Discussion
In this study we explored whether threat uncertainty, expressed in
different reinforcement schedules between CS+ and US, could lead to
wider fear generalization. A second aim was to see whether fear
generalization would be differentially expressed in various systems
involved such as showing lateral inhibition in the visual cortex but
linear generalization in autonomic arousal and subjective ratings. In
contrast to our expectations threat uncertainty did not lead to
overgeneralization of the threat responses in any of the measured
variables. These findings are partly in agreement with Zhao et al.
(2022), who found no influence of the reinforcement rate on autonomic
arousal in generalization but, contrary to our findings, found increased
threat expectancy ratings for the partial reinforcement groups. However,
in both studies threat uncertainty was not associated with wider
generalization gradients.
One reason for the absence of differential generalization gradients
between groups in our study could be successful acquisition of
conditioned fear in all groups. In line with previous studies,
participants found CS+ more arousing, unpleasant, more likely to be
followed by the US, and more physiologically arousing compared to CS-
(Ahrens et al., 2016; Dunsmoor et al., 2017; Herzog et al., 2021;
Lemmens et al., 2021; McClay et al., 2020; Stegmann et al., 2020).
Impaired discriminative fear learning is often found in people with
anxiety and stressor-related disorders (Cha et al., 2014; Greenberg et
al., 2013; Huggins et al., 2021; Lissek et al., 2009, 2010, 2014; Milad
et al., 2007) and is hypothesized to carry over into the generalization
phase leading to less steep (i.e., more linear) generalization
gradients. The clear discrimination between threat and safety cues in
our study could have minimized the manifestation of overgeneralization
despite the fact that threat uncertainty differed across groups (Lenaert
et al., 2014). Another reason could be that threat uncertainty expressed
in different reinforcement schedules is not strong enough to lead to
overgeneralization. Despite the fact that the uncertainty manipulation
was reflected in threat expectations and autonomic arousal during
acquisition, it did not modulate the affective ratings. It is therefore
conceivable that threat uncertainty as a result of reinforcement rate is
not strong enough to cause overgeneralization, but a combination of high
uncertainty and arousal of threat could have a stronger impact instead.
For example, by manipulating threat uncertainty but also the arousal of
the US (e.g., by using pictures from the international affective picture
system that differ in how arousing they are).
Although fear generalization was not modulated by the manipulation of
threat uncertainty, we found that higher trait intolerance of
uncertainty was associated with wider generalization in threat
expectancy ratings. The impact of intolerance of uncertainty in fear
generalization is still somewhat unclear. Results from studies so far
point to less discrimination of SCR responses to the CSs and GSs in
acquisition for people with high intolerance of uncertainty, however
this finding is inconsistent (Bauer et al., 2020; Morriss et al., 2016;
Nelson et al., 2015) and so far there was no correlation with fear
generalization (Mertens et al., 2021). In the current study, we found a
moderate correlation with threat expectancy ratings. A difference with
the previous studies described (Bauer et al., 2020; Morriss et al.,
2016; Nelson et al., 2015) is that the acquisition phase in the current
study did not include any GSs and thus in the generalization phase
participants saw these stimuli for the first time. From studies so far
including the current study it is clear that partial reinforcement
induces uncertainty and it is a good method to demonstrate the role that
intolerance of uncertainty plays in fear generalization since all these
studies use partial reinforcement but no influence has been found with
typical reinforcement schedules (75%; Mertens et al., 2021).
Additionally, since the US-expectancy ratings in this study were
retrospective, we measured the overall subjective feeling of threat
expectancy participants had at the end of the experiment. Our findings
show that partial reinforcement can influence the generalized responses
of a subset of participants scoring high in intolerance of uncertainty
and therefore the reinforcement schedule should be carefully considered
in fear generalization studies. Since this analysis was of exploratory
nature, it should be considered with caution and further research would
be needed to clarify the role of trait intolerance of uncertainty on the
different facets of fear generalization.
Our second aim was to examine whether fear generalization would show
different responses in the various systems involved. No such differences
were observed in the generalization phase; however, our results
demonstrate different mechanisms involved in fear learning between
threat expectancy, autonomic arousal, and affective ratings. More
specifically, although participants expected less threat in the high
uncertainty group, they displayed higher autonomic arousal compared to
the low uncertainty group. This finding adds to existing literature
demonstrating higher SCR with higher uncertainty (de Berker et al.,
2016; Tzovara et al., 2018) as well as unpredictability of threat
(Alvarez et al., 2015; Dretsch et al., 2016). However, not all studies
found modulation of SCR by uncertainty induced by the reinforcement rate
(Zhao et al., 2022). This difference could be because in the study by
Zhao et al. participants might not have been aware of the reinforcement
as the three groups did not display significant differences in threat
expectancy either. However, uncertainty about future events and threats
increases the affective reactions to these events (Bar-Anan et al.,
2009; Grillon et al., 2004, 2008). Therefore, our results suggest that
increased uncertainty is linked to increased autonomic arousal despite
low probability of threat and could therefore reflect the effort to
successfully predict the threat.
It is worth mentioning that threat uncertainty in our study was not
enough to differentiate the groups in the affective ratings. On the one
hand, one would expect that low threat expectancy will not cause very
unpleasant and arousing feelings. However, our findings show that the
CS+ was equally unpleasant and arousing regardless of low expectation of
the threat. This pattern resembles the difficulty people with clinical
anxiety have suppressing their defensive reactions despite concrete
knowledge that these reactions are exaggerated. On the other hand,
expectancy and affective learning are thought to represent distinct
learning processes that can take place during classical conditioning
(Hamm & Vaitl, 1996; Hamm & Weike, 2005; Hermans et al., 2002;
Lonsdorf et al., 2017). Expectancy-learning refers to the association
that the CS activates the expectation of the US in the immediate future,
and it is associated with measures that relate to conscious awareness
such as SCR and US-expectancy (e.g., (Biferno & Dawson, 1977; Dawson &
Biferno, 1973; Ross & Nelson, 1973). Affective learning refers to the
process by which CS presentation activates the representation of the US
and its positive/negative valence without activating its expectation.
