Neurobehavioral mechanisms of human fear generalization
Joseph E. Dunsmoor, Steven E. Prince, Vishnu P. Murty, Philip A. Kragel, Kevin S. LaBar⁎
Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
a b s t r a c ta r t i c l ei n f o
Received 23 December 2010
Accepted 12 January 2011
Available online 19 January 2011
Functional magnetic resonance imaging
While much research has elucidated the neurobiology of fear learning, the neural systems supporting the
generalization of learned fear are unknown. Using functional magnetic resonance imaging (fMRI), we show
that regions involved in the acquisition of fear support the generalization of fear to stimuli that are similar to a
learned threat, but vary in fear intensity value. Behaviorally, subjects retrospectively misidentified a learned
threat as a more intense stimulus and expressed greater skin conductance responses (SCR) to generalized
stimuli of high intensity. Brain activity related to intensity-based fear generalization was observed in the
striatum, insula, thalamus/periacqueductal gray, and subgenual cingulate cortex. The psychophysiological
expression of generalized fear correlated with amygdala activity, and connectivity between the amygdala and
extrastriate visual cortex was correlated with individual differences in trait anxiety. These findings reveal the
brain regions and functional networks involved in flexibly responding to stimuli that resemble a learned
threat. These regions may comprise an intensity-based fear generalization circuit that underlies retrospective
biases in threat value estimation and overgeneralization of fear in anxiety disorders.
© 2011 Elsevier Inc. All rights reserved.
Fear learning involves acquiring defensive behaviors to aid
survival in response to environmental threats. To be adaptive, it is
important that this learning is flexible such that stimuli highly similar
to a learned threat are treated as potentially harmful as well.
Extensive neurophysiological and brain imaging research has estab-
lished several key regions involved in fear learning processes,
including the amygdala, insula, cingulate gyrus, striatum, sensory
cortex, and prefrontal cortex (Phelps and LeDoux, 2005). The goal of
the present study is to examine neural systems contributing to the
generalization of fear learning, and to link generalization-related
activity to individual differences in fear expression, functional
connectivity, and trait anxiety.
Generalization of fear learning serves a functional purpose because
it allows an organism to treat novel stimuli appropriately based on
experience with related stimuli. For example, an animal knows to
avoid a potential predator if it resembles one that has been
encountered in the past. However, in some cases, the transfer of
fear following a learning episode has maladaptive consequences. For
instance, an animal that widely casts defensive behaviors towards a
broad range of stimuli is at risk of wasting energy resources. This
overgeneralization of fear to harmless stimuli is often a symptom of
clinical anxiety, as exemplified by posttraumatic stress disorder. Thus,
to be well adapted to the environment, an organism must balance
expressing fear behaviors towards novel threats on the one hand and
withholding fear responses to non-threats on the other hand.
The laboratory study of fear generalization has frequently used
Pavlovian conditioning procedures, wherein a neutral conditioned
stimulus (CS; e.g., a tone) predicts an intrinsically threatening
unconditioned stimulus (US; e.g., an electric shock). After a CS–US
association is formed, the CS evokes a conditioned fear response (CR),
such as a change in heart rate, respiration rate, or sweating.
Generalization occurs when these behaviors are evoked by stimuli
that are similar to a learned threat, but have never directly predicted
the US. Several factors contribute to fear generalization. For instance,
in his seminal studies of non-human animals, Pavlov (1927) observed
that the CR generalized to graded stimuli that closely resembled the
original CS, and diminished as perceptual similarity decreased.
Gradients that track perceptual similarity have been consistently
observed in animal conditioning experiments (Honig and Urcuioli,
1981). The ability to discriminate a CS from a non-CS additionally
affects the breadth of fear generalization; animals trained with a
single CS show more widespread generalization than animals trained
to discriminate between different stimuli along the same sensory
dimension (Jenkins and Harrison, 1960). Studies have also shown
generalization to increase as a function of intensity — for instance
from a medium volume sound that predicts the US to a loud volume
sound that has never predicted the US (Ghirlanda and Enquist, 2003).
Intensity generalization often involves a shift in peak responding,
such that a non-CS of greater intensity than the CS evokes a greater
response than the CS itself (Ghirlanda and Enquist, 2003). Models of
stimulus-intensity generalization may be particularly well suited to
describe fear behaviors, since following an aversive experience (e.g.,
NeuroImage 55 (2011) 1878–1888
⁎ Corresponding author at: Center for Cognitive Neuroscience, Box 90999, Duke
University, Durham, NC 27708-0999, USA. Fax: +1 919 681 0815.
E-mail address: firstname.lastname@example.org (K.S. LaBar).
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encounter with a vicious dog) an intrinsically salient stimulus (e.g.,
forbidding dog) may preferentially evoke a greater fear response than
a similar but less intense stimulus (e.g., harmless dog). Recent human
behavioral studies have shown that generalized fear responses are
et al., 2008) and emotional intensity dimensions (Dunsmoor et al.,
Neurophysiological research of fear generalization in non-human
as this region serves a pivotal role in forming the CS–US association and
GABA release in the lateral nucleus of the amygdala has been shown to
increase fear generalization (Bergado-Acosta et al., 2008; Shaban et al.,
2006). Duvarci et al. (2009) conducted a fear conditioning task in rats
extent to which animals generalized fear from the CS+ to the CS−. They
suggested that differences in generalization were determined in part by
the bed nucleus of the stria terminals, a region closely linked with the
central nucleus of the amygdala, as rats with excitotoxic lesions to this
precise role of the amygdala (and other regions) in fear generalization is
not clear, as generalization is often conceptualized as heightened
responses to the CS−, and formal behavioral tests using graded stimuli
have rarely been conducted in neuroscientific models of fear generaliza-
tion. Human neuroimaging studies have shown that the amygdala,
is not clear whether these regions show graded responses to stimuli that
vary in similarity from the CS+ along some featural dimension.
