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 intense emotional face tended to express fear to stimuli that resembled a 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 involved in the acquisition of differential fear learning also showed responses to generalized stimuli as a function of emotional intensity during the generalization test. Second, activity in the amygdala and insula correlated with individual variability in physiological arousal 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 test to a non-conditioned stimulus of high intensity, and this increase in 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 fear as well, and indicate that the neural substrates of fear generalization 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 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 (Milad et al., 2007
; Phelps et al., 2004
), and may mediate extinction 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 other stimuli.
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 present study, may relate to assessing the predictive or affective value of each face exemplar. It is important to note that the behavioral metric derived to assess fear generalization captured the 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 fear learning.
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 amygdala than emotionally neutral faces (Fitzgerald et al., 2006
; LaBar 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 generalization (Spence, 1937
) argues that gradients of excitation and inhibition 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-interaction theory as the root cause of emotion-based intensity generalization (Dunsmoor et al., 2009
). In this study, discriminatory fear learning using the most intense stimulus (S5) as the CS− resulted in a sharper generalization gradient around the CS+ (S3), but did not cause a reverse gradient (i.e., greater responses to the S1 and S2 versus the S4 and S5). We also note that the BOLD signal in fMRI does not permit a direct test of 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 generalization gradient. Interestingly, the results show that effects reported for the 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 using its response to the CS+ as the sole index of learning.
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 somewhat 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 correlation”) along an emotional intensity dimension, such that the majority of subjects falsely identified a more intense face as the CS+ in a post-experimental test of awareness. Previous paradigms using fear-relevant (e.g., snakes and spiders) and fear-irrelevant (e.g., flowers and mushrooms) 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. However, our prior psychometric studies using these neutral-to-morphs 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 from the present study reveal a clear difference in the time to classify the 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 matter of historical debate in the conditioned learning literature (see for example Hull, 1943
; Lashley and Wade, 1946
). More contemporary views on the relationship between discrimination and generalization (e.g. Shepard, 1987
) are in part informed by prevailing evidence that generalization can occur despite the capacity to recognize the difference 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 the role of discriminatory processes in aversive learning, and to examine whether the present findings extent to affectively neutral non-intensity dimensions.
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 discrimination 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 could be attributed to the degree of emotional expression, and not other 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 well delineated across species, driven in large part by a desire to better inform models of clinical anxiety disorders. The laboratory study of fear learning has typically involved a systematic examination of the processes involved in acquiring, expressing, and extinguishing fears to 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 investigation 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.