, Neutral) revealed no difference in ratings
for either likeability (F2,123= 1.448, P >0.20) or arousal (F2,123=
> 0.40) before conditioning. As predicted, after
conditioning both Negative (t (41) = 2.595, P = 0.01) and
Positive (t (41) = 2.760, P < 0.01) faces were rated as more
emotionally arousing than the Neutral faces (mean Negative =
3.79, Positive = 3.64, Neutral = 3.14). There was no significant
difference in arousal ratings for the faces that predicted
negative and positive social outcomes (t (41) = –0.771, P >
0.05). A univariate (Condition: Negative, Positive, Neutral)
ANOVA revealed a significant effect of Condition (F2,123 =
23.367, P < 0.001) in the postconditioning likeability ratings.
In accordance with our predictions, subjects disliked faces
predicting Negative social outcomes (–1.10 ± 1.76), liked the
faces predicting positive social outcomes (1.02 ± 1.37), and
showed no strong preference toward the faces predicting
neutral social outcomes (0.26 ± 1.11) (Fig. 2).
Responses to Faces
Greater activity for Early compared with Late trials during
learning was observed in the medial basal amygdala (Fig. 3A).
Voxels within this cluster responded to all stimulus conditions
(Negative, Positive & Neutral) initially, and these responses
returned to baseline during the last half of conditioning. The
peak voxel (x = 19, y = –5, z = –19) maps to the BLA, and the
cluster extends primarily over the medial portion of the BLA
where the basal nuclei are located (Mai et al. 2004). Visual
inspection of the placement of this cluster in terms of each
individual’s anatomy showed that this mapping was consistent
across all 42 subjects (i.e., the cluster did not extend outside
amygdala and extended over the medial aspects of the
amygdala in all subjects).
Increased activity to Negative and Positive stimuli compared
with Neutral stimuli was observed in the dorsal amygdala/SI
region (Fig. 3B) where the superior border of the temporal lobe
meets the ventral border of the basal forebrain. The peak voxel
(x = 13, y = –6, z = –5) maps to an area just dorsal to the central
and medial nuclei of the amygdala (Mai et al. 2004), in an area
where scattered congregations of sublenticular extended
amygdala (SLEA) neurons as well as nucleus basalis of Meynert
(NBM) neurons are also known to be located (see Discussion).
Visual inspection of the placement of this cluster in terms of
each individual’s anatomy showed a consistent spatial profile
within all 42 subjects (i.e., the cluster does not cross into the
immediately superior globus pallidus in any subject). Paired t-
tests on average values extracted from this ROI revealed no
difference between dorsal amygdala/SI activity to the Negative
compared with Positive faces during both Early (t (41) = –0.025,
P = 0.98) and Late (t (41) = –0.624, P = 0.536) trials.
In order to determine areas of the amygdala that differen-
tially represented the predictive valence of the faces, we
computed 2 linear contrasts (see Methods). No voxels in the
amygdala/SI region survived statistical correction (P > 0.1) in
the first linear contrast, where we modeled valence as
Negative > Neutral > Positive. However, as predicted for this
conditioning paradigm, we did find a significant linear
representation of valence (Negative > Positive > Neutral) in
a region overlapping the lateral ventral amygdala (Fig. 3C). The
peak voxel (x = 33, y = –5, z = –17) mapped to the lateral ventral
border of the amygdala. The significant cluster comprised
voxels that mapped to the lateral portion of the BLA as well as
Figure 2. Likeability ratings before and after conditioning. Subjects indicated how
much they liked each face (?4, Not at all to þ4, Very much) after the orienting and
conditioning phases of the task. Subjects liked the negative faces less after
conditioning, and the positive faces ore after conditioning. There was no change in
likeability ratings for neutral faces.
Cerebral Cortex March 2010, V 20 N 3 615
immediately adjacent voxels that mapped to the amygdala--
striatal transition zone (Mai et al. 2004). Visual inspection of
the placement of this cluster in terms of each individual’s
anatomy showed a similarly consistent spatial profile with all 42
subjects, such that this cluster overlapped the lateral ventral
amygdala in all subjects.