The present study provides a novel, systematic examination of
human fear generalization using event-related functional magnetic
resonance imaging (fMRI) with concurrent measures of psychophys-
iological arousal (i.e., skin conductance response, SCR). Predictions on
how the human brain mediates fear generalization were based on
knowledge of the brain systems involved in acquiring and expressing
learned fear. First, generalization could be mediated by regions
involved in differential fear learning — that is, areas that show greater
activity to a CS+ compared to a CS−. We expected that learning-
related regions would show enhanced activity to generalized stimuli
of high emotional intensity, consistent with our prior behavioral work
showing that generalization increases as a function of the emotional
intensity value of non-conditioned cues (Dunsmoor et al., 2009).
However, it is possible that regions that show broad enhancement of
activity to generalized stimuli are not directly related to the
production of fear behaviors, and thus poorly reflect variability in
fear learning and generalization. Therefore, to constrain interpreta-
tions of neural activity, we quantified behavioral measures of
generalization by the change in SCR magnitude from pre-to-post
fear conditioning, and used these residualized scores to examine
brain–behavior correlates during a test of fear generalization. These
individual behavioral profiles take advantage of the fact that
psychophysiological responses entail considerable individual vari-
ability that may be mediated by components of the fear learning
circuitry, such as the amygdala (Cheng et al., 2006; Knight et al.,
2005). Thus, we predicted that the change in fear expression
following fear conditioning would be correlated with activity in
limbic/paralimbic regions involved in sympathetic activation and core
affective processes, such as the amygdala and insula. The amygdala is
also important for enhancing the sensory representation of feared
stimuli (Armony and Dolan, 2002) through reciprocal connections
with sensory processing regions like the extrastriate visual cortex
(Amaral et al., 2003). We predicted enhanced connectivity between
the amygdala and visual processing regions coding for the domain
specific properties of the CS and related stimuli following fear
conditioning. Finally, to provide a link to clinical anxiety disorders,
we investigated whether amygdala connectivity related to fear
generalization is correlated with individual differences in trait
anxiety, in line with previous findings showing enhanced amygda-
la–extrastriate connectivity in phobic individuals viewing phobia-
relevant images (Ahs et al., 2009) and serotonin transporter (5-HTT)
short allele homozygotes viewing fearful faces (Surguladze et al.,
We adapted a Pavlovian conditioning paradigm that allows for
simultaneous examination of fear generalization as a function of
perceptual similarity and emotional intensity (Dunsmoor et al., 2009)
for use with fMRI. During fear conditioning, participants received
pairings of a moderately fearful face (CS+) and a shock US, as well as
an unreinforced neutral face stimulus (CS−) (see Fig. 1). The
generalized stimuli were gradations of the same individual's facial
expression morphed incrementally between neutral and fearful
endpoints, presented prior to conditioning to measure baseline
responses and after conditioning in a steady-state generalization
test (Blough, 1975). By detailing brain activation, fear expression, and
functional connectivity before and after learning has occurred, the
present study can help elucidate how humans generalize from a fear
learning episode. These insights, in turn, can inform neural models of
anxiety disorders characterized by overgeneralized expression of
Fig. 1. Stimulus set and task design. (a) The stimulus dimension consisted of 5 images,
of the same identity, morphed between neutral and fearful endpoints. (b) The task
involved rating whether each face was or was not expressing fear by pressing one of
two buttons. In the first phase (preconditioning), subjects saw each morph increment
in the absence of the US. Fear learning involved repeated pairings of the S3 (CS+) with
an electrical shock US, and the S1 (CS−) unreinforced. The generalization test followed
fear learning and involved presentation of each of the morph increments. The CS+ was
intermittently reinforced during the generalization test. Images are not to scale.
J.E. Dunsmoor et al. / NeuroImage 55 (2011) 1878–1888
Materials and methods
Twenty-five right-handed healthy young adults provided written
informed consent to participate in the study. Two participants were
removed from the analysis due to excessive head movement (N3 mm
in any direction), and 9 participants were not included due to a lack of
SCR data, which precludes an examination of fear learning and
generalization (5 participants lacked SCR data due to technical issues
and 4 participants were classified as non-responders as described
below). The behavioral and fMRI analysis included 14 participants (7
females; age range=19 to 30; median age=22 yrs). Subjects
completed the State-Trait Anxiety Inventory (STAI) (Spielberger,
1983) prior to the start of the experiment. The study was approved by
the Duke University Institutional Review Board.
Stimuli consisted of a male face morphed along a gradient from
neutral-to-fearful taken from the Ekman pictures of facial affect
(Ekman and Friesen, 1976). The morphs were positioned in a full-
frontal orientation and cropped to remove hair, ears, and neckline.
Five morphs were created along the continuum using Morph-Man
2000 software (STOIK): 11.11% fear/88.88% neutral, 33.33% fear/
66.66% neutral, 55.55% fear/44.44% neutral, 77.77% fear/22.22%
neutral, and 100% fear. For clarity, these stimuli are labeled as S1,
S2, S3, S4, and S5, respectively. These face morph values were chosen
based on our prior published psychometric studies on categorical
perception using the same stimuli (Graham et al., 2007; Thomas et al.,
2007). These normative studies showed that the S3 stimulus chosen
as the CS+ in the present study is as close as possible to the point of
subjective equality in categorical perception, such that half of adult
participants view the face as expressing fear whereas the other half
view the face as neutral. Moreover, d′ estimates from these studies
showed that individuals can clearly discriminate these specific morph
increments (the values are not perceptually confused), each succes-
sive value from S2 to S4 adds equally to the cumulative d′ function so
that perceptual discrimination is linear across this segment of the
categorical boundary, and individuals are perceptually sensitive to
morphed featural changes that are even more subtle than those
chosen for the present study. The US consisted of a 6 ms electrical
stimulation applied to the right wrist, calibrated for each participant
to a level deemed “highly annoying, but not painful.”