Additionally, while expectancy learning seems to be related to more
conscious defensive processes such as SCR, affective learning is related
to more unconscious processes such as fear-potentiated startle responses
(Bradley & Lang, 1994; Hamm & Vaitl, 1996; Hamm & Weike, 2005). Our
findings are in line with this distinction between affective and
cognitive learning mechanisms as our manipulation mainly focused on the
expectancy and not necessarily on the valence or arousal of threat. In
turn participants’ threat expectations and autonomic arousal were
affected by threat uncertainty while valence and arousal perceptions
remained unaffected.
Contrary to our expectations and previous literature (Keil et al., 2013;
McTeague et al., 2015; Miskovic & Keil, 2013; Petro et al., 2017;
Stegmann et al., 2020), we found no differential responding in the
visual cortex, neither in the acquisition nor in the generalization
phase. A closer look in the literature revealed several factors that
could explain the absence of discriminatory visuocortical responding.
First, the majority of the previous studies (Gruss & Keil, 2019;
McTeague et al., 2015; Miskovic & Keil, 2013; Moratti & Keil, 2005)
used basic perceptual CSs such as Gabor gratings of different
orientations. Such simple stimuli can directly engage orientation
sensitive cells in the visual cortex (Hubel & Wiesel, 1962) and
therefore, the differential processing of CS+ related orientations
compared to the ones related to CS- is easier to detect with EEG.
However, such differential engagement can be difficult to detect using
complex stimuli such as faces which include multiple features. In
complex stimuli, threat related features could still be selectively
enhanced, but this difference is more difficult to be detected because
the stimuli might share more similarities than differences (McTeague et
al., 2015). Another reason can be the viewing distance of the stimuli.
In previous studies using ssVEPs (Gruss & Keil, 2019) and complex
stimuli such as the ones used in the current study (Kastner-Dorn et al.,
2018; Stegmann et al., 2020; Wieser et al., 2014) participants were
sitting 100 cm away from the screen while in our study they were sitting
150 cm away. Stimuli presented with greater perceived distance have
smaller angular size and smaller cortical representation (Murray et al.,
2006). Thus, the combination of complex stimuli such as faces, and the
longer distance of the stimuli might have influenced the visuocortical
engagement and made the differences too small to detect. Furthermore, a
closer review of the literature revealed that the differential CS
cortical engagement is not consistently reported with ssVEPs (Friedl &
Keil, 2020) and often depends on other individual characteristics such
as genotype (Gruss et al., 2016) and heart rate (Moratti et al., 2006;
Moratti & Keil, 2005) which were not included in this study. The
inconsistent results warrant the need for a systematic review of the
available studies to determine the consistency and size of the effect.
This study has several strengths and some limitations. First, the
examination of psychophysiological, cognitive, and affective measures
allows us to follow fear generalization from the very first moments of
threat perception and track how it is manifested in the brain, body,
cognitive and affective processes. Although we could not observe
discriminatory responses in visuocortical responding, further
exploration is needed to examine the size of the effect and how it can
be better studied or explore other methods that could capture early
stages of fear generalization in the brain such as the late positive
potential (LPP; Nelson et al., 2015). Second, in contrast to Zhao et al.
(2022) where the generalization’s reinforcement rate was identical to
acquisition for one of the groups, we kept the reinforcement rate of the
generalization phase at 20% which was lower than the acquisition phase
but comparable across the groups. Regarding the limitations, the
duration of the experiment was fairly long, which could have influenced
the SCR. Since no instructions were given to the participants about the
reinforcement schedule, we needed to ensure that enough learning trials
would be available. This resulted in a duration of 45 mins which could
have induced a strong habituation of the psychophysiological responses
during the generalization phase (Codispoti et al., 2006; Peeke, 2012)
and could have constituted potential differences between the stimuli too
small to detect. Second, in the generalization phase all groups had the
same reinforcement rate of 20% to ensure that the test phase for the
generalization processes was comparable across groups. However, this
resulted in an asymmetrical decrease in reinforcement from acquisition
to generalization across groups. More specifically, the CS-US
contingency in LU was reduced by 75%, in MU by 66% whereas in the HU
group by 50%. The asymmetrical decrease from acquisition to
generalization could have led to earlier extinction in LU and an
artificial difference between the groups. However, this did not
constitute a problem in our study as no differences were observed among
the groups. Finally, we did not ask our participants how “uncertain”
they felt while seeing the visual stimuli during the experiment.
Uncertainty can be seen both as an external and an internal condition
(Grupe & Nitschke, 2013). We explicitly manipulated external
uncertainty, but a subjective (or internal) uncertainty could have
additionally influenced participants’ responses and may be especially
interesting for anxiety psychopathology.
To conclude, our study successfully replicated fear acquisition and fear
generalization on both verbal and physiological responses. Participants
clearly distinguished between threat and safety signals and generalized
their fear only to those stimuli similar to the threat signal. The
reinforcement schedule and therefore the uncertainty of the threat did
not influence the generalization gradient of the three learning groups,
but higher intolerance of uncertainty was associated with wider
expectancy of threat in generalization. Interestingly, we found
different responses in the subjective ratings by the uncertainty
reflected in the reinforcement rate as this was observed in
participants’ US-expectancy ratings, but not in the valence and arousal
ratings. Finally, our results support the notion that lower
predictability and therefore higher uncertainty of threat leads to
increased autonomic arousal.