Critically, post hoc paired t-tests suggest that Early versus
Late trial responses within the lateral ventral amygdala ROI
were resistant to habituation over the course of conditioning
trials (t(41) = 0.817, P >0.10). This is in contrast to Early versus
Late habituation observed in the medial ventral (voxelwise
analysis) and dorsal amygdala/SI (t(41) = 2.445, P < 0.05) loci.
Post hoc t-tests confirmed that there were no significant
differences between Early and Late trials in this lateral ventral
locus in any condition [Negative condition (t(41) = 0.972, P >
0.30); Positive condition (t(41) = 0.881, P > 0.30); Neutral
condition (t(41) = 0.107, P > 0.90)]. In order to directly
compare regional differences in habituation rates, we con-
ducted a within subjects ANOVA with amygdala subregion
(medial ventral, lateral ventral, and dorsal amygdala) as the
independent variable and habituation value (i.e., Early--Late) as
the dependent variable. As predicted, there was a significant
effect of amygdala subregion (F2,82= 6.861, P < 0.01). Paired t-
tests confirm that there is no difference in habituation values
(Early vs. Late) between the medial ventral and dorsal
amygdala/SI regions (t(41) = 1.251, P > 0.1). In contrast,
habituation rate within the lateral ventral region was signifi-
cantly different from the medial ventral region (t(41) = 4.338,
P <0.001) and the dorsal amygdal/SI region (t(41) = 2.478, P <
0.05). That is, both the medial ventral and dorsal amygdala
Figure 3. Differential amygdala response to conditioned stimuli. PSC, Percent Signal Change. (A) Activity in the medial ventral amygdala increased in response to all 3 faces, and
this response habituated over time. (B) Activity in the dorsal amygdala/SI increased to Neg and Pos faces when compared with Neu, and this habituated over time but maintained
differential representation of Neg and Pos compared with Neu. (C) Linear representation of valence in the lateral ventral amygdala with Neg [ Pos [ Neu, which did not
habituate over time.
Amygdala Responses during Social Conditioning
Davis et al.
regions showed significantly greater habituation than the
lateral ventral amygdala.
The 3 activation peaks reported here were from 13 to 23
mm apart (Fig. 4). The ability to separately measure BOLD
signal change from proximal brain structures is ultimately
limited by the intrinsic spatial smoothness of fMRI in these
regions. Estimates of spatial smoothness of our sample in the
amygdala/SI region yielded a FWHM of 7 mm, well below the
interpeak distances reported. In addition, none of the clusters
overlapped. Thus, these data show that we can confidently
dissociate these coarse spatial clusters at the spatial resolution
parameters utilized here based on their functional profiles.
Responses to Sentences
We also included estimates of subjects’ responses to each of
the sentence categories (Negative, Positive, Neutral) in our
general linear model. In order to ensure that the observed
responses to faces were different from responses to the
sentences, we computed the same statistical contrasts on
cluster means extracted from each of the functional ROIs
specified above for responses to sentences. We found that
activity in the medial ventral amygdala to all sentences was
similar to that observed to faces [i.e., was greater during Early
trials compared with Late trials (t(41) = 4.373, P < 0.001)].
Critically, responses to the sentences did not mirror responses
to the faces in the dorsal amygdala/SI ROI showing Negative
and Positive > Neutral faces (t(41) = –0.24, P > 0.1), or the
lateral ventral ROI showing a linear representation of valence
modeled as Negative >Positive >Neutral faces (t(41) = –0.503,
P > 0.1).
Evaluation of Gender Differences
Post hoc t-tests were performed on cluster means extracted
from individual subjects to evaluate potential influences of
gender for each ROI. No significant differences between males
and females were observed in the ventral medial locus (Early vs.
Late: t(40) = 1.143, P > 0.2), the dorsal locus (Neg/Pos > Neu:
t(40) = 1.208, P >0.2), or the ventral lateral locus (Neg >Pos >
Neu: t(40) = –1.923, P > 0.05).