The experimental paradigm was based on Dunsmoor et al. (2009),
and is illustrated in Fig. 1. The experiment began with a short
habituation phase that included 1 presentation of each of the 5 morph
increments, which allowed participants to get accustomed to the
experiment and reduced orienting responses. Habituation data are
not reported. The scanning session consisted of three consecutive
phases that occurred in the same order for each participant:
preconditioning/baseline (3 runs), fear conditioning (2 runs), and
the generalization test (3 runs). A short 5-minute break followed
preconditioning andfearconditioning,during whichtimeparticipants
passively viewed a silent video clip of a train traveling through British
Columbia (Highball Productions). Each trial was 4 s in duration,
during which time participants were asked to rate whether or not the
face was expressing fear (forced choice: yes/no) as quickly and
accurately as possible by pressing one of two buttons. The order of
button presses was counterbalanced across subjects. The intertrial-
interval (ITI) consisted of a white fixation cross on a black background
that followed the offset of each trial. The lengths of the ITI were
jittered according to an exponential distribution function. Precondi-
tioning contained a total of 9 trials of each of the 5 morph increments
(45 total trials) with an average ITI of 5 s (minimum 4 s). Fear
conditioning contained a total of 16 S3 (CS+) and 16 S1 (CS−) trials
(32 total) with an average ITI of 11 s (minimum of 9 s). The CS+ co-
terminated with the US on 10/16 trials, whereas the CS− was never
paired with the US (partial reinforcement delay conditioning
procedure). The generalization test contained a total of 9 trials of
each of the 5 morph increments (45 total) with an average ITI of 9 s
(minimum of 5 s). The S3 was intermittently paired with the US on
6/9 trials (“steady-state” generalization test) to offset the effects of
extinction over the course of an extended testing session, as routinely
implemented in animal models of generalization (Blough, 1975;
Honig and Urcuioli, 1981). In all phases, stimulus presentation was
counterbalanced and pseudorandomized such that no more than two
of the same morph increment occurred in a row. Subjects were not
informed of the CS–US contingencies. Following the conclusion of the
generalization test, a functional localizer task was performed to
isolate cortical regions selective for processing images of faces. The
localizer, based on a previously published design (Morris et al., 2008),
included pseudo-randomized blocks of black and white images of
faces and flowers that each contained 24 images presented for 500 ms
each. Two localizer blocks were run and separated by 12 s of fixation.
Data from the functional localizer were entered into a general linear
model with 2 conditions of interest: faces and flowers (see below).
Psychophysiological methods and analysis
All psychophysiological recording and shock administration was
controlled with the MP-150 BIOPAC system (BIOPAC systems, Goleta,
CA). MRI-compatible Ag/AgCl SCR electrodes were placed on the
middle phalanx of the second and third digits of the non-dominant
hand. The electrical stimulation, applied to the right wrist, was
controlled using the STM-100 and STM-200 modules connected to the
MP-150 system. All psychophysiological equipment was grounded
through the RF filter panel and shielded from magnetic interference.
SCR analysis was carried out using AcqKnowledge software (BIOPAC
systems) using procedures previously described (Dunsmoor et al.,
2009). An SCR was scored as a response if the trough-to-peak
response occurred 1–4 s following stimulus onset, lasted between 0.5
and 5.0 s, and was greater than 0.02 microSiemens. A trial that did not
meet these criteria was scored as a zero.
A long-standing issue in the study of stimulus generalization
concerns measurement of the generalization gradient (Hull, 1943;
Pavlov, 1927). The present analysis developed an approach adapted
from the animal literature to characterize individual fear generaliza-
tion profiles by normalizing SCRs to each morph increment as
proportion of total response output (Honig and Urcuioli, 1981). This
approach is particularly suitable for the present study, as it accounts
for individual differences in response profiles during the precondi-
tioning and generalization test phases. To derive this metric,
preconditioning data were analyzed by dividing the sum of SCRs for
each morph increment (S1–S5) by the total sum of SCRs to all morph
increments during the preconditioning phase. Fear conditioning data
were also normalized on the basis of response output to the CS+ and
CS−. Finally, SCRs obtained during the generalization test were
analyzed for each test run, as fluctuations in response patterns over
time have been observed in prior behavioral stimulus generalization
data, the sum of responses to each stimulus type was divided by the
sum of responses to all trials within each of the three generalization
test runs. This yielded three generalization gradients, one for each run
of the generalization test. The behavioral correlates of fear general-
ization were operationally defined as the difference in normalized
SCRs during each generalization test run from the corresponding
stimulus during preconditioning. In this way, preconditioning served
as a baseline measure for each face stimulus for each participant, and
J.E. Dunsmoor et al. / NeuroImage 55 (2011) 1878–1888
allowed for an analysis of the proportional change in response
patterns following fear learning. SCR data were analyzed by ANOVA
and polynomial trend analyses, with an α value of 0.05 (SPSS 15.0,
Functional image acquisition, preprocessing, and GLM analysis
Tesla MRI. Subjects wore ear plugs to reduce scanner noise, and head
motion was minimized by using foam pads. Blood oxygenation level-
dependent functional images were acquired parallel to the AC-PC line
using a SENSETMspiral in sequence: acquisition matrix, 64×64; field of
view, 256×256; flip-angle, 60°; 34 slices with interleaved acquisition;
slicethickness,3.8 mmwithnogapsbetweenslices;repetitiontime,2 s;
echo time, 27 ms. Functional data were preprocessed using SPM
8 software (Wellcome Department of Cognitive Neurology, University
College London, www.fil.ion.ucl.ac.uk) implemented in Matlab (The
Mathworks Inc, Natick, MA). The first 4 functional images from each
scanning run were discarded to account for magnetic equilibration
effects, and remaining images were corrected for head motion using a
threshold of 3 mm in any direction. Preprocessing included realign-
ment, spatial normalization to the Montreal Neurological Institute
(MNI) template using a fourth degree B-spline interpolation, and
conducted using the GLM. Covariates of interest included the 5 morph
conditioning (CS+ and CS−), and 5 morph increments for generaliza-
tion test (S1–S5). The hemodynamic response was modeled for each
covariate of interest using a variable duration design that incorporated
reaction time foreach trial(Grinband et al., 2008).The USwasmodeled
as an impulse (dirac) function and, along with the 6 head motion
parameters, were included as a covariate of no interest. Second-level
random effects analyses were performed using one-sample t-tests in
the CS+ (S3) and CS− (S1). These contrasts included CS+ and CS−
trials from the fear conditioning phase and the steady-state generaliza-
tion test. A threshold value was initially set to pb0.001, uncorrected,
withanextentthreshold of 5 contiguous voxels, to identify wholebrain
activity related to differential fear learning. For each a priori region of
interest (ROI) identified from whole brain analysis, the search space for
wise error (FWE) correction of pb0.05. These included separate masks
for the caudate, insula, thalamus, and anterior cingulate cortex. All
reported regions survived this correction. Contrast images were then
(S1), and each generalized stimulus versus the CS+ (S3). For these
contrasts images, the mean beta-parameters were extracted from the
preconditioning and generalization test phase using an 8 mm radius
sphere surrounding the peak voxel identified from independent
functional contrast comparing the CS+ versus CS− (coordinates
reported in Table 1). For activation in the thalamus extending into the
periacqueductal gray (PAG), the peak coordinates used for the ROI
analysis were selected from a meta-analytic review by Kober et al.
were assessed by one-sample t-tests for each contrast and two-sample
the generalization test phases, with an α value of 0.05 (SPSS 15.0,
Chicago, IL). Data from the functional localizer were analyzed by first-
level contrasts of faces versus flowers. These contrast maps were
analyzed in a second level random-effects analysis, yielding regions
preferentially engaged by images of faces versus flowers. Search space
was restricted to an anatomical mask of the fusiform gyrus (Maldjian
et al., 2003) using FWE correction of pb0.05.
fMRI regression analysis
Regression analyses were conducted using a second-level random-
effects regression model in SPM 8 to investigate brain–behavior
correlations in human fear conditioning and generalization. Regres-
sion analysis of fear conditioning was conducted for the two runs of
the fear conditioning phase using the difference in SCRs between the
CS+ (S3) and the CS− (S1) as covariates, and brain imaging contrasts
of CS+ versus CS− as the dependent variable (excluding one subject
from the second run for technical problems with the SCR equipment).
This analysis revealed regions positively tracking the difference in
SCRs between the CS+ and CS−. Next, we conducted a regression
analysis of the generalization test using the change in normalized
SCRs from pre-to-post fear conditioning to each stimulus as a
covariate. Individual contrasts for S1–S5 (relative to baseline), for
the three generalization test runs, were entered as the dependent
variable. For clarity, responses to the S3 do not reflect generalization,
as the S3 continued to serve as the CS+ throughout the steady-state
generalization test. Mean parameter estimates from the functional
ROIs identified from regression analysis were extracted and brain–
behavior correlations were plotted for illustrative purposes. No
outliers (defined as data points 3 standard deviations from the
mean) were detected. Regions from brain–behavior analyses were
initially identified for the whole brain at pb0.001 uncorrected and an
extent threshold of 5 contiguous voxels. All regions were then subject
to FWE correction of pb0.05 applied to the appropriate bilateral
anatomical mask from the Wake Forest PickAtlas toolbox.
Amygdala connectivity analysis
The goal of this analysis was to examine functional connectivity
between the amygdala and face-selective cortex as a function of phase
(preconditioning, generalization test) and stimulus value (S1–S5).
The seed region for this analysis was the left amygdala identified from
the independent fear conditioning regression analysis. The face-
selective region was the right fusiform gyrus identified from an
independent functional localizer task. A GLM was created that
modeled each individual trial as a separate covariate (Rissman et al.,
2004). Correlations were computed for each subject by calculating the
Pearson correlation coefficients between the mean parameter
estimates from the seed region (amygdala) and fusiform gyrus.
These values were converted from correlation coefficients to Z scores
using the Fisher transform in order to make group inference. Inputs
from the single subject level were input into a full factorial model as
implemented in SPM 8, with phase (preconditioning, generalization
test) and stimulus (S1–S5) as factors.
Fear learning-related activity.
RegionHemisphereMNI coordinates Volume
xyz size (mm3) Peak TPeak Z
CS+ versus CS−
CS− versus CS+
J.E. Dunsmoor et al. / NeuroImage 55 (2011) 1878–1888
Analysis of SCRs during the preconditioning phase, using ANOVA
with S1–S5 as a repeated measure, revealed no difference in SCRs as a
function of stimulus intensity value, F4, 52=0.83, p=0.51 (Fig. 2a).
These results demonstrate that SCRs were not sensitive to stimulus
intensity value prior to fear learning, consistent with research
demonstratingthatstatic facialexpressions are notinherentlyhighly
arousing(Anderson etal.,2006).Next,assessmentof SCRsduringthe
fear conditioning phase revealed that differential fear learning took
place — subjects expressed greater normalized SCRs to the CS+
(mean±SEM: 0.72±0.02) thantotheCS− (0.27±0.02),t(13)=9.20,
maineffectof stimulusintensity,F4, 52=9.003,pb0.001,withapositive
linear trend across the five face exemplars (pb0.001) (Fig. 2a). This
asymmetric gradient is in line with generalization based on emotional
function that saturates but does not fall below the CS+ value
(Dunsmoor et al., 2009; Ghirlanda and Enquist, 2003). The pre-to-post
difference in SCR response profiles (Fig. 2b) provided the primary
behavioral index of fear generalization. ANOVA of these fear general-
ization scores showed a main effect of stimulus intensity, F4, 52=3.76,
p=0.009, with a significant positive linear trend across the five face
exemplars, p=0.01. Whereas stimuli of less intensity than the CS+
high intensity showed an average increase following fear conditioning.