Relationship between Neural Activity and Self-Report
We collected postacquisition likeability and arousal ratings to
confirm that subjects learned the value associated with the 3
face identities. We did not expect that subjective ratings of the
faces taken postacquisition would correlate with amygdala
response during acquisition. For completeness, we note that
subjective arousal ratings were not correlated with dorsal
amygdala/SI responses based on arousal (r = 0.201, P > 0.1). In
addition, subjective likeability ratings did not correlate with
neural responses observed in the lateral ventral amygdala (r =
0.058, P > 0.1).
Here we showed human amygdala activation during a social
conditioning task where subjects learned that different
individuals predicted different social outcomes. Consistent
with previous work (Buchel et al. 1998; LaBar et al. 1998;
Morris et al. 1998; Buchel et al. 1999; Morris et al. 2001; Phelps
et al. 2004; Cheng et al. 2007; Hare et al. 2008), we observed
robust habituation of amygdala responses across the course of
a relatively short conditioning phase. In the medial ventral
amygdala, this effect comprised responses that were initially
strong across all stimulus categories (Negative/Positive/Neu-
tral) that then returned to baseline. In the dorsal amygdala/SI,
this effect comprised responses that were higher to Negative
and Positive compared with Neutral faces, and these differ-
ences between conditions were maintained even as the overall
response magnitude for all conditions decreased over time.
Finally, in the lateral ventral amygdala, we observed a linear
representation of valence such that Negative > Positive >
Neutral. This locus differed from the dorsal and medial loci in
that the magnitude of these responses did not significantly
decrease over the acquisition phase studied here (Fig. 3).
Gender differences were not observed for any of the reported
Amygdala Responses during Acquisition of an Associative
Social Learning Task
In the current study, we reduced the complex process of
assigning reinforcement value to others based on prior social
interactions to a simple associative learning paradigm. We
based the design of this paradigm on more traditional Pavlovian
conditioning studies showing that the human amygdala is
sensitive to signals that predict biologically relevant outcomes
(e.g., Buchel et al. 1998; LaBar et al. 1998; Buchel et al. 1999;
Morris et al. 2001; Phelps et al. 2001). Studies showing human
amygdala involvement in processing facial expressions also
informed our predictions because facial expressions commu-
nicate the emotional states of others and allow for the
prediction of social outcomes (see Whalen et al. 2009). In
fact, a growing body of literature suggests that the amygdala is
Figure 4. Three-dimensional representation of amygdala activation during learning.
Activations are superimposed on an averaged anatomical brain (42 subjects) to show
their location relative to one another. Three clusters were obtained from different
a priori contrasts. Activity in the dorsal amygdala/SI and medial ventral amygdala
clusters habituate over time, whereas activity in the lateral ventral amygdala is
sustained throughout learning.
Cerebral Cortex March 2010, V 20 N 3 617
involved in such subtle social and emotional processes just as it
is involved in processing stimuli that induce stronger emotional
states, such as tones predicting shock (e.g., Buchel et al. 1998,
1999; LaBar et al. 1998; Morris et al. 2001; Phelps et al. 2001),
threatening scenes (e.g., Canli et al. 2000) or aversive odors
(Zald and Pardo 1997; see Whalen 1998 for discussion). Here
we extend traditional models of Pavlovian conditioning to
characterize the amygdala’s role in assigning reinforcement
value to other people, and suggest that its role in effective
social interaction may be best understood through its more
elemental role in forming biologically relevant associations
(Adolphs 2003; Amaral et al. 2003).
The human amygdala has been previously implicated in
other extensions of Pavlovian conditioning, such as abstract
representations of fear acquired through social communication
(Phelps et al. 2001) or through social observation (Olsson and
Phelps 2007). Indeed, the exact relationship between the
current model of learning and traditional forms of Pavlovian
conditioning requires further elucidation. One possibility is
that the current paradigm is related to evaluative conditioning,
which is an associative process whereby a previously neutral
stimulus acquires the affective value of an unconditioned
stimulus, rather than predicts its occurrence (De Houwer et al.
2001). Future studies might seek to directly compare the
present paradigm with more traditional conditioning tasks to
document the differences between the two.