However, behavioral responses across the emotional intensity gradient
were variable overall, and these individual variations in behavior
provide the basis for the neuroimaging regression analysis. See also
Supplemental Table 1 for square root transformed and range corrected
On each trial, subjects rated whether or not the morphed stimulus
effect of morph increment during preconditioning, F4, 52=6.667,
pb0.001, with both linear (p=0.01) and quadratic (p=0.02) trends
(Fig. 2c). A main effect of stimulus type on RT was also observed during
the generalization test, F4, 52=3.596, p=0.01. Post-hoc Bonferroni-
corrected t-tests revealed that RTs were faster for the S4 versus the S3
both before and after fear conditioning, pb.05. See also Supplemental
Fig. 1 for subjective ratings of fear expression. At the conclusion of the
experimental session, subjects were asked to identify which morphed
stimulus had been paired with the US. A chi-square test revealed that a
(4)=23.86, pb0.001, from the array of stimuli (Fig. 2d). This false
retrospective identification for the CS+ as a more emotionally intense
stimulus confirms prior findings (Dunsmoor et al., 2009) of a co-
variation bias (Öhman and Mineka, 2001). It is important to emphasize
whether the misidentification would be similar if probed during the
We did not observe any brain regions that increased linearly as a
function of fear intensity prior to fear conditioning. The neural correlates
of differential fear learning were identified by comparing activity to the
the caudate, insula, and thalamus, extending into the PAG (Table 1). The
reverse contrast (CS− versus CS+) revealed enhanced activity in the
commonly identified in human fMRI investigations of fear conditioning
(Delgadoet al.,2008;LaBar and Cabeza, 2006; Phelps and LeDoux, 2005).
Activity related to the generalized stimuli (S2, S4, and S5) was examined
by probing a priori functional ROIs identified from fear learning. First, to
Fig. 2. Behavioral results. (a) Mean normalized SCRs from preconditioning and the generalization test show that response output was undifferentiated along the neutral-to-fearful
continuum during preconditioning (white bars) and shifted towards stimuli of high emotional intensity during the generalization test (black bars). (b) Difference scores, reflecting the
change inresponseoutput from preconditioningtogeneralization test, show anaverage decrease inpsychophysiological responses tothe S1and S2and anaverageincreaseto the S3, S4,
and S5.(c) Mean reaction timesshowthatsubjects werefastest to categorizetheS4andS5asfearful.(d) Amajorityofsubjects(71%)mistakenlyidentifiedthe S4astheCS+indicatinga
strong illusory correlation. Error bars reflect standard error of the mean (SEM), (*) denote significant differences (pb0.05) and (**) at pb0.01.
J.E. Dunsmoor et al. / NeuroImage 55 (2011) 1878–1888
examine whether differential learning activity was specific to the CS+,
contrasts were created that compared activity to each generalized
stimulus against the CS−. In this way, the CS− served as a reference
condition (Lissek et al., 2008, 2010) for both learning (CS+ versus CS−)
and generalization (S2 versus CS−; S4 versus CS−; S5 versus CS−).
the generalization test. Prior to fear conditioning, no significant effects
were observed. Following fear conditioning, enhanced activity relative to
the CS− was revealed for the S4 and S5 within the caudate and insula
(Fig. 3a), suggesting that generalization-related activity in these regions
was driven primarily by the intensity value of non-conditioned stimuli.
Enhanced activity was also observed in the thalamus extending into the
PAG for the S5 (see Supplemental Fig. 2). That these effects only emerged
during the generalization test, and were not present at preconditioning,
learning. A similar analysis was conducted for the reverse contrast
(regions that preferentially signaled the CS− relative to the CS+ during
learning). No significant differences in activity to the generalized stimuli
were observed in these regions during preconditioning, but enhanced
activity was observed to the S2 versus the CS+ within the rostral and
exhibit a reversed intensity-based generalization gradient perhaps
constituting a ‘safety’ signal (Fig. 3b).
Brain–behavior correlation results
Analysis of brain–behavior correlations focused on the relationship
between brain activity and SCRs during the fear conditioning and
generalization test phases. In line with prior findings (Cheng et al.,
2006; Knight et al., 2005), activity in the left amygdala (x=−30, y=4,
z= −26; 256 mm3) was positively correlated with behavioral measures
Supplemental Fig. 3). This result confirms that amygdala activity was
related to measures of autonomic arousal that reflect the acquisition of
conditioned fear. Based on findings from our previous behavioral study
showing that the S4 in particular evoked considerable fear generalization
and induced a false memory as the CS+ (Dunsmoor et al., 2009), we
revealed for this stimulus. Consistent with predictions, positive correla-
tions were found in the left amygdala (x=−26, y=4, z=−18;
1280 mm3) and both right (x=42, y=8, z=−6; 6400 mm3) and left
(x=−42, y=−12, z=−2; 832 mm3) insula (Fig. 4) for S4 presenta-
tions. These results show that the change in fear expression to the S4 is
reflected by increases in activity within regions commonly implicated in
conditioned fear learning. For comparison, no regions showed positive
perceptual overlap with the CS+ but contained less emotional intensity
value. These results support the idea that emotional intensity, and not
that neural responses in these regions were linked to the physiological
expression of fear — when a direct S4 vs. S2 contrast was conducted
irrespective of SCR levels, there were no significant results. Thus, activity
extent to which correlations identified for the S4 were selective to this
the S4 and all other stimulus values, including the CS+. This analysis
revealed activity in the left dorsal amygdala (x=−26, y=4, z=−18;
Fig. 3. Brain regions involved in differential fear learning show generalized patterns of activity. (a) Regions of interest were identified by contrasts of CS+ versus CS−. Contrasts of each non-
the right caudate. (b) Regions of interest were identified by contrasts of CS− versus CS+, and revealed activity in the subgenual (x=−2,y=32, z=−6) and rostral (x=−6, y=44, z=10)
corrected with a family-wise-error pb0.05, shown here at pb0.001 (uncorrected) for visualization purposes. Error bars reflect SEM. For one-sample t tests, (*) denote significant differences
(pb0.05) and (**) at pb0.01. For paired-samples t test, (/) denotes significant differences (pb0.05).
J.E. Dunsmoor et al. / NeuroImage 55 (2011) 1878–1888
192 mm3), indicating that the amygdala showed the strongest brain–
behavior effects for the S4, which was falsely identified as the CS+ by a
majority of participants after the generalization test.