Dissociating fMRI Responses across the Human
In the present study, we observed that both the dorsal amygdala/
SI and the medial ventral amygdala showed signal increases to
faces predicting both negative and positive social outcomes. This
finding is consistent with data in monkeys showing that neurons
throughout the basal and centromedial amygdaloid nuclei code
visual stimuli predicting either negative or positive outcomes
(Paton et al. 2006). These data support the assertion that the
amygdala is involved in linking negative as well as positive value
to stimuli that predict threat and reward, respectively (Holland
and Gallagher 1999; Murray 2007).
In contrast, a lateral ventral region of the amygdala
differentiated between conditioned stimuli based upon their
valence, with the largest responses observed to faces predict-
ing negative social outcomes. This spatial dissociation con-
verges with an earlier study of amygdala responses to surprised
facial expressions (Kim et al. 2003). In that study, a strikingly
similar region of the lateral ventral amygdala showed the
greatest response to negatively interpreted surprised faces.
Critically, in these same subjects, a dorsal amygdala/SI region
and a medial ventral region of the amygdala showed compa-
rable signal increases to positively as well as negatively
interpreted surprised faces (Kim et al. 2003), consistent with
the responses observed within the dorsal amygdala/SI and
ventral medial amygdala in the present study. Together, these
fMRI data support the notion that it is possible to dissociate
activity across coarsely defined subregions of the human
amygdala at resolution levels routinely employed by numerous
neuroimaging laboratories (i.e., 3 mm3voxels). Such results
suggest that an appreciation for regional differences might help
resolve apparent discrepancies across studies of the amygdala
where some show amygdala activity clearly tracking valence
(Kim et al. 2003; Pessoa et al. 2005; Straube et al. 2008) while
others show responses related to arousal value (Garavan et al.
2001; Anderson et al. 2003; Kensinger and Schacter 2006; Lewis
et al. 2007; Demos et al. 2008).
Response Habituation Profiles Observed during
Activity within the lateral ventral amygdala showed differential
activity to all face categories based upon their learned valence,
with the greatest signal increases observed to the face identity
predicting negative social outcomes. In addition, these
responses were sustained across the acquisition phase studied
here. These data are potentially consistent with Pavlovian
conditioning data in rats (Repa et al. 2001; Radwanska et al.
2002) showing that a population of cells in the lateral nucleus
of the amygdala maintained differential representations of
conditioned stimuli (CS+ vs. CS–) throughout learning (see also
Medina et al. 2002). These findings converge with another
human neuroimaging report showing that a region of the
human ventral amygdala was more resistant to habituation
during Pavlovian conditioning (Morris et al. 2001). Here we
localize such an effect to the most lateral aspects of the ventral
This effect was in stark contrast to responses observed
within the medial ventral amygdala, which showed a clear
pattern of habituation (i.e., early signal increases that returned
to baseline). Noteably, the habituation profile observed in the
dorsal amygdala/SI region was more complex. Here, like the
medial ventral amygdala, early signal increases to positive and
negative stimuli returned to baseline over time. However, like
the lateral ventral amygdala, response magnitude differences
between valenced (Negative and Positive) and Neutral face
identities were maintained within the dorsal amygdala/SI
through Early and Late trials. Based on these findings, we
tentatively suggest that during acquisition, the lateral ventral
amygdala maintained differential representations of valence
while the dorsal amygdala/SI region maintained representa-
tions related to the similar arousal value of Negative and
Positive identities. Further, regional differences in habituation
rates are at least consistent with the possibility that parts of the
amygdala maintain learned representations while others play
a more time-limited role during learning (Repa et al. 2001;
Medina et al. 2002; Radwanska et al. 2002).
Relating Regional fMRI Responses to Amygdala Anatomy
Though we are careful to refer to these differential activations
in a regional sense, the predictions for the present study were
inspired by data in animals showing that amygdala subnuclei
can be dissociated both functionally and anatomically. In
humans, fMRI activations within the ventral region of the
amygdaloid complex (e.g., z = –10 to z = –28) clearly map to the
BLA as defined in Mai et al. (2004). Within the BLA, the basal
nuclei are located medially and the lateral nucleus is located
laterally. Here, we report differences between these 2 ventral
amygdala regions in habituation rates and valence discrimina-
tion that suggests it may be possible to functionally dissociate
these portions of BLA using BOLD.