Amygdala connectivity results
Finally, because the amygdala is important for modulating activity in
sensory processing regions to more effectively detect and evaluate
affective stimuli (Armony and Dolan, 2002; Vuilleumier, 2005), we
predicted that connectivity between the amygdala and extrastriate visual
cortex would be important for supporting generalization from a learned
threat to related stimuli. To examine amygdala connectivity, we used an
independent functional ROI of the left amygdala identified from the fear
conditioning regression analysis (x=−30, y=4, z=−26) as a seed
region for a trial-by-trial functional connectivity analysis of the
preconditioning and generalization test phases (Rissman et al., 2004).
We focused on connectivity between the amygdala and a face-selective
region in the fusiform gyrus (FFG) identified from the independent
functional localizer task (x=38, y=64, z=22). During precondition-
ing, there was no difference in functional connectivity as a function of
stimulus intensity value (F4, 52=2.02, p=0.11). During the generaliza-
connectivity (F4, 52=3.856, p=0.008) with a linear (p=0.006) and
quadratic (p=0.036) trend (Fig. 5). A significant phase (precondition-
ing, generalization test) by stimulus interaction was observed for the
CS+ versus the CS− (F1, 13=7.449 p=0.017), indicating a learning-
related change in amygdala connectivity following fear conditioning. A
significant phase by stimulus interaction was also observed for the S4
versus the CS− (F1, 13=7.454 p=0.017), but not for the other
generalized stimuli (pN0.1). Prior research has also shown enhanced
amygdala–fusiform connectivity in anxious individuals while viewing
phobia-relevant stimuli (Ahs et al., 2009), and in 5-HTT short allele
homozygotes while viewing fearful faces (Surguladze et al., 2008). To
determine whether individual differences in trait anxiety would
correlate with increases in amygdala connectivity following fear
learning, we correlated the change in connectivity for the CS+ and S4
from pre- to post-conditioning with each participant's trait anxiety
two stimuli to show an increase in amygdala–FFG connectivity
following fear conditioning. We found that individual differences in
trait anxiety were positively related to the pre-to-post fear learning
increase in amygdala connectivity for the S4 (r(13)=0.62, p=0.018),
in trait anxiety were also negatively related to amygdala activity
indexing differential learning (CS+ versus CS−) during early acquisi-
tion training due to a generalized response to the CS− (see
Supplemental Fig. 3). No other fMRI results were moderated by trait
Fear generalization is a common occurrence following a highly
aversive experience that is exaggerated in some individuals. Here,
subjects who underwent classical fear conditioning to a moderately
learned threat but contained greater emotional intensity. Generalized
fear expression was mirrored by a false memory of the CS+ as a more
intense stimulus than it actually was, indicating a retrospective bias in
estimating threat value. fMRI results confirmed several key hypotheses.
First, regions involvedintheacquisitionof differentialfearlearningalso
showed responses to generalized stimuli as a function of emotional
and insulacorrelated withindividualvariabilityinphysiologicalarousal
measures related to the expression of fear generalization. Finally,
functional connectivity between the amygdala and the face-selective
region of the fusiform gyrus was enhanced during the generalization
testtoa non-conditioned stimulusof highintensity, and thisincreasein
connectivity from pre-to-post fear learning was correlated with trait
anxiety. Combined, these results provide new evidence that regions
with an established role in fear learning are important for generalizing
may be best understood on the basis of individual differences in
behavior and anxiety levels.
Present findings for the striatum, insula, and thalamus/PAG extend
previous knowledge concerning the role of these regions in aversive
learning. For instance, prior research has shown that the striatum
serves a role in learning to predict and fear an aversive stimulus
(Delgado et al., 2008). The striatum has also been shown to flexibly
adapt when contingencies surrounding CS–US pairing are reversed
(Schiller and Delgado, 2010). The thalamus is a key region involved in
aversive learning that provides sensory information to the amygdala
directly and indirectly (LeDoux, 1996), and this region frequently
Fig. 4. Brain–behavior interactions in fear generalization. (a) Normalized SCR scores for
the S4 illustrate the variability in response across subjects and across runs.
(b) Correlations between SCR scores and brain activity were revealed in the insula
and amygdala, such that increases in arousal from pre-to-post fear learning were
associated with increases in brain activity in these regions.
J.E. Dunsmoor et al. / NeuroImage 55 (2011) 1878–1888
shows enhanced responses to a CS+ in human imaging studies of fear
conditioning (LaBar and Cabeza, 2006). The periacqueductal gray
supports physiological responses to aversively conditioned stimuli
(LeDoux et al., 1988) and is implicated as part of a core functional
group of limbic regions involved in affective processes (Kober et al.,
2008). Likewise, the insula serves a role in physiological responses to
affectively significant stimuli, and prior neuroimaging research has
shown that uncertainty and anticipation for receiving an aversive
stimulus enhances insula activity (Berns et al., 2006; Dunsmoor et al.,
2007). In all, generalized patterns of activity in the striatum, insula,
and thalamus/PAG imply that areas involved in fear learning are not
specific to an aversively conditioned stimulus. These regions may be
important for responding to stimuli that share properties with a
learned threat in order to adaptively react to potential threats from
the environment. During the generalization test, a non-conditioned
stimulus of lower emotional intensity, which most closely resembled
the ‘safe’ CS−, showed enhanced activation in the rostral and
subgenual ACC when compared to the CS+. Prior research has
shown that these regions are important for regulating emotional
responses (Phelps and LeDoux, 2005; Sotres-Bayon et al., 2004). For
instance, the vmPFC is involved during the recall of learned extinction
(Miladet al., 2007;Phelps et al.,2004), and may mediateextinction by
inhibiting activity in the amygdala (Maren and Quirk, 2004). Human
neuroimaging of fear conditioning has shown that during fear
acquisition responses to the CS+ often decrease in the vmPFC,
whereas responses to the CS− increase (Schiller and Delgado, 2010;
Schiller et al., 2008). This pattern emerged in the present study, such
that responses to the CS− were significantly enhanced relative to the
CS+, suggesting that the CS− evoked activity related to regulatory
processes. Given that the CS− and S2 were both associated with
relative decreases in arousal following fear conditioning, it is
noteworthy that the S2 was the only other stimulus to evoke
enhanced activity within these regions. This finding suggests that
the regulation of fear can generalize beyond a learned safety signal to
The regression analysis of brain–behavior interactions yielded
crucial findings on the relationship between the change in arousal
following fear learning and neural activity in the amygdala and insula.
Animal models of fear conditioning have consistently implicated the
amygdala as the final common pathway involved in fear learning and
fear expression (LeDoux, 2000). The precise role for the amygdala in
fear generalization is not known, but the amygdala may initiate rapid
generalized fear responses by way of direct connections with the
sensory thalamus (Han et al., 2008) as neurons in the sensory
thalamus are broadly tuned (Bordi and LeDoux, 1994). Prior
neuroimaging research has shown that amygdala activity is related
to the production of conditioned SCRs, and does not merely track the
presence of a stimulus with threat value (Cheng et al., 2006; Knight
et al., 2005). Consistent with these previous findings, amygdala
activity was related with differential SCRs (CS+ versus CS−) during
the fear conditioning phase, and selectively tracked behavioral
measures related to increases in arousal evoked by the S4 during
the generalization test. This result extends previous fMRI findings that
have demonstrated correlations in fear expression and amygdala
activity to the conditioned stimulus (Cheng et al., 2006; Knight et al.,
2005). Activity in the insula was also correlated with generalized fear
expression. Previous fMRI studies have linked the insula to visceral
and emotional processing in general (Phan et al., 2002). Theories have
emerged proposing that the role of the insula is to integrate
physiological information concerning internal bodily states to inform
psychological awareness and decision making (Craig, 2009) which, in
the context of the presentstudy, may relateto assessingthe predictive
oraffectivevalueofeachface exemplar.It isimportantto notethatthe
behavioral metric derived to assess fear generalization captured the
Fig. 5. Learning-related and generalization-related increases in amygdala–visual cortex activity and its relation to trait anxiety. (a) Single trial connectivity analysis, using the
amygdala as a seed region, showed learning-related changes inamygdala connectivity with a face-selective region within the fusiform gyrus that wasidentified from an independent
functional localizer task. Connectivity between these regions was undifferentiated prior to fear learning, and showed a linear increase in connectivity during the generalization test.
ANOVA revealed a phase (preconditioning, generalization test) by stimulus interaction for the CS+ versus the CS−, as well as for the S4 versus the S2. (b) Increases in functional
connectivity on S4 trials, from preconditioning to the generalization test, were positively related with trait anxiety scores (r(13)=0.62, p=0.018). Increases in amygdala–FFG
connectivity were not related with anxiety scores for the CS+ (r(13)=−0.12, p=0.69).
J.E. Dunsmoor et al. / NeuroImage 55 (2011) 1878–1888
change in the proportional response to each stimulus following fear
conditioning, and did not reflect pure SCR magnitude to each stimulus
during the generalization test (c.f. Schiller and Delgado, 2010). This
distinction is critical, as this measure provides a novel way to assess
how subjects change in their psychophysiological response profile
from pre-to-post fear learning. This approach is in contrast to several
neuroimaging investigations that have shown a relationship between
the direct production of SCRs and brain activity (e.g. Critchley et al.,
2000). The present method helps to ensure that changes in behavior
are due to the intervening fear learning phase (Weinberger, 2007),
either through associative learning or non-associative sensitization
processes [see Dunsmoor et al. (2009)]. Therefore, these findings
provide a key insight into the relationship between brain activity and
behavioral responses to generalized threats following an episode of
Functional connectivity analysis revealed increased amygdala–FFG
coupling during the generalization test for the CS+ and S4. The
amygdala may serve a role in modulating activity in cortical regions to
stimuli that have acquired affective significance, for instance through
fear conditioning (Armony and Dolan, 2002). Prior research has
shown that affectively salient faces preferentially engage the
amygdala and FFG (Vuilleumier and Pourtois, 2007). In the present
study, functional connectivity between the amygdala and FFG was
undifferentiated prior to fear conditioning. The lack of differential
amygdala effects during preconditioning for high versus low value
fearful faces is in line with prior findings showing that static images of
fearful (and angry) faces do not evoke larger responses in the
et al., 2003) Following fear conditioning, amygdala–FFG connectivity
was characterized by learning-related and generalization-related
effects — connectivity was enhanced to both the CS+ and the S4
following the fear conditioning phase. These connections may
facilitate fear responses by enhancing the sensory representation of
stimuli related to a learned threat. The finding that correlations
between trait anxiety and increases in amygdala connectivity for the
S4 is informative for the way in which amygdala connectivity
contributes to stimulus processing in high anxious individuals. The
association between anxiety levels and brain activity has been
explored across a number of emotional processing tasks (Etkin and
Wager, 2007), and high anxiety levels are frequently associated with
amygdala activity evoked by negative stimuli (Bishop, 2007). The
present finding is consistent with a study reporting amygdala–FFG
connectivity in phobic patients viewing phobic stimuli (Ahs et al.,
2009). If amygdala connectivity serves to facilitate responses to
stimuli that share properties with a feared stimulus, then this
pathway might be related to overgeneralization of fears in anxiety
disorders. It is interesting that connectivity was not enhanced for the
S5 following fear conditioning, considering that the S5 contained the
greatest degree of fear expression. This may indicate that functional
connectivity is related predominately to the behavioral measures of
fear conditioning (i.e., SCRs and retrospective CS+ identification),
which show a bias in favor of the S4 above all other stimulus values.