The central nucleus lies dorsal to the BLA in humans (see
Supplemental Fig. 1). fMRI activations located within dorsal
amygdala regions (z = –3 to z = –9) are more difficult to localize
to a specific neuronal source due to this region’s complicated
intermingled anatomy. For example, neurons similar to those
Amygdala Responses during Social Conditioning
Davis et al.
found in the central nucleus of the amygdala extend from the
amygdala ‘‘proper’’ in a superior and medial direction through
the ventral basal forebrain, and are referred to as the SLEA. Also
intermingled within this same region are the corticopetal
cholinergic neurons of the nucleus basalis of Meynert (NBM)
(Heimer and Van Hoesen 2006). Indeed, the intermingled
nature of the neurons in this region of the ventral basal
forebrain is why it is referred to as the SI. Thus, fMRI activations
within the dorsal amygdala/SI region detailed here, as well as
spatially similar activations documented previously (Breiter
et al. 1996; Whalen et al. 1998; Canli et al. 2000; Morris et al.
2001; Phelps et al. 2001; Whalen et al. 2001; Pessoa et al. 2002,
2005, 2006; Kim et al. 2003) could reflect activity of dorsal
amygdala nuclei, SLEA and/or NBM neurons. Concerns that
these neuronal groups cannot be discerned with fMRI are
mitigated by the fact that they are anatomically interconnected
and together have been implicated in alerting other neural
systems (e.g., cortex) to the arousal value of predictive stimuli
(Kapp et al. 1992, 1994; Whalen et al. 1994; Holland and
Gallagher 1999). This theoretical stance is consistent with the
presently observed signal increases within the dorsal amygdala/
SI region to both positively and negatively valenced faces (see
also Kim et al. 2003), as well as the significantly increased
subjective arousal ratings ascribed to these 2 stimulus
categories following learning.
Although subregions of the amygdaloid complex are heavily
interconnected, differences in intrinsic and extrinsic connec-
tivity suggest that the subregions identified here should
support different aspects of associative learning tasks. The
lateral nucleus receives the majority of the amgydala’s sensory
input, with heavy projections from higher order unimodal
sensory cortices as well as multimodal sensory projections
(Freese and Amaral 2009). As such, the lateral nucleus is
commonly thought of as a sensory input and convergent
processing center that detects incoming sensory input and
then integrates this input across modalities (LeDoux et al.
1990; Pitkanen 2000). In primates, the lateral nucleus receives
considerable input from visual area TE (Aggleton et al. 1980)
which is a high level visual area situated at the end of the
ventral visual stream (commonly referred to as the ‘‘what’’
pathway). Information within the amygdala flows from the
lateral nucleus to more medially situated nuclei, such as the
basal and accessory basal nuclei. These nuclei are heavily
interconnected with the orbital and medial prefrontal cortices,
as well as the rostral aspects of the anterior cingulate cortex
(see Freese and Amaral 2009). These connections allow the
basal nuclei to integrate contextual and motivational informa-
tion with the sensory information passed on from the lateral
nucleus. Although the basal nuclei can act as one output source
for the amygdala (i.e., sending outputs to prefrontal cortical
regions), they also project to the central and medial nuclei of
the amygdala. These nuclei, located dorsally in the human,
project directly to all of the major neuromodulatory centers in
the brain (Price and Amaral 1981; Price 1986; Price et al. 1987).
Thus, these nuclei, in conjunction with the intermingled cell
groups of the SLEA, have the ability to nonspecifically increase
arousal throughout the brain.
These patterns of intrinsic and extrinsic connectivity are
potentially consistent with the differential patterns of activation
observed in the current paradigm. That is, the lateral aspects of
the amygdala maintained differential representations of the
valenced identities of the faces, consistent with its role in
detecting and representing highly processed sensory informa-
tion (LeDoux et al. 1990; Pitka ¨ nen 2000) such as that emanating
from the ventral visual stream. In contrast, activity in the medial
ventral amygdala did not discriminate between valenced face
identities, but rather showed an overall increase in activity to all
faces that decreased over the course of learning. This could be
consistent with the idea that the basal nuclei, via their reciprocal
connections with the prefrontal cortex (Freese and Amaral
2009), are integrating the self-relevant contextual information
provided by the sentences with existing representations of the
individual faces. Finally, the dorsal aspects of the amygdala,
consistently implicated in increasing nonspecific arousal (see
Kapp et al. 1992; Whalen 1998), show increased responsivity to
both negative and positive stimuli (which are associated with
equivalent levels of emotional arousal in this task) compared
with the neutral stimulus.
Caveats and Limitations
Because classical Pavlovian conditioning studies inspired the
current design, future studies employing additional dependent
measures (e.g., electrodermal activity, startle, etc.) will allow us
to more carefully compare our paradigm with previous work to
elucidate the overlap in brain circuitry mediating these forms of
learning. Moreover, such implicit indices of learning might allow
us to use individual differences in learning to predict differences
in amygdala activity across the subregions studied here, and to
more carefully assess exactly where in the acquisition phase
learning occurred. Indeed, though self-reported likeability and
arousal ratings collected in the present study provided evidence
that subjects learned about the value of the presented individual
faces, these ratings did not correlate with amygdala responses.
This could be due to the fact that the self-report data were
collected post-training, or to the fact that these ratings showed
a relatively restricted range that was not optimal for examining
individual differences in learning. Collecting implicit measures of
learning on-line could provide future studies with the variability
necessary to examine such differences.
The present study utilized a relatively short acquisition
phase. This was based on previous human neuroimaging studies
of conditioning which used a similarly small number of trials
(Buchel et al. 1998, 1999; LaBar et al. 1998; Morris et al. 2001;
Phelps et al. 2004; Cheng et al. 2007), presumably because fMRI
response habituation within the amygdala is a robust phenom-
enon. Thus, future studies will be needed to determine how
long the observed responses in the lateral amygdala are
sustained (e.g., would they have eventually habituated with
more acquisition trials?). Further, the limited number of trials
used here did not allow us to address when exactly the
amygdala began to show evidence of learning on an individual
subjectbasis. Therefore,we look forwardto design
Differential regional activation of the amygdala during associative learning, based on clusters
identified from voxelwise tests and surviving correction for multiple comparisons at P \ 0.05
Contrast Location (Mai et al. 2004)xyz Cluster
Early [ Late
Neg/Pos [ Neu
Neg [ Pos [ Neu
Medial ventral amygdala
Lateral ventral amygdala
Note: Cluster size is reported as number of 2 mm 3 2 mm 3 2 mm voxels and x, y, z coordinates
represent the peak voxel within each cluster.
Cerebral Cortex March 2010, V 20 N 3 619
modifications that might extend amygdala activity during
learning to address these issues. Such modifications would
allow for the determination of any regulatory role the
prefrontal cortex might have in this process as predicted by
the nonhuman animal literature (e.g., Likhtik et al. 2005).
Finally, caution should be exercised in asserting regional
differences in functional activation across a small volume such
as the amygdaloid complex. The compelling nature of the
present spatial dissociations is their 1) nonoverlapping nature
(Fig. 4), 2) distinct response profiles (Fig. 3), and 3) consistency
with previous research. One goal of the present design was to
show it is possible to make crude spatial dissociations at the
spatial resolution levels commonly employed by many neuro-
imaging laboratories. Future studies using higher resolution
imaging will allow for the delineation of anatomical ROIs based
on human amygdala subnuclei and can be used to verify the
response profiles observed here.
materialcanbe foundat: http://www.cercor.
National Institute of Mental Health (MH080716 and MH069315).
The authors would like to thank Michael Anderle, Ron Fisher, Justin
Kim, and Ashly McLean for technical assistance. Special thanks go to
Wayne Kerr for providing the inspiration for this study. Conflict of
Interest: None declared.
Address correspondence to F. Caroline Davis, BA, Department of
Psychological & Brain Sciences, Dartmouth College, 6207 Moore Hall,
Hanover, NH 03755, USA. Email: Frances.C.Davis@dartmouth.edu.
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