These findings may be interpreted from a number of theoretical
perspectives. First, a gradient-interaction theory of stimulus general-
form around the CS+ and CS−, respectively. The summation of these
gradients leads to a shift in responses to a value further from the CS−,
which could explain the peak-shift in behavioral and neural responses
to the S4 and S5 but not the S2. However, gradient-interaction theory
has not received strong support from empirical studies of stimulus
generalization, as it has been repeatedly shown that inhibition and
excitation do not generalize in the same manner (Ghirlanda, 2002;
Lissek et al., 2008; Rescorla, 2006). Furthermore, our previous
behavioral findings using this design argues against gradient-interac-
tion theory as the root cause of emotion-based intensity generalization
the most intense stimulus (S5) as the CS− resulted in a sharper
gradient (i.e., greater responses to the S1 and S2 versus the S4 and S5).
inhibitory vs. excitatory gradient-interaction effects. Nonetheless, our
fMRI analysis in the present study revealed that brain regions that
respond selectively to the CS+ or CS− during the initial learning also
generalized their response accordingly, so brain regions that support
discrimination learning do make some contribution to the generaliza-
amygdala do not peak to the CS+ value used during initial learning but
instead peaks to the S4, so the amygdala's role in fear conditioning may
be underestimated by usingits response to the CS+ as the sole index of
Another interpretation comes from an elemental associative
learning model of stimulus generalization (McLaren and Mackintosh,
2002). In this view, elements that predict the US accrue associative
value while elements that do not predict the US lose associative value
over the course of conditioning (Rescorla and Wagner, 1972). During
the generalization test, similarity to the CS+ is measured by those
shared elements that have gained associative value (in this case,
features related to fear expression) while other perceptual elements
(in this case, features related to identity) are given less weight
(McLaren and Mackintosh, 2002). A peak shift can occur if a non-CS
contains more of those associative elements than the CS+ itself.
However, according to this model the S5 would have evoked more
generalization than the CS+ and S4, as it contained the most amount
of emotional intensity. Moreover, an elemental associative model
does not fully accord to our previous behavioral findings which failed
to show a reverse intensity based gradient (Dunsmoor et al., 2009).
We propose that a stimulus intensity model (Ghirlanda, 2002;
Ghirlanda and Enquist, 2003) may best explain the present results.
Intensity effects are marked by a response bias (i.e., peak shift) to
generalize learned behaviors towards novel stimuli that are some-
what more intense than the CS+ (Ghirlanda and Enquist, 2003). Thus,
an intrinsically intense non-CS may be more likely to evoke a
heightened fear response after an episode of fear learning than a
stimulus that is similar to but less intense than the CS+. Overall,
further brain imaging research will be needed to establish any model
of generalization as neurobiologically plausible.
Interestingly, we observed a co-variation bias (or “illusory correla-
subjects falsely identified a more intense face as the CS+ in a post-
(e.g., snakes and spiders) and fear-irrelevant (e.g., flowers and mush-
rooms) cues have shown that subjects often mistakenly conclude that
fear-relevant cues were paired with an aversive US at a higher rate,
when in fact both classes of stimuli were equally paired with the US
(Öhman and Mineka, 2001). This bias is more pronounced in high
anxious individuals (Tomarken et al., 1989). The present result is in
keeping with these previous findings, and suggests that even healthy
adults are biased towards remembering details of a fear learning
experience as more emotionally intense than they actually were.
Alternatively, this retrospective bias in threat estimation could indicate
that subjects were unable to discriminate between the CS+ and S4.
have shown that healthy subjects can readily discriminate between
morph values that are even more subtle than those chosen for the
present study (Graham et al., 2007; Thomas et al., 2007), and RT data
S3 and S4. Therefore we do not believe that the retrospective bias was
indicative of a perceptual confusion during learning itself but future
studies could explicitly test awareness intermittent with learning. The
relationship between generalization and discrimination has been the
example Hull, 1943; Lashley and Wade, 1946). More contemporary
J.E. Dunsmoor et al. / NeuroImage 55 (2011) 1878–1888
views on the relationship between discrimination and generalization
(e.g. Shepard, 1987) are in part informed by prevailing evidence that
between exemplars (Guttman and Kalish, 1956). That is, generalization
along a dimension often follows an orderly gradient to stimuli that are
both confusable and discriminable (Pavlov, 1927). Notably, empirical
research on stimulus generalization comes predominately from studies
of appetitive instrumental learning. Future studies that focus on
classically conditioned fear behaviors will be needed to fully address
whether thepresentfindings extent toaffectively neutralnon-intensity
A limitation of the present study is the use of a single stimulus
dimension (fear intensity), whereas generalization can occur along any
dimension. In the animal literature, the use of a single dimension is
commonly used when exploring effects of intra-dimensional discrim-
ination training (Honig and Urcuioli, 1981). It is for this reason that we
employed only a single dimension in the present study, thus ensuring
that intra-dimensional changes in behavioral and neural responses
factors related to identity. Our prior fMRI work using these face morphs
has further shown that dynamic changes in identity and emotional
expression are partly dissociable in the brain (LaBar et al., 2003). Thus,
additional research is warranted to determine how generalization is
mediated along other featural dimensions.
In conclusion, the neural and behavioral systems involved in
processing and reacting to feared stimuli are becoming increasingly
welldelineated acrossspecies,drivenin largepartby a desireto better
inform models of clinical anxiety disorders. The laboratory study of
fear learning has typically involved a systematic examination of the
processes involvedin acquiring,expressing,andextinguishing fearsto
a specific stimulus. To understand anxiety disorders marked by
heightened fear responses, however, it is necessary to explore the
processes involved in the generalization of fear to a wider range of
stimuli, especially given that a feared stimulus can be encountered in
multiple forms (Shepard, 1987). Results from the present investiga-
tion demonstrate that regions involved in fear learning are not always
specific to a learned threat. Moreover, individual differences in
intensity-based generalization suggest a dynamic interplay between
corticolimbic–autonomic coupling during fear generalization, and
underscores the importance for interpreting brain activity by
behavioral measures. Lastly, analysis of functional connectivity
between the amygdala and FFG suggest that the amygdala may be
important for modulating the sensory representation of stimuli that
approximate a learned threat, and that heightened amygdala
connectivity may be associated with overgeneralization of fears for
individuals with heightened anxiety. Collectively, these results
provide novel methodological approaches and insights into the neural
basis of fear learning and generalization.
We thank Matthew Fecteau for assistance with the psychophys-
iological equipment. This project was supported by NSF grant
0745919 and NIH grant 2 P01 NS041328.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
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